arXiv subjects

David Wood

Publications and source records attributed to David Wood.

At least 19 recordsLinked to original sources

arXiv2025

Synchronous Propagation of Periodic Signals in Feedforward Networks of Standard Model Neurons

Periodic signals propagating along chains are common in biology, for example in locomotion and peristalsis, and are also of interest for continuum robots. In previous work we constructed such networks as 'feedforward lifts' of a central pattern generator (CPG). When the CPG undergoes periodic oscillations, created by Hopf bifurcation or other mechanisms, it can then transmit periodic signals along one or more feedforward chains in a synchronous or phase-synchronous manner. We proved necessary and sufficient conditions for the stability of these lifted periodic orbits, in several senses. Here we examine the implications of the resulting theory for chains of neurons, using several standard neuron models: FitzHugh-Nagumo, Morris-Lecar, Hindmarsh-Rose, and Hodgkin-Huxley. We compare different notions of transverse stability, and summarize some numerical simulations showing that for all these neuron models the propagating signal can be transversely Floquet stable. Finally we discuss implications for less idealized models.

nlin.CD↗

arXiv2025

Stable Synchronous Propagation in Feedforward Networks for Biped Locomotion

Rhythmic gait patterns in animal locomotion are widely believed to be produced by a central pattern generator (CPG), a network of neurons that drives the muscle groups. In previous papers we have discussed how phase-synchronous signals can propagate along chains of neurons using a feedforward lift of the CPG, given sufficient conditions for stability to synchrony-breaking perturbations, and shown that stable signals are common for four standard neuron models. Here we apply these ideas to biped locomotion using a fifth model: Wilson-Cowan (or rate model) neurons. Feedforward architecture propagates the phase pattern of a CPG along a chain of identical modules. For certain rate models, we give analytic conditions that are sufficient for transverse Liapunov and Floquet stability. We compare different notions of transverse stability, summarise some numerical simulations, and outline an application to signal propagation in a model of biped locomotion.

math.DS↗

arXiv2025

GneissWeb: Preparing High Quality Data for LLMs at Scale

Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's ability to generalize on a wide range of downstream tasks. Large pre-training datasets for leading LLMs remain inaccessible to the public, whereas many open datasets are small in size (less than 5 trillion tokens), limiting their suitability for training large models. In this paper, we introduce GneissWeb, a large dataset yielding around 10 trillion tokens that caters to the data quality and quantity requirements of training LLMs. Our GneissWeb recipe that produced the dataset consists of sharded exact sub-string deduplication and a judiciously constructed ensemble of quality filters. GneissWeb achieves a favorable trade-off between data quality and quantity, producing models that outperform models trained on state-of-the-art open large datasets (5+ trillion tokens). We show that models trained using GneissWeb dataset outperform those trained on FineWeb-V1.1.0 by 2.73 percentage points in terms of average score computed on a set of 11 commonly used benchmarks (both zero-shot and few-shot) for pre-training dataset evaluation. When the evaluation set is extended to 20 benchmarks (both zero-shot and few-shot), models trained using GneissWeb still achieve a 1.75 percentage points advantage over those trained on FineWeb-V1.1.0.

cs.CL↗

arXiv2025

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extraction from tables, charts, diagrams, sketches, and infographics, as well as general image tasks. The architecture of Granite Vision is centered around visual modality alignment with a decoder-only, 2 billion parameter Granite large language model. Additionally, we introduce a dedicated safety classification approach in test-time that leverages a sparse set of attention vectors to identify potential harmful inputs. Despite its lightweight architecture, Granite Vision achieves strong results in standard benchmarks related to visual document understanding, as well as on the LiveXiv benchmark, which is designed to avoid test set contamination by using a constantly updated corpus of recently published Arxiv papers. We are releasing the model under the Apache-2 license, allowing for both research and commercial use, while offering complete visibility into the training data and other relevant details. See https://huggingface.co/ibm-granite/ for model weights.

cs.CV↗

arXiv2024

Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. Results: Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis. Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.

cs.CV↗

arXiv2024

Mechanical and suture-holding properties of a UV-cured atelocollagen membrane with varied crosslinked architecture

