Showing 1–50 of 115 results for author: Richardson, A

A converged architecture for processing 32 Tbps of physics data in real-time at the LHCb experiment

Authors: Roel Aaij, Christina Agapopoulou, Thomas Boettcher, Dorothea vom Bruch, Daniel Hugo Cámpora Pérez, Adrian Casais Vidal, Tommaso Colombo, Daniel C. Craik, Tim Evans, Placido Fernandez Declara, Marianna Fontana, Vladimir V. Gligorov, Arthur Hennequin, Louis Henry, Brij Kishor Jashal, Saverio Mariani, Rosen Matev, Niklas Nolte, Niko Neufeld, Arantza Oyanguren, Alberto Perro, Flavio Pisani, Renato Quagliani, Florian Reiss, Kate A. Richardson , et al. (5 additional authors not shown)

Abstract: The LHCb detector at the Large Hadron Collider has been upgraded to acquire an unprecedented 32 Tbps of particle-collision data to provide new insights in the High Energy Physics domain. The data produced by the detector is filtered in real-time to select interesting collisions. As part of the upgrade, a pre-filtering stage has been removed leading to a factor 40 increase in data rate. To deal wit… ▽ More The LHCb detector at the Large Hadron Collider has been upgraded to acquire an unprecedented 32 Tbps of particle-collision data to provide new insights in the High Energy Physics domain. The data produced by the detector is filtered in real-time to select interesting collisions. As part of the upgrade, a pre-filtering stage has been removed leading to a factor 40 increase in data rate. To deal with the high throughput demands of LHCb real-time data processing, we present an off-the-shelf network architecture using zero-copy techniques in conjunction with an efficient, fully-GPU-based filter. Our converged architecture is able to process the full 32 Tbps of particle-collision data in real-time, the highest in any physics experiment to date. Our result extends the reach of the LHCb physics programme and sets a new standard for real-time data processing at particle physics experiments. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations

Authors: Naaz Sibia, Jessica Wen, Amber Richardson, Yashika Jain, Khushi Malik, Bogdan Simion, Carolina Nobre, Angela Zavaleta Bernuy, Andrew Petersen, Michael Liut

Abstract: Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) offers cognitive design principles for coordinating views, but less is known about what shapes learners' engagement with available representations. We conducted a within-subjects study with 19 undergraduates… ▽ More Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) offers cognitive design principles for coordinating views, but less is known about what shapes learners' engagement with available representations. We conducted a within-subjects study with 19 undergraduates who had completed CS1 and CS2. Students completed think-aloud tasks, reflective interviews, and webcam-based gaze tracking while using a multi-representational probe with synchronized code, memory, and metaphor views, and Python Tutor, across scope, while loops, and linked lists. Gaze analysis showed that students spent nearly half their time focused on code despite available visual scaffolds. Students without prior experience anchored even more heavily in code and engaged minimally with metaphor views. Interviews identified three factors shaping selective engagement: agency, as students sought control over cognitive effort rather than simply having it reduced; representational fit, as identical designs differed in whether they felt helpful or overwhelming; and legitimacy, as some students avoided metaphorical scaffolds they perceived as childish or insufficiently rigorous for university-level work. These findings suggest that multi-representational tools in computing education require attention to affective and social factors alongside cognitive design. Practical considerations include positioning visualizations as verification instruments, offering toggleable abstraction levels, and framing tools to signal disciplinary legitimacy. More broadly, the themes help explain why cognitively sound visualization tools may fail to engage the students they are designed to help. △ Less

Submitted 17 June, 2026; originally announced June 2026.

Comments: Pre-Print of a paper to be published at the International Computing Education Research (ICER) conference 2026

BuyTheBy: A dataset of 18,710 text-based paper mill advertisements with 51,812 timestamped prices

Authors: Reese AK Richardson, Spencer S Hong, Anna Abalkina

Abstract: The study of paper mills and similar businesses operating in the market for academic and education fraud services is frustrated by the lack of market price data on their various offerings. Here, we assemble BuyTheBy, a large, annotated dataset of timestamped, text-based paper mill advertisements from seven businesses operating out of seven different countries. The dataset consists of 18,710 indivi… ▽ More The study of paper mills and similar businesses operating in the market for academic and education fraud services is frustrated by the lack of market price data on their various offerings. Here, we assemble BuyTheBy, a large, annotated dataset of timestamped, text-based paper mill advertisements from seven businesses operating out of seven different countries. The dataset consists of 18,710 individual advertisements, of which 15,839 have prices listed. Among these there are 20,598 positions listed as for sale on 5,567 unique products in 14 different product categories with 51,812 timestamped price data points. We perform elementary analysis of this dataset to demonstrate its utility for quantitative understanding of markets for academic fraud services and suggest future use cases. △ Less

Submitted 21 April, 2026; originally announced April 2026.

