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James Thompson

Publications and source records attributed to James Thompson.

At least 19 recordsLinked to original sources
arXiv2026

Tracking and Predicting Evolution of Social Communities

We develop an algorithmic framework for studying the evolution of communities in social networks. We begin with the theoretical foundation, from which we conclude that an evolution is at most as strong as its weakest link. This allows us to formulate an efficient algorithm to identify all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, to identify those features of the early stages of a community that indicate whether a community is going to be shortlived or not. Our results show that it is possible to correlate the lifespan of a community to structural parameters of its early evolution; these conclusions are robust across all the social networks we have investigated.

cs.SI↗
arXiv2026

Operation-Type-Aware Client Routing for Leader-Based Consensus Datastores

Leader-based consensus datastores (etcd, ZooKeeper) face two competing routing goals: spread load evenly across members, and route operations to the member whose protocol role matches the operation. Writes must commit through the leader, so sending them elsewhere adds a forwarding hop. Linearizable reads need only a lightweight leader confirmation before any member can serve them locally. The upstream etcd client uses gRPC's round_robin balancer, distributing reads and writes uniformly across cluster members. An operation-aware client resolves this by pinning writes to the leader and distributing reads across the healthy read pool. In steady state on a 3-node etcd cluster (80/20 read/write mix, 5 trials), this lowers write P50 by 29% and raises throughput by 9%. When a follower degrades silently, the operation-aware client detects the latency shift and removes it from the read pool, cutting read P99 by 64%, write P99 by 74%, and raising throughput by 89%. The same routing rule applied to ZooKeeper (ZAB protocol, different implementation) points in the same direction, showing that the result follows from leader-based consensus structure rather than one system's implementation. The key obstacle to discovering this policy adaptively is that the leader confirmation round-trip occurs between cluster members, so the client sees only a blended latency signal rather than the decisive coordination cost directly.

cs.DC↗
arXiv2026

Seekable OCI: Lazy-Loading Container Images via Range-Request Indexing

Container image pulling accounts for the majority of pod startup time in Kubernetes environments. Standard pull downloads the entire image before the container can start, even when the application accesses only a fraction of the image content at startup. We present SOCI (Seekable OCI), a lazy-loading architecture that enables containers to start without downloading the full image. SOCI builds an external index over standard OCI images, mapping files to byte ranges within compressed layers. At runtime, a FUSE filesystem intercepts file accesses and serves them via HTTP range requests. Unlike prior approaches that require image format conversion, SOCI works with unmodified images and standard registries. The index is stored as an OCI referrer artifact, requiring no changes to images, registries, or deployment tooling. On a 1.3 GB Python web service image, SOCI reduces cold-start pull time from 20 seconds to approximately 2.8 seconds (7.4x speedup), with pull time independent of image size. Larger images see larger speedups (9.3x on a 2.5 GB image) because SOCI pull time is constant while standard pull scales linearly. We measure a crossover at 80% access density: below this, lazy loading wins; above, parallel full pull is faster. SOCI lazy loading is deployed in production on Amazon EKS and Amazon ECS Fargate (which launched 18.4 million tasks per day during Prime Day 2025), and has been serving lazy-load requests since 2023. EKS Auto Mode uses SOCI's parallel pull mode for GPU instances.

