Internet polls are regularly underestimating support for Hillary Clinton

Since the Republican and Democratic conventions in July, Hillary Clinton has experienced a poll ‘bounce’, to lead Donald Trump by about 8 percent, especially in telephone polls with live interviewers. Internet polls, by contrast, tend to show Clinton leading by only 2 or 3 percent. Are these internet polls underestimating Clinton or overestimating Trump? Using results from the 2016 presidential primaries to assess state polls’ accuracy, Taylor Howell, Christopher Stout and Reuben Kline find that that internet polls were slightly more likely to overestimate support for Trump than live interviewer polls, and that they were likely to underestimate support for Clinton by nearly 2 percent. They suggest that online polls may still have some ways to come in terms of accuracy and may be skewing away from Clinton by offering a “don’t know” option to those who are as yet unwilling to commit to voting for her

Shall we dance? Welfarist incorporation and the politics of state-labour NGO relations in China

State-labour NGOs relations in China have been particularly fraught. In 2012 these took an interesting twist, as some local governments made overtures to labour NGOs to co-operate in providing services to migrant workers. This article argues that this shift is part of a broader strategy of `welfarist incorporation’ to redraw the social contract between state and labour. There are two key elements to this: first, relaxation of the registration regulations for social organisations; second, governmental purchasing of services from social organisations. These overtures have both a state and market logic to maintain social control and stabilise relations of production

Direct Policy Optimization using Deterministic Sampling and Collocation

We present an approach for approximately solving discrete-time stochastic optimal-control problems by combining direct trajectory optimization, deterministic sampling, and policy optimization. Our feedback motion-planning algorithm uses a quasi-Newton method to simultaneously optimize a reference trajectory, a set of deterministically chosen sample trajectories, and a parameterized policy. We demonstrate that this approach exactly recovers LQR policies in the case of linear dynamics, quadratic objective, and Gaussian disturbances. We also demonstrate the algorithm on several nonlinear, underactuated robotic systems to highlight its performance and ability to handle control limits, safely avoid obstacles, and generate robust plans in the presence of unmodeled dynamics.Comment: revisions for RA-L 202

Scalable Methods for Monitoring Limited Access Roadways using Crowd-Sourced Probe Data

Commercial crowd-sourced probe vehicle data has been gaining traction in recent years as a ubiquitous and scalable resource for identifying traffic congestion on limited access roadways. It is routinely used in real-time by navigation software that displays color coded maps. However, outside of public agency traffic management centers, there are no factual “big picture” reports on traffic conditions. The media tries to fill this gap, but they either provide descriptions of construction locations, or highly subjective opinions. This paper proposes and illustrates a “big picture” characterization of regional and national traffic conditions using archived and real-time data. Average speeds of vehicles on segments of roadway can be retrieved in near real-time at one-minute intervals to produce performance metrics that measure cumulative miles of congestion per route, per entire Metropolitan Statistical Area (MSA), and on coast-to-coast Interstates using speed profile analysis. Moreover, both real-time and historic archival performance measures can be used for after-action analysis of major traffic events. In this study, the traffic congestion for four MSAs and two Interstates during the week of June 28 to July 6 was used as a case study to illustrate the concepts. The study found most congestion in the Chicago, Los Angeles, and New York City metropolitan areas occurred during the PM rush on July 2 before the holiday weekend, with at least 20% of all limited access roadways in each area falling below 40 mph between the hours of 4:30 PM and 5:45 PM local time. On a coast-to-coast level, Interstate 80 showed the heaviest congestion eastbound at 5:15 PM EDT with 140 combined miles of congestion across 11 states. Data reduction and aggregation methods using 15-minute medians outlined in this study allow future systems to implement regional congestion graphs, speed profile charts, and temporal congestion graphs for operational and practical uses. This information can be leveraged by local, regional, and state transportation agencies as well as for media dissemination and outreach to inform the public

Book Reviews

Reviews of the following books: A History of the Maine Agricultural Experiment Station, 1885-1978 by David C. Smith; Radical Sects of Revolutionary New England by Stephen A. Marini; English America and the Revolution of 1688: Royal Administration and the Structure of Provincial Government by Jack M. Sosin

Plasmids Bring Novel Functions to \u3ci\u3eCaulobacter\u3c/i\u3e Genomes

Caulobacter is a well-studied bacterial genus, but little is known about the plasmids that are found in some wild Caulobacter isolates. I identified nine plasmids from seven different Caulobacter strains and grouped them based on their size and the similarity of their repABC, parAB, and mobAB genes. Protein pathway analysis of the genes on the K31p1, and K31p2 plasmids showed many metabolic pathways that would enhance the metabolic versatility of the host strain. In contrast, the CB4 plasmid contained 21 heavy metal resistance genes organized into 9 operons. The majority of the CB4 heavy metal resistance genes code for genes that enhance copper resistance. Growth assays demonstrated increased copper resistance and quantitative PCR showed an increase in the expression of eight of the nine heavy metal operons when induced with copper

Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo

We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to easily author and solve complex robotics tasks, and currently supports three shooting-based planners: derivative-based iLQG and Gradient Descent, and a simple derivative-free method we call Predictive Sampling. Predictive Sampling was designed as an elementary baseline, mostly for its pedagogical value, but turned out to be surprisingly competitive with the more established algorithms. This work does not present algorithmic advances, and instead, prioritises performant algorithms, simple code, and accessibility of model-based methods via intuitive and interactive software. MJPC is available at: github.com/deepmind/mujoco_mpc, a video summary can be viewed at: dpmd.ai/mjpc.Comment: Minor fixes and formattin

Functional network changes and cognitive control in schizophrenia

Cognitive control is a cognitive and neural mechanism that contributes to managing the complex demands of day-to-day life. Studies have suggested that functional impairments in cognitive control associated brain circuitry contribute to a broad range of higher cognitive deficits in schizophrenia. To examine this issue, we assessed functional connectivity networks in healthy adults and individuals with schizophrenia performing tasks from two distinct cognitive domains that varied in demands for cognitive control, the RiSE episodic memory task and DPX goal maintenance task. We characterized general and cognitive control-specific effects of schizophrenia on functional connectivity within an expanded frontal parietal network (FPN) and quantified network topology properties using graph analysis. Using the network based statistic (NBS), we observed greater network functional connectivity in cognitive control demanding conditions during both tasks in both groups in the FPN, and demonstrated cognitive control FPN specificity against a task independent auditory network. NBS analyses also revealed widespread connectivity deficits in schizophrenia patients across all tasks. Furthermore, quantitative changes in network topology associated with diagnostic status and task demand were observed. The present findings, in an analysis that was limited to correct trials only, ensuring that subjects are on task, provide critical insights into network connections crucial for cognitive control and the manner in which brain networks reorganize to support such control. Impairments in this mechanism are present in schizophrenia and these results highlight how cognitive control deficits contribute to the pathophysiology of this illness