Comments: 23 pages, 18 figures. The BuyTheBy dataset is available at https://doi.org/10.5281/zenodo.19684277

Adding internal audio sensing to internal vision enables human-like in-hand fabric recognition with soft robotic fingertips

Authors: Iris Andrussow, Jans Solano, Benjamin A. Richardson, Georg Martius, Katherine J. Kuchenbecker

Abstract: Distinguishing the feel of smooth silk from coarse cotton is a trivial everyday task for humans. When exploring such fabrics, fingertip skin senses both spatio-temporal force patterns and texture-induced vibrations that are integrated to form a haptic representation of the explored material. It is challenging to reproduce this rich, dynamic perceptual capability in robots because tactile sensors t… ▽ More Distinguishing the feel of smooth silk from coarse cotton is a trivial everyday task for humans. When exploring such fabrics, fingertip skin senses both spatio-temporal force patterns and texture-induced vibrations that are integrated to form a haptic representation of the explored material. It is challenging to reproduce this rich, dynamic perceptual capability in robots because tactile sensors typically cannot achieve both high spatial resolution and high temporal sampling rate. In this work, we present a system that can sense both types of haptic information, and we investigate how each type influences robotic tactile perception of fabrics. Our robotic hand's middle finger and thumb each feature a soft tactile sensor: one is the open-source Minsight sensor that uses an internal camera to measure fingertip deformation and force at 50 Hz, and the other is our new sensor Minsound that captures vibrations through an internal MEMS microphone with a bandwidth from 50 Hz to 15 kHz. Inspired by the movements humans make to evaluate fabrics, our robot actively encloses and rubs folded fabric samples between its two sensitive fingers. Our results test the influence of each sensing modality on overall classification performance, showing high utility for the audio-based sensor. Our transformer-based method achieves a maximum fabric classification accuracy of 97 % on a dataset of 20 common fabrics. Incorporating an external microphone away from Minsound increases our method's robustness in loud ambient noise conditions. To show that this audio-visual tactile sensing approach generalizes beyond the training data, we learn general representations of fabric stretchiness, thickness, and roughness. △ Less

Submitted 13 February, 2026; originally announced February 2026.

Journal ref: 2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids)

Estimation of Kinematic Motion from Dashcam Footage

Authors: Evelyn Zhang, Alex Richardson, Jonathan Sprinkle

Abstract: The goal of this paper is to explore the accuracy of dashcam footage to predict the actual kinematic motion of a car-like vehicle. Our approach uses ground truth information from the vehicle's on-board data stream, through the controller area network, and a time-synchronized dashboard camera, mounted to a consumer-grade vehicle, for 18 hours of footage and driving. The contributions of the paper i… ▽ More The goal of this paper is to explore the accuracy of dashcam footage to predict the actual kinematic motion of a car-like vehicle. Our approach uses ground truth information from the vehicle's on-board data stream, through the controller area network, and a time-synchronized dashboard camera, mounted to a consumer-grade vehicle, for 18 hours of footage and driving. The contributions of the paper include neural network models that allow us to quantify the accuracy of predicting the vehicle speed and yaw, as well as the presence of a lead vehicle, and its relative distance and speed. In addition, the paper describes how other researchers can gather their own data to perform similar experiments, using open-source tools and off-the-shelf technology. △ Less

Submitted 30 November, 2025; originally announced December 2025.

Comments: 8 pages, 10 figures

Incorporating Ephemeral Traffic Waves in A Data-Driven Framework for Microsimulation in CARLA

