arXiv subjects

Simon Smith

Publications and source records attributed to Simon Smith.

9 recordsLinked to original sources

arXiv2026

Generation of Diverse and Functional Robot Designs using Superquadrics Parametrisation and Quality-Diversity

Generative design of robots requires navigating a vast search-space, encompassing physical configurations and behavioural parameters. Evolutionary Algorithms (EAs) have shown promising results, but often converge prematurely to a small set of sub-optimal designs. Most EAs fail to maintain sufficient diversity in the population that would allow the discovery of distinct functional robots. To counter premature convergence, we introduce a superquadrics-based representation (SQs) for robot bodies. SQs are interpretable, compact and computationally efficient mathematical representations of 3D geometrical shapes that can be tuned to specific design-spaces. To encourage morphological diversity, we combine this representation with a quality-diversity (QD) algorithm (MAP-Elites). We compare SQs and Compositional Pattern Producing Networks representations as generators of morphologies, combining them with standard EAs and MAP-Elites. In two test environments, we find that using SQs to generate morphology in conjunction with the MAP-Elites algorithm reaches the highest QD-score across both environments, maximising diversity of design and functionality of generated robots. The findings highlight the benefits of using a compact and interpretable geometric representation for exploring a complex design-space and suggest that combining SQs with an explicit diversity mechanism increases the quality and number of designs generated.

cs.RO↗

arXiv2023

The AutoSPADA Platform: User-Friendly Edge Computing for Distributed Learning and Data Analytics in Connected Vehicles

Contemporary connected vehicles host numerous applications, such as diagnostics and navigation, and new software is continuously being developed. However, the development process typically requires offline batch processing of large data volumes. In an edge computing approach, data analysts and developers can instead process sensor data directly on computational resources inside vehicles. This enables rapid prototyping to shorten development cycles and reduce the time to create new business values or insights. This paper presents the design, implementation, and operation of the AutoSPADA edge computing platform for distributed data analytics. The platform's design follows scalability, reliability, resource efficiency, privacy, and security principles promoted through mature and industrially proven technologies. In AutoSPADA, computational tasks are general Python scripts, and we provide a library to, for example, read signals from the vehicle and publish results to the cloud. Hence, users only need Python knowledge to use the platform. Moreover, the platform is designed to be extended to support additional programming languages.

cs.DC↗

arXiv2021

The UW Virtual Brain Project: An immersive approach to teaching functional neuroanatomy

Learning functional neuroanatomy requires forming mental representations of 3D structure, but forming such representations from 2D textbook diagrams can be challenging. We address this challenge in the UW Virtual Brain Project by developing 3D narrated diagrams, which are interactive, guided tours through 3D models of perceptual systems. Lessons can be experienced in virtual realty (VR) or on a personal computer monitor (PC). We predicted participants would learn from lessons presented on both VR and PC devices (comparing pre-test/post-test scores), but that VR would be more effective for achieving both content-based learning outcomes (i.e test performance) and experience-based learning outcomes (i.e., reported enjoyment and ease of use). All participants received lessons about the visual system and auditory system, one in VR and one on a PC(order counterbalanced). We assessed content learning using a drawing/labeling task on paper (2D drawing) in Experiment 1 and a Looking Glass autostereoscopic display (3D drawing) in Experiment 2. In both experiments, we found that the UW Virtual Brain Project lessons were effective for teaching functional neuroanatomy, with no difference between devices. However, participants reported VR was more enjoyable and easier to use. We also evaluated the VR lessons in our Classroom Implementation during an undergraduate course on perception. Students reported that the VR lessons helped them make progress on course learning outcomes, especially for learning system pathways. They suggested lessons could be improved byadding more examples and providing more time to explore in VR.

cs.HC↗

arXiv2019

S-RASTER: Contraction Clustering for Evolving Data Streams

Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory, quickly identifying approximate clusters. It also exhibits good scalability in the presence of multiple CPU cores. RASTER exhibits very competitive performance compared to standard clustering algorithms, but at the cost of decreased precision. Yet, RASTER is limited to batch processing and unable to identify clusters that only exist temporarily. In contrast, S-RASTER is an adaptation of RASTER to the stream processing paradigm that is able to identify clusters in evolving data streams. This algorithm retains the main benefits of its parent algorithm, i.e. single-pass linear time cost and constant memory requirements for each discrete time step within a sliding window. The sliding window is efficiently pruned, and clustering is still performed in linear time. Like RASTER, S-RASTER trades off an often negligible amount of precision for speed. Our evaluation shows that competing algorithms are at least 50% slower. Furthermore, S-RASTER shows good qualitative results, based on standard metrics. It is very well suited to real-world scenarios where clustering does not happen continually but only periodically.

