An Adaptive Tangent Feature Perspective of Neural Networks

In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to be transformed during training. We consider linear transformations of features, resulting in a joint optimization over parameters and transformations with a bilinear interpolation constraint. We show that this optimization problem has an equivalent linearly constrained optimization with structured regularization that encourages approximately low rank solutions. Specializing to neural network structure, we gain insights into how the features and thus the kernel function change, providing additional nuance to the phenomenon of kernel alignment when the target function is poorly represented using tangent features. We verify our theoretical observations in the kernel alignment of real neural networks.Comment: 14 pages, 3 figures. Appeared at the First Conference on Parsimony and Learning (CPAL 2024

Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products

A Framework for Modelling Probabilistic Uncertainty in Rainfall Scenario Analysis

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Exploring the effectiveness of evidence-based methods to measure and improve offenders’ engagement in treatment

Treatment non-engagement in forensic settings is a major problem, which has been associated with increased recidivism and higher costs. This thesis aimed to evaluate existing methods of enhancing engagement, test an innovative motivational strategy to enhance engagement, and critically evaluate an effective measurement of engagement. Firstly, a systematic review and a meta-analysis of Randomised Controlled Trials (RCTs) was conducted to evaluate the effectiveness of Motivational Interviewing (MI). It was concluded that MI may be effective for engagement, but measurement of engagement is inconsistent and unreliable. Therefore, MI was integrated into a novel training package for staff in addition to a promising readiness model and a motivational assessment. The feasibility of such intervention was investigated for probation staff, and its preliminary effect on probationers’ group engagement was assessed using the Group Engagement Measure (GEM-27; Macgowan, 1997). Findings showed while it is generally feasible to implement such an intervention, it is possible that short training in such settings might not be as impactful due to organisational issues, staff burnout and external influences. However, GEM-27 showed promise with regards to being able to measure offender engagement. After critically reviewing its characteristics, with further research and modifications, it was concluded GEM could be widely used in forensic settings. In conclusion, advancements in evidence-based measures of engagement and forensic specific strategies to enhance offender engagement are the initial steps towards developing a comprehensive theory of offender engagement and increasing treatment effectiveness