Authors: Alex Richardson, Azhar Hasan, Gabor Karsai, Jonathan Sprinkle

Abstract: This paper introduces a data-driven traffic microsimulation framework in CARLA that reconstructs real-world wave dynamics using high-fidelity time-space data from the I-24 MOTION testbed. Calibration of road networks in microsimulators to reproduce ephemeral phenomena such as traffic waves for large-scale simulation is a process that is fraught with challenges. This work reconsiders the existence… ▽ More This paper introduces a data-driven traffic microsimulation framework in CARLA that reconstructs real-world wave dynamics using high-fidelity time-space data from the I-24 MOTION testbed. Calibration of road networks in microsimulators to reproduce ephemeral phenomena such as traffic waves for large-scale simulation is a process that is fraught with challenges. This work reconsiders the existence of the traffic state data as boundary conditions on an ego vehicle moving through previously recorded traffic data, rather than reproducing those traffic phenomena in a calibrated microsim. Our approach is to autogenerate a 1 mile highway segment corresponding to I-24, and use the I-24 data to power a cosimulation module that injects traffic information into the simulation. The CARLA and cosimulation simulations are centered around an ego vehicle sampled from the empirical data, with autogeneration of "visible" traffic within the longitudinal range of the ego vehicle. Boundary control beyond these visible ranges is achieved using ghost cells behind (upstream) and ahead (downstream) of the ego vehicle. Unlike prior simulation work that focuses on local car-following behavior or abstract geometries, our framework targets full time-space diagram fidelity as the validation objective. Leveraging CARLA's rich sensor suite and configurable vehicle dynamics, we simulate wave formation and dissipation in both low-congestion and high-congestion scenarios for qualitative analysis. The resulting emergent behavior closely mirrors that of real traffic, providing a novel cosimulation framework for evaluating traffic control strategies, perception-driven autonomy, and future deployment of wave mitigation solutions. Our work bridges microscopic modeling with physical experimental data, enabling the first perceptually realistic, boundary-driven simulation of empirical traffic wave phenomena in CARLA. △ Less

Submitted 28 November, 2025; originally announced November 2025.

Comments: Submitted to IEEE IV 2026

OpenTwinMap: An Open-Source Digital Twin Generator for Urban Autonomous Driving

Authors: Alex Richardson, Jonathan Sprinkle

Abstract: Digital twins of urban environments play a critical role in advancing autonomous vehicle (AV) research by enabling simulation, validation, and integration with emerging generative world models. While existing tools have demonstrated value, many publicly available solutions are tightly coupled to specific simulators, difficult to extend, or introduce significant technical overhead. For example, CAR… ▽ More Digital twins of urban environments play a critical role in advancing autonomous vehicle (AV) research by enabling simulation, validation, and integration with emerging generative world models. While existing tools have demonstrated value, many publicly available solutions are tightly coupled to specific simulators, difficult to extend, or introduce significant technical overhead. For example, CARLA-the most widely used open-source AV simulator-provides a digital twin framework implemented entirely as an Unreal Engine C++ plugin, limiting flexibility and rapid prototyping. In this work, we propose OpenTwinMap, an open-source, Python-based framework for generating high-fidelity 3D urban digital twins. The completed framework will ingest LiDAR scans and OpenStreetMap (OSM) data to produce semantically segmented static environment assets, including road networks, terrain, and urban structures, which can be exported into Unreal Engine for AV simulation. OpenTwinMap emphasizes extensibility and parallelization, lowering the barrier for researchers to adapt and scale the pipeline to diverse urban contexts. We describe the current capabilities of the OpenTwinMap, which includes preprocessing of OSM and LiDAR data, basic road mesh and terrain generation, and preliminary support for CARLA integration. △ Less

Submitted 26 November, 2025; originally announced November 2025.

Through the Grapevine: Vineyard Distance as a Measure of Topological Dissimilarity

Authors: Alvan Arulandu, Daniel Gottschalk, Thomas Payne, Alexander Richardson, Thomas Weighill

Abstract: We introduce a new measure of distance between datasets, based on vineyards from topological data analysis, which we call the vineyard distance. Vineyard distance measures the extent of topological change along an interpolation from one dataset to another, either along a pre-computed trajectory or via a straight-line homotopy. We demonstrate through theoretical results and experiments that vineyar… ▽ More We introduce a new measure of distance between datasets, based on vineyards from topological data analysis, which we call the vineyard distance. Vineyard distance measures the extent of topological change along an interpolation from one dataset to another, either along a pre-computed trajectory or via a straight-line homotopy. We demonstrate through theoretical results and experiments that vineyard distance is less sensitive than $L^p$ distance (which considers every single data value), but more sensitive than Wasserstein distance between persistence diagrams (which accounts only for shape and not location). This allows vineyard distance to reveal distinctions that the other two distance measures cannot. In our paper, we establish theoretical results for vineyard distance including as upper and lower bounds. We then demonstrate the usefulness of vineyard distance on real-world data through applications to geospatial data and to neural network training dynamics. △ Less

Submitted 22 January, 2026; v1 submitted 28 October, 2025; originally announced October 2025.

