Bureaucratic representation and gender mainstreaming in international organizations: evidence from the World Bank

How does the representation of women in international organizations affect the implementation of gender mainstreaming policies? Many international organizations have adopted policies to prevent gender discrimination in their operations, but their implementation is often lackluster. We argue that these shortcomings appear due to a combination of institutional incentives and an underrepresentation of women in their staff. We test the argument in the case of the World Bank, drawing on highly disaggregated staffing data, an instrumental variable strategy, and an elite survey experiment. Our results show that most staff incorporate at least shallow gender mainstreaming in their projects. Deeper implementation of gender mainstreaming is more likely when women staff supervise projects, hold positions of authority, and are more represented as coworkers. These results contribute to understanding the disconnects between talk and action on mainstreaming policies and inform debates on representation in global governance

Data-Driven Methods for Real-Time Control in Autonomous Racing Games

Offline datasets and demonstrations can provide valuable guidance for planning and control in robotic and intelligent systems. Often, demonstration data consists of sequences of observations and control actions that occurred in the environment, demonstrating a specific task or desirable behavior. They can originate from human experts or demonstrators, simple rule-based or model-based controllers, or even previously machine-learned policies. However, this data is most commonly unstructured, without annotations of underlying tasks, rewards, thoughts, rules, or states that were used by the demonstration generator. For long-horizon, complicated tasks, these unstructured demonstrations often fail to provide enough information to facilitate learning. Thus, additional structure in sequential demonstrations could inform or enhance learning-based planning and control.This dissertation explores how meaningful partitions of sequential data can guide and accelerate learning, and improve the performance and reliability of control systems. In particular, we primarily focus on the complicated task of planning and control for an autonomous racing vehicle, which must rapidly traverse a closed racetrack with complicated, unknown vehicle dynamics. We identify three challenges that exist in current algorithms for autonomous racing systems. First, the control system should be robust to state deviations and maintain control when it encounters previously unseen states. Second, training the control system should efficiently leverage its knowledge to minimize the number of environment interactions necessary to train a high-performance system. Third, the control system should emulate human behavior to race in predictable and understandable ways. In this dissertation, we explore how offline datasets can address these challenges while maintaining good racing performance through both modular planning and control and end-to-end learning.In the first part, we explore control systems that are built on separate planning and control modules. In particular, we design a trajectory generation module that can leverage an existing demonstration for fast, online trajectory planning. In Chapter 2, we propose a novel acceleration motion primitive for online trajectory generation that can plan trajectories that are better suited for the complex racing environment than existing methods. Analysis of the trajectories reveals that our method generates trajectories reduce the error between generated trajectories and the offline demonstration and lead to less aggressive acceleration and jerk than existing velocity motion primitives. In Chapter 3, we learn a sequence of motion primitives from a reference trajectory, and use the primitive sequence to generate racing trajectories in real-time. When used with Model Predictive Control (MPC), our proposed acceleration motion primitive and learning-based trajectory generation algorithm allows the MPC to recover from deviations from the reference while maintaining racing speeds. In the second part, we explore how offline data can improve end-to-end control policy learning. In Chapter 4, we tackle the problem of learning a control policy from long horizon sparse rewards by adding additional hierarchical structure from offline data. Our Skill-Critic method for online fine-tuning of the hierarchical policies reduces the requirements for online interactions with the environment and results in the fastest racer. In Chapter 5, rather than learning from a designed reward function, we tackle the problem of imitating human driver demonstrations. We propose a method that leverages offline sequence modeling architectures and online fine-tuning to imitate human racers. Our proposed method is more sample efficient. The resulting policies are more stable and are able to achieve the fastest lap-times compared to existing methods

A Novel Codon-optimized SIV Gag-pol Immunogen for Genebased Vaccination

Simian immunodeficiency virus (SIV) is a robust pathogen used in non-human primates to model HIV vaccines. SIV encodes a number of potential vaccine targets. By far the largest and most conserved protein target in SIV is its gag-pol protein that bears many epitopes to drive multivalent immune T cell responses. While gag-pol is an attractive antigen, it is only translated after a frame shift between gag and pol with the effect that gag and pol are expressed at an approximate 10/1 ratio. The codon bias of native lentiviral genes are also mismatched with the abundance of tRNAs in mammalian cells resulting in poor expression of unmodified SIV genes. To provide a better SIV gag-pol immunogen for gene-based vaccination, we codon-optimized the full gag-pol sequence from SIVmac239. To increase pol expression, we artificially moved the pol sequence in frame to gag to bypass the need for a translational frame shift for its expression. Finally, we inserted four self-cleaving picornavirus sequences into gag p24, protease, reverse transcriptase, and into integrase to fragment the proteins for potentially better immune presentation. We demonstrate that these immunogens are well expressed in vitro and drive similar antibody and T cell responses with or without cleavage sequences