cs.DC↗
arXiv2026

Bounded-Memory Parallel Image Pulling for Large Container Images

AI/ML workloads increasingly run as containers, where a container image must be downloaded to the host before the workload can start. This cold image pull lands on the critical path whenever a training or inference job scales up or a host is updated, and for GPU workloads it has become the dominant component of startup time as AI/ML images reach 31--48~GiB compressed. We present Disk-Backed Parallel Pull (DBPP), an alternative to the in-memory ordered reassembly used by containerd~2.2, the upstream container runtime. containerd splits layers into chunks fetched concurrently over HTTP range requests, but chunks that arrive out of order accumulate in the runtime heap until a sequential consumer drains them in order. This backlog grows with image size, and on GPU nodes where host memory is shared with frameworks and model weights, it leads to out of memory (OOM) termination of the runtime itself. DBPP writes each chunk directly to its target byte offset on disk, eliminating the ordering dependency and bounding memory regardless of image size. Because each layer lands on disk as a complete, seekable file, DBPP runs SHA-256 digest verification and decompression simultaneously, two passes containerd must run one after the other. In controlled experiments across five production-scale images (up to 48.5~GiB), DBPP reduces peak daemon memory by 8.7--25.3$\times$ while maintaining comparable pull throughput. On a memory-constrained node, containerd~2.2 is OOM-killed pulling a 31.4~GiB image while DBPP completes the same pull. The underlying idea reaches past container images: any pipeline that buffers data in memory only to enforce ordering can move that buffer to disk once the backing store is fast enough, trading a scarce, contended resource for an abundant one.

cs.DC↗
arXiv2025

An AI system to help scientists write expert-level empirical software

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments\cite{hannay2009how}. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS)\cite{silver2016mastering} to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish, and numerical solution of integrals, and a novel rule-based construction for time series forecasting. By devising and implementing novel solutions to diverse tasks, ERA represents a significant step towards accelerating scientific progress.

cs.AI↗
arXiv2024

Ultrafast Processes in 1,2-Dichloroethene measured with a Universal XUV probe

The presence of two chlorine atoms in 1,2-dichloroethene allows for isomerisation around the double bond. This isomerisation can lead to rich photochemistry. We present a time-resolved pump-probe photoelectron spectroscopy measurement on both the cis- and trans- isomers of 1,2-dichloroethene. A universal XUV probe of 22.3 eV is used allowing observation of photoelectrons formed anywhere on the potential energy surface, even from the ground-state or dissociation products. Following excitation with a 200 nm probe both ultrafast excited state dynamics and product formation are observed within the time resolution of the experiment. Excited state population begins to return to the ground state on an ultrafast time scale (< 70 fs) and population of products channels is observed on the same timescale. With the aid of ab initio calculations it is found that population transfer from the excited state is facilitated by vibrational modes involving C-C-H bends.

physics.chem-ph↗
arXiv2022

Convex geometries representable with colors, by ellipses on the plane, and impossible by circles

A convex geometry is a closure system satisfying the anti-exchange property. This paper, following the work of K. Adaricheva and M. Bolat (2016) and the Polymath REU 2020 team, continues to investigate representations of convex geometries on a 5-element base set. It introduces several properties: the opposite property, nested triangle property, area Q property, and separation property, of convex geometries of circles on a plane, preventing this representation for numerous convex geometries on a 5-element base set. It also demonstrates that all 672 convex geometries on a 5-element base set have a representation by ellipses, as given in the appendix for those without a known representation by circles, and introduces a method of expanding representation with circles by defining unary predicates, shown as colors.

math.CO↗
arXiv2020

Convex geometries representable by at most 5 circles on the plane

A convex geometry is a closure system satisfying the anti-exchange property. In this work we document all convex geometries on 4- and 5-element base sets with respect to their representation by circles on the plane. All 34 non-isomorphic geometries on a 4-element set can be represented by circles, and of the 672 geometries on a 5-element set, we made representations of 623. Of the 49 remaining geometries on a 5-element set, one was already shown not to be representable due to the Weak Carousel property, as articulated by Adaricheva and Bolat (Discrete Mathematics, 2019). In this paper we show that 7 more of these convex geometries cannot be represented by circles on the plane, due to what we term the Triangle Property.

math.CO↗
arXiv2020

Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

cs.CV↗
arXiv2020

Approximation of Riemannian measures by Stein's method

In this article, we present the theoretical basis for an approach to Stein's method for probability distributions on Riemannian manifolds. Using a semigroup representation for the solution to the Stein equation, we use tools from stochastic calculus to estimate the derivatives of the solution, yielding a bound on the Wasserstein distance. We first assume the Bakry-Emery-Ricci tensor is bounded below by a positive constant, after which we deal separately with the case of uniform approximation on a compact manifold. Applications of these results are currently under development and will appear in a subsequent article.

math.PR↗
arXiv2019

Exponential integrability and exit times of diffusions on sub-Riemannian and metric measure spaces