MSC Class: 55N31

WALLABY Pilot Survey: Characterizing Low Rotation Kinematically Modelled Galaxies

Authors: N. Deg, K. Spekkens, N. Arora, R. Dudley, H. White, A. Helias, J. English, T. O'Beirne, V. Kilborn, G. Ferrand, M. L. A. Richardson, B. Catinella, L. Cortese, H. Dénes, A. Elagali, B. -Q. For, K. Lee-Waddell, J. Rhee, L. Shao, A. X. Shen, L. Staveley-Smith, T. Westmeier, O. I. Wong

Abstract: Many of the tensions in cosmological models of the Universe lie in the low mass, low velocity regime. Probing this regime requires a statistically significant sample of galaxies with well measured kinematics and robustly measured uncertainties. WALLABY, as a wide area, untargetted HI survey is well positioned to construct this sample. As a first step towards this goal we develop a framework for te… ▽ More Many of the tensions in cosmological models of the Universe lie in the low mass, low velocity regime. Probing this regime requires a statistically significant sample of galaxies with well measured kinematics and robustly measured uncertainties. WALLABY, as a wide area, untargetted HI survey is well positioned to construct this sample. As a first step towards this goal we develop a framework for testing kinematic modelling codes in the low resolution, low $S/N$, low rotation velocity regime. We find that the WALLABY Kinematic Analysis Proto-Pipeline (WKAPP) is remarkably successful at modelling these galaxies when compared to other algorithms, but, even in idealized tests, there are a significant fraction of false positives found below inclinations of $\approx 40^{\circ}$. We further examine the 11 detections with rotation velocities below $50~\kms$ in the WALLABY pilot data releases. We find that those galaxies with inclinations above $40^{\circ}$ lie within $1-2~σ$ of structural scaling relations that require reliable rotation velocity measurements, such as the baryonic Tully Fisher relation. Moreover, the subset that have consistent kinematic and photometric inclinations tend to lie nearer to the relations than those that have inconsistent inclination measures. This work both demonstrates the challenges faced in low-velocity kinematic modelling, and provides a framework for testing modelling codes as well as constructing a large sample of well measured low rotation models from untargetted surveys. △ Less

Submitted 2 October, 2025; originally announced October 2025.

Comments: 28 pages, 13 figures, accepted to ApJ

ISyHand: A Dexterous Multi-finger Robot Hand with an Articulated Palm

Authors: Benjamin A. Richardson, Felix Grüninger, Lukas Mack, Joerg Stueckler, Katherine J. Kuchenbecker

Abstract: The rapid increase in the development of humanoid robots and customized manufacturing solutions has brought dexterous manipulation to the forefront of modern robotics. Over the past decade, several expensive dexterous hands have come to market, but advances in hardware design, particularly in servo motors and 3D printing, have recently facilitated an explosion of cheaper open-source hands. Most ha… ▽ More The rapid increase in the development of humanoid robots and customized manufacturing solutions has brought dexterous manipulation to the forefront of modern robotics. Over the past decade, several expensive dexterous hands have come to market, but advances in hardware design, particularly in servo motors and 3D printing, have recently facilitated an explosion of cheaper open-source hands. Most hands are anthropomorphic to allow use of standard human tools, and attempts to increase dexterity often sacrifice anthropomorphism. We introduce the open-source ISyHand (pronounced easy-hand), a highly dexterous, low-cost, easy-to-manufacture, on-joint servo-driven robot hand. Our hand uses off-the-shelf Dynamixel motors, fasteners, and 3D-printed parts, can be assembled within four hours, and has a total material cost of about 1,300 USD. The ISyHands's unique articulated-palm design increases overall dexterity with only a modest sacrifice in anthropomorphism. To demonstrate the utility of the articulated palm, we use reinforcement learning in simulation to train the hand to perform a classical in-hand manipulation task: cube reorientation. Our novel, systematic experiments show that the simulated ISyHand outperforms the two most comparable hands in early training phases, that all three perform similarly well after policy convergence, and that the ISyHand significantly outperforms a fixed-palm version of its own design. Additionally, we deploy a policy trained on cube reorientation on the real hand, demonstrating its ability to perform real-world dexterous manipulation. △ Less

Submitted 30 September, 2025; originally announced September 2025.