A Novel Codon-optimized SIV Gag-pol Immunogen for Genebased Vaccination

Simian immunodeficiency virus (SIV) is a robust pathogen used in non-human primates to model HIV vaccines. SIV encodes a number of potential vaccine targets. By far the largest and most conserved protein target in SIV is its gag-pol protein that bears many epitopes to drive multivalent immune T cell responses. While gag-pol is an attractive antigen, it is only translated after a frame shift between gag and pol with the effect that gag and pol are expressed at an approximate 10/1 ratio. The codon bias of native lentiviral genes are also mismatched with the abundance of tRNAs in mammalian cells resulting in poor expression of unmodified SIV genes. To provide a better SIV gag-pol immunogen for gene-based vaccination, we codon-optimized the full gag-pol sequence from SIVmac239. To increase pol expression, we artificially moved the pol sequence in frame to gag to bypass the need for a translational frame shift for its expression. Finally, we inserted four self-cleaving picornavirus sequences into gag p24, protease, reverse transcriptase, and into integrase to fragment the proteins for potentially better immune presentation. We demonstrate that these immunogens are well expressed in vitro and drive similar antibody and T cell responses with or without cleavage sequences

A Novel Codon-optimized SIV Gag-pol Immunogen for Genebased Vaccination

Simian immunodeficiency virus (SIV) is a robust pathogen used in non-human primates to model HIV vaccines. SIV encodes a number of potential vaccine targets. By far the largest and most conserved protein target in SIV is its gag-pol protein that bears many epitopes to drive multivalent immune T cell responses. While gag-pol is an attractive antigen, it is only translated after a frame shift between gag and pol with the effect that gag and pol are expressed at an approximate 10/1 ratio. The codon bias of native lentiviral genes are also mismatched with the abundance of tRNAs in mammalian cells resulting in poor expression of unmodified SIV genes. To provide a better SIV gag-pol immunogen for gene-based vaccination, we codon-optimized the full gag-pol sequence from SIVmac239. To increase pol expression, we artificially moved the pol sequence in frame to gag to bypass the need for a translational frame shift for its expression. Finally, we inserted four self-cleaving picornavirus sequences into gag p24, protease, reverse transcriptase, and into integrase to fragment the proteins for potentially better immune presentation. We demonstrate that these immunogens are well expressed in vitro and drive similar antibody and T cell responses with or without cleavage sequences

Outracing Human Racers with Model-based Planning and Control for Time-trial Racing

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this work, we investigate how to approach human expert-level racing performance with model-based planning and control methods using the high-fidelity racing simulator Gran Turismo Sport (GTS). GTS enables a unique opportunity for autonomous racing research, as many recordings of racing from highly skilled human players can served as expert emonstrations. By comparing the performance of the autonomous racing software with human experts, we better understand the performance gap of existing software and explore new methodologies in a principled manner. In particular, we focus on the commonly adopted model-based racing framework, consisting of an offline trajectory planner and an online Model Predictive Control-based (MPC) tracking controller. We thoroughly investigate the design challenges from three perspective, namely vehicle model, planning algorithm, and controller design, and propose novel solutions to improve the baseline approach toward human expert-level performance. We showed that the proposed control framework can achieve top 0.95% lap time among human-expert players in GTS. Furthermore, we conducted comprehensive ablation studies to validate the necessity of proposed modules, and pointed out potential future directions to reach human-best performance.Comment: 16 pages, 13 figures, 3 table

Leap Forward: Advancing LEAP's Land Use Goals

http://deepblue.lib.umich.edu/bitstream/2027.42/110959/1/leap_forwardreduced.pd

Contemporary Family Law, 6th Edition

Jessica Dixon Weaver: https://orcid.org/0000-0002-6960-1459https://scholar.smu.edu/facbooks/1071/thumbnail.jp

Photometry of Kuiper belt object (486958) Arrokoth from New Horizons LORRI

On January 1st 2019, the New Horizons spacecraft flew by the classical Kuiper belt object (486958) Arrokoth (provisionally designated 2014 MU69), possibly the most primitive object ever explored by a spacecraft. The I/F of Arrokoth is analyzed and fit with a photometric function that is a linear combination of the Lommel-Seeliger (lunar) and Lambert photometric functions. Arrokoth has a geometric albedo of p_v = 0.21_(−0.04)^(+0.05) at a wavelength of 550 nm and ≈0.24 at 610 nm. Arrokoth's geometric albedo is greater than the median but consistent with a distribution of cold classical Kuiper belt objects whose geometric albedos were determined by fitting a thermal model to radiometric observations. Thus, Arrokoth's geometric albedo adds to the orbital and spectral evidence that it is a cold classical Kuiper belt object. Maps of the normal reflectance and hemispherical albedo of Arrokoth are presented. The normal reflectance of Arrokoth's surface varies with location, ranging from ≈0.10–0.40 at 610 nm with an approximately Gaussian distribution. Both Arrokoth's extrema dark and extrema bright surfaces are correlated to topographic depressions. Arrokoth has a bilobate shape and the two lobes have similar normal reflectance distributions: both are approximately Gaussian, peak at ≈0.25 at 610 nm, and range from ≈0.10–0.40, which is consistent with co-formation and co-evolution of the two lobes. The hemispherical albedo of Arrokoth varies substantially with both incidence angle and location, the average hemispherical albedo at 610 nm is 0.063 ± 0.015. The Bond albedo of Arrokoth at 610 nm is 0.062 ± 0.015