In this article we derive moment estimates, exponential integrability, concentration inequalities and exit times estimates for canonical diffusions in two settings each beyond the scope of Riemannian geometry. Firstly, we consider sub-Riemannian limits of Riemannian foliations. Secondly, we consider the non-smooth setting of $\mathrm{RCD}^*(K,N)$ spaces. In each case the necessary ingredients are an It\^{o} formula and a comparison theorem for the Laplacian, for which we refer to the recent literature. As an application, we derive pointwise Carmona-type estimates on eigenfunctions of Schr\"{o}dinger operators.

math.PR↗
arXiv2018

Functional inequalities for Feynman-Kac semigroups

Using the tools of stochastic analysis, we prove various gradient estimates and Harnack inequalities for Feynman-Kac semigroups with possibly unbounded potentials. One of the main results is a derivative formula which can be used to characterize a lower bound on Ricci curvature using a potential.

math.FA↗
arXiv2018

Uniform gradient estimates on manifolds with a boundary and applications

We revisit the problem of obtaining uniform gradient estimates for Dirichlet and Neumann heat semigroups on Riemannian manifolds with boundary. As applications, we obtain isoperimetric inequalities, using Ledoux's argument, and uniform quantitative gradient estimates, firstly for $C^2_b$ functions with boundary conditions and then for the unit spectral projection operators of Dirichlet and Neumann Laplacians.

math.FA↗
arXiv2017

Quantitative $C^1$-estimates by Bismut formulae

For a $C^2$ function $u$ and an elliptic operator $L$, we prove a quantitative estimate for the derivative $du$ in terms of local bounds on $u$ and $Lu$. An integral version of this estimate is then used to derive a condition for the zero-mean value property of $\Delta u$. An extension to differential forms is also given. Our approach is probabilistic and could easily be adapted to other settings.

math.AP↗
arXiv2017

Soil Property and Class Maps of the Conterminous US at 100 meter Spatial Resolution based on a Compilation of National Soil Point Observations and Machine Learning

With growing concern for the depletion of soil resources, conventional soil data must be updated to support spatially explicit human-landscape models. Three US soil point datasetswere combined with a stack of over 200 environmental datasets to generate complete coverage gridded predictions at 100 m spatial resolution of soil properties (percent organic C, total N, bulk density, pH, and percent sand and clay) and US soil taxonomic classes (291 great groups and 78 modified particle size classes) for the conterminous US. Models were built using parallelized random forest and gradient boosting algorithms. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100 and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Model validation results indicate an out-of-bag classification accuracy of 60 percent for great groups, and 66 percent for modified particle size classes; for soil properties cross-validated R-square ranged from 62 percent for total N to 87 percent for pH. Nine independent validation datasets were used to assess prediction accuracies for soil class models and results ranged between 24-58 percent and 24-93 percent for great group and modified particle size class prediction accuracies, respectively. The hybrid "SoilGrids+" modeling system that incorporates remote sensing data, local predictions of soil properties, conventional soil polygon maps, and machine learning opens the possibility for updating conventional soil survey data with machine learning technology to make soil information easier to integrate with spatially explicit models, compared to multi-component map units.

physics.geo-ph↗
arXiv2017

Derivative and divergence formulae for diffusion semigroups

For a semigroup $P_t$ generated by an elliptic operator on a smooth manifold $M$, we use straightforward martingale arguments to derive probabilistic formulae for $P_t(V(f))$, not involving derivatives of $f$, where $V$ is a vector field on $M$. For non-symmetric generators, such formulae correspond to the derivative of the heat kernel in the forward variable. As an application, these formulae can be used to derive various shift-Harnack inequalities.

math.PR↗
arXiv2016

Derivatives of Feynman-Kac Semigroups

We prove Bismut-type formulae for the first and second derivatives of a Feynman-Kac semigroup on a complete Riemannian manifold. We derive local estimates and give bounds on the logarithmic derivatives of the integral kernel. Stationary solutions are also considered. The arguments are based on local martingales, although the assumptions are purely geometric.

math.DG↗