An Analysis of the Connections Between Layers of Deep Neural Networks

We present an analysis of different techniques for selecting the connection be- tween layers of deep neural networks. Traditional deep neural networks use ran- dom connection tables between layers to keep the number of connections small and tune to different image features. This kind of connection performs adequately in supervised deep networks because their values are refined during the training. On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers. In this work, we tested four different techniques for connecting the first layer of the network to the second layer on the CIFAR and SVHN datasets and showed that the accuracy can be im- proved up to 3% depending on the technique used. We also showed that learning the connections based on the co-occurrences of the features does not confer an advantage over a random connection table in small networks. This work is helpful to improve the efficiency of connections between the layers of unsupervised deep neural networks

Oblique UAS imagery and point cloud processing for 3D rock glacier monitoring

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesRock glaciers play a large ecological role and are heavily relied upon by local communities for water, power, and revenue. With climate change, the rate at which they are deforming has increased over the years and is making it more important to gain a better understanding of these geomorphological movements for improved predictions, correlations, and decision making. It is becoming increasingly more practical to examine a rock glacier with 3D visualization to have more perspectives and realistic terrain profiles. Recently gaining more attention is the use of Terrestrial Laser Scanners (TLS) and Unmanned Aircraft Systems (UAS) used separately and combined to gather high-resolution data for 3D analysis. This data is typically transformed into highly detailed Digital Elevation Models (DEM) where Differences of DEM (DoD) is used to track changes over time. This study compares these commonly used collection methods and analysis to a newly conceived multirotor UAS collection method and to a new point cloud Multiscale Model to Model Cloud Comparison (M32C) change detection seen from recent studies. Data was collected of the Innere Ölgrube Rock Glacier in Austria with a TLS in 2012 and with a multirotor UAS in 2019. It was found that oblique imagery with terrain height corrections, that creates perspectives similar to what the TLS provides, increased the completeness of data collection for a better reconstruction of a rock glacier in 3D. The new method improves the completeness of data by an average of at least 8.6%. Keeping the data as point clouds provided a much better representation of the terrain. When transforming point clouds into DEMs with common interpolations methods it was found that the average area of surface items could be exaggerated by 2.2 m^2 while point clouds were much more accurate with 0.3 m^2 of accuracy. DoD and M3C2 results were compared and it was found that DoD always provides a maximum increase of at least 1.1 m and decrease of 0.85 m more than M3C2 with larger standard deviation with similar mean values which could attributed to horizontal inaccuracies and smoothing of the interpolated data

Clustering Learning for Robotic Vision

We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters with only a few minutes and a much reduced set of parameters. The goal of this paper is to promote the technique for general-purpose robotic vision systems. We report its use in static image datasets and object tracking datasets. We show that networks trained with clustering learning can outperform large networks trained for many hours on complex datasets.Comment: Code for this paper is available here: https://github.com/culurciello/CL_paper1_cod

Delegating Data Collection in Decentralized Machine Learning

Motivated by the emergence of decentralized machine learning ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two fundamental machine learning challenges: lack of certainty in the assessment of model quality and lack of knowledge regarding the optimal performance of any model. We show that lack of certainty can be dealt with via simple linear contracts that achieve 1-1/e fraction of the first-best utility, even if the principal has a small test set. Furthermore, we give sufficient conditions on the size of the principal's test set that achieves a vanishing additive approximation to the optimal utility. To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract

Incentive-Theoretic Bayesian Inference for Collaborative Science

Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should acknowledge this state of affairs. To this end, we study hypothesis testing when there is an agent (e.g., a researcher or a pharmaceutical company) with a private prior about an unknown parameter and a principal (e.g., a policymaker or regulator) who wishes to make decisions based on the parameter value. The agent chooses whether to run a statistical trial based on their private prior and then the result of the trial is used by the principal to reach a decision. We show how the principal can conduct statistical inference that leverages the information that is revealed by an agent's strategic behavior -- their choice to run a trial or not. In particular, we show how the principal can design a policy to elucidate partial information about the agent's private prior beliefs and use this to control the posterior probability of the null. One implication is a simple guideline for the choice of significance threshold in clinical trials: the type-I error level should be set to be strictly less than the cost of the trial divided by the firm's profit if the trial is successful

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actually harmed the welfare of sicker patients by increasing provider selection behavior. We analyze the incentive misalignments that arise from such average treated outcome metrics, and show that the incentives driving treatment decisions would align with maximizing total patient welfare if the metrics (i) accounted for counterfactual untreated outcomes and (ii) considered total welfare instead of averaging over treated patients. Operationalizing this, we show how counterfactual metrics can be modified to behave reasonably in patient-facing ranking systems. Extending to realistic settings when providers observe more about patients than the regulatory agencies do, we bound the decay in performance by the degree of information asymmetry between principal and agent. In doing so, our model connects principal-agent information asymmetry with unobserved heterogeneity in causal inference

Palaeogeographic reconstruction in the transition zone : the role of geophysical forward modelling in ground investigation surveys

Geophysical survey techniques are commonly used as part of studies to reconstruct past geographies in archaeological and palaeoenvironmental landscape investigations onshore and offshore. However, their use across the intertidal zone for constructing contiguous models is far more challenging. In order to enhance the interpretation of the recovered data forward modelling is used here to demonstrate the effective use of a staged approach to site investigation. Examples of data from electrical and electromagnetic techniques have been modelled and tested with ground truth measurements including trial pits, coring and cone penetrometer testing. This combination of forward modelling and testing has proved to be particularly effective at mapping key geological situations of archaeological interest. The approach is demonstrated by reference to varying sub-surface sediment types exemplified by two field examples from the UK coast where typical palaeolandscape features, namely incised channels and deeply buried topographies are encountered. These palaeogeographic features were chosen as they have high potential for association with the evidence of past human activity.PostprintPeer reviewe

Prediction-Powered Inference

We introduce prediction-powered inference \unicode{x2013} a framework for performing valid statistical inference when an experimental data set is supplemented with predictions from a machine-learning system. Our framework yields provably valid conclusions without making any assumptions on the machine-learning algorithm that supplies the predictions. Higher accuracy of the predictions translates to smaller confidence intervals, permitting more powerful inference. Prediction-powered inference yields simple algorithms for computing valid confidence intervals for statistical objects such as means, quantiles, and linear and logistic regression coefficients. We demonstrate the benefits of prediction-powered inference with data sets from proteomics, genomics, electronic voting, remote sensing, census analysis, and ecology.Comment: Code is available at https://github.com/aangelopoulos/prediction-powered-inferenc

Ionic N-Phenylpyridinium Tetracatenar Mesogens : N -phenylpyridinium tetracatenar mesogens: Competing driving forces in mesophase formation and unprecedented difference in phase stabilisation within a homologous series

Ionic, tetracatenar liquid crystals containing an N-phenylpyridinum core are described; many of these compounds display a SmA phase, something extremely rare in tetracatenar materials. The competing forces driving mesophase formation lead to an unprecedented difference in phase stabilities for SmA and Colh phases