The mechanical competence and suturing ability of collagen-based membranes are paramount in Guided Bone Regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane. UV-cured networks were obtained via either single functionalisation of AC with 4-vinylbenzyl chloride (4VBC) or sequential functionalisation of AC with both 4VBC and methacrylic anhydride (MA). The wet-state compression modulus (Ec), Atomic Force Microscopy elastic modulus (EAFM) and swelling ratio (SR) were significantly affected by the UV-cured network architecture, leading up to a three-fold reduction in SR, and about two-fold increase in both Ec and EAFM, in the sequentially functionalised, compared to the single-functionalised, samples. Electron microscopy, dimensional analysis and compression testing revealed the direct impact of the ethanol series dehydration process on membrane microstructure, yielding densification of the freshly synthesised porous samples and a pore-free microstructure with increased Ec. Noteworthy, the single-functionalised, but not the sequentially functionalised, samples displayed higher suture retention strength in both the dry state and following 1 hour in Phosphate Buffered Saline (PBS), compared to Bio-Gide(r). These structure-property relationships confirm the key role played by the molecular architecture of covalently crosslinked collagen, aimed towards long-lasting resorbable membranes for predictable GBR.

q-bio.TO↗

arXiv2024

Data-Prep-Kit: getting your data ready for LLM application development

Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG).

cs.AI↗

arXiv2024

EdgeSphere: A Three-Tier Architecture for Cognitive Edge Computing

Computing at the edge is increasingly important as Internet of Things (IoT) devices at the edge generate massive amounts of data and pose challenges in transporting all that data to the Cloud where they can be analyzed. On the other hand, harnessing the edge data is essential for offering cognitive applications, if the challenges, such as device capabilities, connectivity, and heterogeneity can be overcome. This paper proposes a novel three-tier architecture, called EdgeSphere, which harnesses resources of the edge devices, to analyze the data in situ at the edge. In contrast to the state-of-the-art cloud and mobile applications, EdgeSphere applications span across cloud, edge gateways, and edge devices. At its core, EdgeSphere builds on Apache Mesos to optimize resources usage and scheduling. EdgeSphere has been applied to practical scenarios and this paper describes the engineering challenges faced as well as innovative solutions.

cs.DC↗

arXiv2024

Letter to the Editor: What are the legal and ethical considerations of submitting radiology reports to ChatGPT?

This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports. While acknowledging the potential of LLMs in generating labels for computer vision, concerns are raised about the ethical implications of using patient data without explicit approval, highlighting the necessity of stringent data protection measures under GDPR.

cs.CV↗

arXiv2023

Developing and implementing an Einsteinian science curriculum from Years 3 to 10 : Part A Concepts, rationale and learning outcomes

There has been a growing realisation that school science curricula do not adequately reflect the revolutionary changes in our scientific understanding of the 20th century. This discrepancy between current school education and our modern scientific understanding has led to calls for the modernisation of the science curriculum. Although there have been attempts to introduce topics of Einsteinian physics (i.e., quantum physics and relativity) to school education, often at the secondary level, we still lack a seamless curriculum in which modern science concepts are gradually introduced in primary and middle schools. Guided by the Model of Educational Reconstruction and following a mixed-methods research design, the Einstein-First project aims to address this gap. Einstein-First has developed and implemented an Einsteinian curriculum from Years 3 to 10 (students aged 7- 16) that resolves the disconnect between science in schools and the modern world. This paper presents the concepts, rationale, and learning outcomes of the curriculum implementation in six Australian schools with 315 students across Years 3 to 10. Our findings lay the foundation for informed curriculum development towards a school education that can enhance students' understanding and appreciation of the fundamental concepts of modern science and its impact on our society.

physics.ed-ph↗

arXiv2023

Developing and implementing an Einsteinian science curriculum from Years 3 to 10: Part B Teacher upskilling: response to training and teacher's classroom experience

Recent years have seen a growing interest in modernizing physics and science curricula around the world. While many science educators and curriculum developers design instructional resources to successfully introduce topics of Einsteinian physics to young learners, it is clear that successful curriculum development needs to rest on successful teacher professional development.Teachers with or without science backgrounds were trained in short professional learning workshops or completed micro-credential courses. The courses enabled teachers to gain knowledge and confidence to deliver the Einstein-First program. Detailed lesson plans and instructional videos for teachers define the lessons. Questionnaires were used to collect data, and teacher interviews were conducted following the various teacher training programs. The research results show that teachers effectively deliver the Einsteinian physics programs and that their subject matter and pedagogical content knowledge increased. In addition, teacher attitudes were favorable towards modernizing the physics curriculum. We conclude that it is feasible to upskill teachers from diverse backgrounds in Einsteinian physics and break the cycle that has inhibited the modernization of school curricula.

