The Public Face: Working the Front Desk of the UWC

A student’s first impression of the writing center is typically made at a reception desk. In the Undergraduate Writing Center (UWC) at the University of Texas at Austin, the front desk generally employs two staffers to welcome students, collect intake information, pair students with consultants, and do their best to ensure that consultations run smoothly. As a service that strives to provide a welcoming and supportive environment for students, the front desk's role is clearly important.University Writing Cente

Recommendations on patient-facing websites regarding diagnostic imaging for low back, knee, and shoulder pain: A scoping review

Objective To describe and synthesise the content of public-facing websites regarding the use of diagnostic imaging for adults with lower back pain, knee, and shoulder pain. Methods Scoping review conducted in accordance with PRISMA guidance. A Google search was performed to identify public-facing websites that were either United Kingdom-based, or National Health Service affiliated. The DISCERN tool was used to appraise website quality before information regarding the use of imaging was synthesised using thematic analysis. Results Eighty-six websites were included, with 48 making reference to the use of imaging. The information within the majority (n = 43) of public-facing websites aligns with best available evidence. Where there is inconsistency, this may be explained by lower website quality. Three themes were apparent regarding the use of imaging – imaging to inform diagnosis and management; imaging in context; patient experience and expectations. Conclusion The recommendations and rationale for use of imaging contained within public-facing websites does not appear to justify the increase in imaging rates for musculoskeletal pain in the UK. Innovation Publicly available information following a novel search strategy, is largely aligned with best evidence, further understanding is required to determine reasons for requesting imaging from a patient and clinician perspective

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Estimating heterogeneous treatment effects from observational data is a crucial task across many fields, helping policy and decision-makers take better actions. There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data. We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on the level of hidden confounding. We derive the B-Learner by adapting recent results for sharp and valid bounds of the average treatment effect (Dorn et al., 2021) into the framework given by Kallus & Oprescu (2022) for robust and model-agnostic learning of distributional treatment effects. The B-Learner can use any function estimator such as random forests and deep neural networks, and we prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods. Semi-synthetic experimental comparisons validate the theoretical findings, and we use real-world data demonstrate how the method might be used in practice.Comment: 18 pages, 3 figure

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages

In this paper, we introduce a novel method for enhancing the effectiveness of on-policy Deep Reinforcement Learning (DRL) algorithms. Current on-policy algorithms, such as Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), do not sufficiently account for cautious interaction with the environment. Our method addresses this gap by explicitly integrating cautious interaction in two critical ways: by maximizing a lower-bound on the true value function plus a constant, thereby promoting a \textit{conservative value estimation}, and by incorporating Thompson sampling for cautious exploration. These features are realized through three surprisingly simple modifications to the A3C algorithm: processing advantage estimates through a ReLU function, spectral normalization, and dropout. We provide theoretical proof that our algorithm maximizes the lower bound, which also grounds Regret Matching Policy Gradients (RMPG), a discrete-action on-policy method for multi-agent reinforcement learning. Our rigorous empirical evaluations across various benchmarks consistently demonstrates our approach's improved performance against existing on-policy algorithms. This research represents a substantial step towards more cautious and effective DRL algorithms, which has the potential to unlock application to complex, real-world problems

DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the billions of potential interventions to maximize the expected influence on the target phenotype. However, to reduce the risk of failure in future stages of trials, practical experiment design aims to find a set of interventions that maximally change a target phenotype via diverse mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the rate of significant discoveries per experiment while simultaneously probing for a wide range of diverse mechanisms during a genomic experiment campaign. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation covering both synthetic as well as real-world experimental design tasks. DiscoBAX outperforms existing state-of-the-art methods for experimental design, selecting effective and diverse perturbations in biological systems

On the assessment of pedestrian distress in urban winds

Urban winds can cause a risk to pedestrian safety if not properly assessed. High-rise buildings produce a complex flow field at ground level, where regions of accelerated and recirculating flows are present. Gust wind speeds provide an indication of the maximal speed pedestrian might experience due to the unsteady flow. In this study, low- and high-fidelity numerical and experimental techniques to predict pedestrian level winds are tested on a realistic full-scale test-route at the University of Birmingham Campus during a storm event. Results show that it is beneficial to increase the complexity of simulations as a direct correspondence exists between the gust wind speed and the turbulent environment. While not much gain is achieved switching from Irwin Probes to hot-wire anemometry, LES greatly outperforms RANS and challenges experimental simulations in terms of reliability. The validity of the peak factor is also questioned and a general comment on the adequacy of each technique is discussed

Differentiable Multi-Target Causal Bayesian Experimental Design

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.Comment: Camera-ready version ICML 202