cs.DS↗

arXiv2019

Contraction Clustering (RASTER): A Very Fast Big Data Algorithm for Sequential and Parallel Density-Based Clustering in Linear Time, Constant Memory, and a Single Pass

Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering algorithms are infeasible due to their high memory requirements and/or unfavorable runtime complexity. In contrast, Contraction Clustering (RASTER) is a single-pass algorithm for identifying density-based clusters with linear time complexity. Due to its favorable runtime and the fact that its memory requirements are constant, this algorithm is highly suitable for big data applications where the amount of data to be processed is huge. It consists of two steps: (1) a contraction step which projects objects onto tiles and (2) an agglomeration step which groups tiles into clusters. This algorithm is extremely fast in both sequential and parallel execution. Our quantitative evaluation shows that a sequential implementation of RASTER performs significantly better than various standard clustering algorithms. Furthermore, the parallel speedup is significant: on a contemporary workstation, an implementation in Rust processes a batch of 500 million points with 1 million clusters in less than 50 seconds on one core. With 8 cores, the algorithm is about four times faster.

cs.DS↗

arXiv2019

Facilitating Rapid Prototyping in the OODIDA Data Analytics Platform via Active-Code Replacement

OODIDA (On-board/Off-board Distributed Data Analytics) is a platform for distributed real-time analytics, targeting fleets of reference vehicles in the automotive industry. Its users are data analysts. The bulk of the data analytics tasks are performed by clients (on-board), while a central cloud server performs supplementary tasks (off-board). OODIDA can be automatically packaged and deployed, which necessitates restarting parts of the system, or all of it. As this is potentially disruptive, we added the ability to execute user-defined Python modules on clients as well as the server. These modules can be replaced without restarting any part of the system; they can even be replaced between iterations of an ongoing assignment. This feature is referred to as active-code replacement. It facilitates use cases such as iterative A/B testing of machine learning algorithms or modifying experimental algorithms on-the-fly. Consistency of results is achieved by majority vote, which prevents tainted state. Active-code replacement can be done in less than a second in an idealized setting whereas a standard deployment takes many orders of magnitude more time. The main contribution of this paper is the description of a relatively straightforward approach to active-code replacement that is very user-friendly. It enables a data analyst to quickly execute custom code on the cloud server as well as on client devices. Sensible safeguards and design decisions ensure that this feature can be used by non-specialists who are not familiar with the implementation of OODIDA in general or this feature in particular. As a consequence of adding the active-code replacement feature, OODIDA is now very well-suited for rapid prototyping.

cs.DC↗

arXiv2019

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is OODIDA (On-board/Off-board Distributed Data Analytics), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

cs.PL↗

arXiv2016

Most recent changepoint detection in Panel data

Detecting recent changepoints in time-series can be important for short-term prediction, as we can then base predictions just on the data since the changepoint. In many applications we have panel data, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such panel data which aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computationally efficient as it involves analysing each time-series independently to obtain a profile likelihood like quantity that summarises the evidence for the series having either no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series which has their most recent changepoint at that time. We demonstrate the usefulness of this method on two data sets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms.

stat.AP↗

arXiv2006

Infinite primitive directed graphs

A group $G$ of permutations of a set $\Omega$ is {\em primitive} if it acts transitively on $\Omega$, and the only $G$-invariant equivalence relations on $\Omega$ are the trivial and universal relations. A graph $\Gamma$ is {\em primitive} if its automorphism group acts primitively on its vertex set. A graph $\Gamma$ has {\em connectivity one} if it is connected and there exists a vertex $\alpha$ of $\Gamma$, such that the induced graph $\Gamma \setminus \{\alpha\}$ is not connected. If $\Gamma$ has connectivity one, a {\em block} of $\Gamma$ is a connected subgraph that is maximal subject to the condition that it does not have connectivity one. The primitive undirected graphs with connectivity one have been fully classified by Jung and Watkins: the blocks of such graphs are primitive, pairwise-isomorphic and have at least three vertices. When one considers the general case of a directed primitive graph with connectivity one, however, this result no longer holds. In this paper we investigate the structure of these directed graphs, and obtain a complete characterisation.

math.CO↗