physics.ed-ph↗

arXiv2023

Stable Synchronous Propagation of Signals by Feedforward Networks

We analyse the dynamics of networks in which a central pattern generator (CPG) transmits signals along one or more feedforward chains in a synchronous or phase-synchronous manner. Such propagating signals are common in biology, especially in locomotion and peristalsis, and are of interest for continuum robots. We construct such networks as feedforward lifts of the CPG. If the CPG dynamics is periodic, so is the lifted dynamics. Synchrony with the CPG manifests as a standing wave, and a regular phase pattern creates a travelling wave. We discuss Liapunov, asymptotic, and Floquet stability of the lifted periodic orbit and introduce transverse versions of these conditions that imply stability for signals propagating along arbitrarily long chains. We compare these notions to a simpler condition, transverse stability of the synchrony subspace, which is equivalent to Floquet stability when nodes are 1-dimensional.

math.DS↗

arXiv2021

On the Aerodynamics of Multistage Co-Axial Vertical-Axis Wind Turbines

This study explored the aerodynamics of a new multi-stage co-axial vertical-axis wind turbine based on bio-inspiration from natural swimming habit of fish. The turbine was formed from a conventional straight-bladed vertical-axis turbine (VAWT) with a small inner rotor, also of three blades. The azimuthal and radial locations of the inner rotor were varied. Using numerical simulations, performance of the proposed new design was evaluated over a range of tip-speed ratios. The preliminary results identified a 600% increase in power output for multi-stage VAWTs at tip-speed rations TSR = 0 - 3, and a substantial drop in power coefficient at TSR > 3.0. The wake dynamics analyses revealed that the increase was due to interactions between the blades of one rotor and the other. This reduced the unsteady separation from the outer rotor which produced most of the power. A detailed parametric study was also completed, which showed the implications of geometric and kinematic details on the performance of the proposed multistage VAWT.

physics.flu-dyn↗

arXiv2021

TIC 172900988: A Transiting Circumbinary Planet Detected in One Sector of TESS Data

We report the first discovery of a transiting circumbinary planet detected from a single sector of TESS data. During Sector 21, the planet TIC 172900988b transited the primary star and then 5 days later it transited the secondary star. The binary is itself eclipsing, with a period of P = 19.7 days and an eccentricity of e = 0.45. Archival data from ASAS-SN, Evryscope, KELT, and SuperWASP reveal a prominent apsidal motion of the binary orbit, caused by the dynamical interactions between the binary and the planet. A comprehensive photodynamical analysis of the TESS, archival and follow-up data yields stellar masses and radii of M1 = 1.2384 +/- 0.0007 MSun and R1 = 1.3827 +/- 0.0016 RSun for the primary and M2 = 1.2019 +/- 0.0007 MSun and R2 = 1.3124 +/- 0.0012 RSun for the secondary. The radius of the planet is R3 = 11.25 +/- 0.44 REarth (1.004 +/- 0.039 RJup). The planet's mass and orbital properties are not uniquely determined - there are six solutions with nearly equal likelihood. Specifically, we find that the planet's mass is in the range of 824 < M3 < 981 MEarth (2.65 < M3 < 3.09 MJup), its orbital period could be 188.8, 190.4, 194.0, 199.0, 200.4, or 204.1 days, and the eccentricity is between 0.02 and 0.09. At a V = 10.141 mag, the system is accessible for high-resolution spectroscopic observations, e.g. Rossiter-McLaughlin effect and transit spectroscopy.

astro-ph.EP↗

arXiv2021

AutoAI-TS: AutoAI for Time Series Forecasting

A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that performs well on a given data is still challenging. Automatically providing a good set of models to users for a given dataset saves both time and effort from using trial-and-error approaches with a wide variety of available models along with parameter optimization. We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset. With its flexible zero-conf design, AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users and provides a trained model that is ready to use. For given data, AutoAI-TS utilizes a wide variety of models including classical statistical models, Machine Learning (ML) models, statistical-ML hybrid models and deep learning models along with various transformations to create forecasting pipelines. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline. The paper describe in detail all the technical aspects of AutoAI-TS along with extensive benchmarking on a variety of real world data sets for various use-cases. Benchmark results show that AutoAI-TS, with no manual configuration from the user, automatically trains and selects pipelines that on average outperform existing state-of-the-art time series forecasting toolkits.

