Polyploidy breaks speciation barriers in Australian burrowing frogs Neobatrachus

Polyploidy has played an important role in evolution across the tree of life but it is still unclear how polyploid lineages may persist after their initial formation. While both common and well-studied in plants, polyploidy is rare in animals and generally less understood. The Australian burrowing frog genus Neobatrachus is comprised of six diploid and three polyploid species and offers a powerful animal polyploid model system. We generated exome-capture sequence data from 87 individuals representing all nine species of Neobatrachus to investigate species-level relationships, the origin and inheritance mode of polyploid species, and the population genomic effects of polyploidy on genus-wide demography. We describe rapid speciation of diploid Neobatrachus species and show that the three independently originated polyploid species have tetrasomic or mixed inheritance. We document higher genetic diversity in tetraploids, resulting from widespread gene flow between the tetraploids, asymmetric inter-ploidy gene flow directed from sympatric diploids to tetraploids, and isolation of diploid species from each other. We also constructed models of ecologically suitable areas for each species to investigate the impact of climate on differing ploidy levels. These models suggest substantial change in suitable areas compared to past climate, which correspond to population genomic estimates of demographic histories. We propose that Neobatrachus diploids may be suffering the early genomic impacts of climate-induced habitat loss, while tetraploids appear to be avoiding this fate, possibly due to widespread gene flow. Finally, we demonstrate that Neobatrachus is an attractive model to study the effects of ploidy on the evolution of adaptation in animals

Visual Saliency Estimation and Its Applications

The human visual system can automatically emphasize some parts of the image and ignore the other parts when seeing an image or a scene. Visual Saliency Estimation (VSE) aims to imitate this functionality of the human visual system to estimate the degree of human attention attracted by different image regions and locate the salient object. The study of VSE will help us explore the way human visual systems extract objects from an image. It has wide applications, such as robot navigation, video surveillance, object tracking, self-driving, etc. The current VSE approaches on natural images models generic visual stimuli based on lower-level image features, e.g., locations, local/global contrast, and feature correlation. However, existing models still suffered from some drawbacks. First, these methods fail in the cases when the objects are near the image borders. Second, due to imperfect model assumptions, many methods cannot achieve good results when the images have complicated backgrounds. In this work, I focuses on solving these challenges on the natural images by proposing a new framework with more robust task-related priors, and I apply the framework to low-quality biomedical images. The new framework formulates VSE on natural images as a quadratic program (QP) problem. It proposes an adaptive center-based bias hypothesis to replace the most common image center-based center-bias, which is much more robust even when the objects are far away from the image center. Second, it models a new smoothness term to force similar color having similar saliency statistics, which is more robust than that based on region dissimilarity when the image has a complicated background or low contrast. The new approach achieves the best performance among 11 latest methods on three public datasets. Three approaches based on the framework by integrating both high-level domain-knowledge and robust low-level saliency assumptions are utilized to imitate the radiologists\u27 attention to detect breast tumors from breast ultrasound images

Searching for the Best Neighborhood: Mobility and Social Interactions

The paper seeks to contribute to the social interactions literature by exploiting data on individual's self- selection into neighborhoods. We study a model in which households search for the best location in the presence of neighborhood effects in the formation of children's human capital and in the process of cultural transmission. We use micro data from the PSID which we have merged, using geocodes, with contextual information at the leves of census tracts and of counties from the 2000 US Census. We control for numerous individual characteristics and neighborhood attributes and find, consistently with neighborhood effects models, that households with children, but not those without, are more likely to move out of neighborhoods whose attributes are not favorable to the productin of human capital and the transmission of parents' cultural traits, and to move into neighborhoods which instead exhibit desireable such attributes.

Future of Household Waste A Case Study of Influences on Follo Ren IKS and Miljøbilen’s Viability as a Mainstream Service

Massive amounts of waste are generated globally, requiring improved sorting and collection methods to reduce waste and increase recycling and reuse. Global efforts are being initiated to address this increasing waste and its subsequent consequences. Norway is a country that contributes to these efforts and has implemented strong national policies to handle its own waste. In Norway, each municipality is responsible for managing household waste, with most of them coming together to establish an intercommunal company (IKS) to manage this work. This study focuses on the implementation of a waste collection service called Miljøbilen by Follo Ren, a waste management company responsible for waste management in Frogn, Nesodden, Nordre Follo and Ås. The aim of Miljøbilen is to reduce the number of visitors to the recycling centres, decreasing emissions from cars, and provide a beneficial service for individuals who cannot access these centres due not owning a car, have limited waste to dispose of, or have reduced physical capabilities. The research conducted an EGS (Environmental Governance Systems) Framework analysis. This analysis identified key factors influencing the implementation of a waste collection service, including the company and its policies, the services provided to households, and the potential outcomes in terms of financial, environmental, and social results. A discussion on the advantages and disadvantages of Miljøbilen revealed that while there are financial costs associated with running the service, the overall financial, environmental, and social benefits make it socio-economically profitable. A survey was conducted as part of the evaluation of Miljøbilen, indicating that users of the service are generally satisfied. For non-users, the survey responses suggest that providing more information about the service can potentially increase its usage. The implementation of Miljøbilen supports the need for individually tailored and streamlined waste management services due to an increasing focus on reducing household car usage. Miljøbilen offers one perspective on the effectiveness of such services

Searching for the Best Neighborhood: Mobility and Social Interactions

The paper seeks to contribute to the social interactions literature by exploiting data on individuals’ self-selection into neighborhoods. We study a model in which households search for the best location in the presence of neighborhood effects in the formation of children’s human capital and in the process of cultural transmission. We use micro data from the PSID which we have merged, using geocodes, with contextual information at the levels of census tracts and of counties from the 2000 US Census. We control for numerous individual characteristics and neighborhood attributes and find, consistently with neighbourhood effects models, that households with children, but not those without, are more likely to move out of neighborhoods whose attributes are not favorable to the production of human capital and the transmission of parents’ cultural traits, and to move into neighborhoods which instead exhibit desirable such attributes.

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Reinforcement Learning and Planning for Preference Balancing Tasks

Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action spaces with unknown system dynamics. We show that PEARL learns a sub-optimal policy on a subset of states and actions, and transfers the policy to the expanded domain to produce a more refined plan on a class of robotic problems. We establish convergence to task goal conditions, and even when preconditions are not verifiable, show that this is a valuable method to use before other more expensive approaches. Evaluation is done on several robotic problems, such as Aerial Cargo Delivery, Multi-Agent Pursuit, Rendezvous, and Inverted Flying Pendulum both in simulation and experimentally. Additionally, PEARL is leveraged outside of robotics as an array sorting agent. The results demonstrate high accuracy and fast learning times on a large set of practical applications