cs.LG↗

arXiv2019

NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification

Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network. When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training. Encouragingly, the agent learns, without explicit instruction, a search policy in agreement with standardized radiological hallmarks of Alzheimer's disease, suggesting a route to automated biomarker discovery for more poorly understood disorders.

cs.LG↗

arXiv2017

Thiol-ene photo-click collagen-PEG hydrogels: impact of water-soluble photoinitiators on cell viability, gelation kinetics and rheological properties

Thiol-ene photo-click hydrogels were prepared via step-growth polymerisation using thiol-functionalised type-I collagen and 8-arm poly(ethylene glycol) norbornene terminated (PEG-NB), as a potential injectable regenerative device. Type-I collagen was thiol-functionalised by a ring opening reaction with 2-iminothiolane (2IT), whereby up to 80 Abs% functionalisation and 90 RPN% triple helical preservation were recorded via 2,4,6-Trinitrobenzenesulfonic acid (TNBS) colorimetric assay and circular dichroism (CD). Type, i.e. 2-Hydroxy-1-[4-(2-hydroxyethoxy)phenyl]-2-methyl-1-propanone (I2959) or lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP), and concentration of photoinitiator were varied to ensure minimal photoinitiator-induced cytotoxicity and to enable thiol-ene network formation of collagen-PEG mixtures. The viability of G292 cells following 24 h culture in photoinitiator-supplemented media was largely affected by the photoinitiator concentration, with I2959-supplemented media observed to induce higher toxic response compared to LAP-supplemented media. In line with the in vitro study, selected photoinitiator concentrations were used to prepare thiol-ene photo-click hydrogels. Gelation kinetics proved to be largely affected by the specific photoinitiator, with LAP-containing thiol-ene mixtures leading to significantly reduced complete gelation time with respect to I2959-containing mixtures. Other than the specific photoinitiator, the photoinitiator concentration was key to adjust hydrogel storage modulus (G'), whereby 15-fold G' increase was observed in samples prepared with 0.5% (w/v) compared to 0.1% (w/v) LAP. The photoinitiator-gelation-cytotoxicity relationships established in this study will be instrumental to the design of orthogonal collagen-based niches for regenerative medicine.

physics.chem-ph↗

arXiv2017

Influence of telopeptides on the structural and physical properties of polymeric and monomeric acid-soluble type I collagen

Currently two factors hinder the use of collagen as building block of regenerative devices: the potential antigenicity, and limited mechanical strength in aqueous environment. Polymeric collagen is naturally found in the cross-linked state and is mechanically tougher than the monomeric, cross-link-free (acid-soluble) collagen ex vivo. The antigenicity of collagen, on the other hand, is mainly ascribed to inter-species variations in amino acid sequences, which are primarily located in the non-helical terminal telopeptides. Although these can be removed through enzymatic treatment to produce atelocollagen, the effect of telopeptide removal on triple helix organization, amino acidic composition and thermal properties is often disregarded. Here, we compare the structural, chemical and physical properties of polymeric and monomeric type I collagen with and without telopeptides, in an effort to elucidate the influence that either covalent crosslinks or telopeptides possess. Circular dichroism (CD) was used to examine the triple helical conformation and quantify the denaturation temperature (Td) of both monomeric collagen (36.5 °C) and monomeric atelocollagen (35.5 °C). CD measurements were combined with differential scanning calorimetry (DSC) in order to gain insight into the triple helix-to-coil thermal transition and shrinkage temperature (Ts) of polymeric atelo collagen (44.8 °C), polymeric collagen (62.7 °C), monomeric atelo collagen (51.4 °C) and monomeric collagen (66.5 °C). Structural and thermal analyses were combined with high pressure liquid chromatography (HPLC) to determine the content of specific collagen amino acidic residues used as markers for the presence of telopeptides and mature crosslinks.

physics.chem-ph↗