Comments: Accepted at IEEE Humanoids 2025

The DNA of nuclear models: How AI predicts nuclear masses

Authors: Kate A. Richardson, Sokratis Trifinopoulos, Mike Williams

Abstract: Obtaining high-precision predictions of nuclear masses, or equivalently nuclear binding energies, $E_b$, remains an important goal in nuclear-physics research. Recently, many AI-based tools have shown promising results on this task, some achieving precision that surpasses the best physics models. However, the utility of these AI models remains in question given that predictions are only useful whe… ▽ More Obtaining high-precision predictions of nuclear masses, or equivalently nuclear binding energies, $E_b$, remains an important goal in nuclear-physics research. Recently, many AI-based tools have shown promising results on this task, some achieving precision that surpasses the best physics models. However, the utility of these AI models remains in question given that predictions are only useful where measurements do not exist, which inherently requires extrapolation away from the training (and testing) samples. Since AI models are largely black boxes, the reliability of such an extrapolation is difficult to assess. We present an AI model that not only achieves cutting-edge precision for $E_b$, but does so in an interpretable manner. For example, we find that (and explain why) the most important dimensions of its internal representation form a double helix, where the analog of the hydrogen bonds in DNA here link the number of protons and neutrons found in the most stable nucleus of each isotopic chain. Furthermore, we show that the AI prediction of $E_b$ can be factorized and ordered hierarchically, with the most important terms corresponding to well-known symbolic models (such as the famous liquid drop). Remarkably, the improvement of the AI model over symbolic ones can almost entirely be attributed to an observation made by Jaffe in 1969 based on the structure of most known nuclear ground states. The end result is a fully interpretable data-driven model of nuclear masses based on physics deduced by AI. △ Less

Submitted 30 September, 2025; v1 submitted 11 August, 2025; originally announced August 2025.

Comments: 19 pages, 11 figures

Report number: CERN-TH-2025-153

How Sovereign Is Sovereign Compute? A Review of 775 Non-U.S. Data Centers

Authors: Aris Richardson, Haley Yi, Michelle Nie, Simon Wisdom, Casey Price, Ruben Weijers, Steven Veld, Mauricio Baker

Abstract: Previous literature has proposed that the companies operating data centers enforce government regulations on AI companies. Using a new dataset of 775 non-U.S. data center projects, this paper estimates how often data centers could be subject to foreign legal authorities due to the nationality of the data center operators. We find that U.S. companies operate 48% of all non-U.S. data center projects… ▽ More Previous literature has proposed that the companies operating data centers enforce government regulations on AI companies. Using a new dataset of 775 non-U.S. data center projects, this paper estimates how often data centers could be subject to foreign legal authorities due to the nationality of the data center operators. We find that U.S. companies operate 48% of all non-U.S. data center projects in our dataset when weighted by investment value - a proxy for compute capacity. This is an approximation based on public data and should be interpreted as an initial estimate. For the United States, our findings suggest that data center operators offer a lever for internationally governing AI that complements traditional export controls, since operators can be used to regulate computing resources already deployed in non-U.S. data centers. For other countries, our results show that building data centers locally does not guarantee digital sovereignty if those facilities are run by foreign entities. To support future research, we release our dataset, which documents over 20 variables relating to each data center, including the year it was announced, the investment value, and its operator's national affiliation. The dataset also includes over 1,000 quotes describing these data centers' strategic motivations, operational challenges, and engagement with U.S. and Chinese entities. △ Less

Submitted 3 May, 2026; v1 submitted 30 July, 2025; originally announced August 2025.

Comments: For associated dataset, see https://github.com/alarichardson/non-us-data-center-registry

Theory-Grounded Evaluation of Human-Like Fallacy Patterns in LLM Reasoning

Authors: Andrew Keenan Richardson, Ryan Othniel Kearns, Sean Moss, Vincent Wang-Mascianica, Philipp Koralus

Abstract: We study logical reasoning in language models by asking whether their errors follow established human fallacy patterns. Using the Erotetic Theory of Reasoning (ETR) and its open-source implementation, PyETR, we programmatically generate 383 formally specified reasoning problems and evaluate 38 models. For each response, we judge logical correctness and, when incorrect, whether it matches an ETR-pr… ▽ More We study logical reasoning in language models by asking whether their errors follow established human fallacy patterns. Using the Erotetic Theory of Reasoning (ETR) and its open-source implementation, PyETR, we programmatically generate 383 formally specified reasoning problems and evaluate 38 models. For each response, we judge logical correctness and, when incorrect, whether it matches an ETR-predicted fallacy. Two results stand out: (i) as a capability proxy (Chatbot Arena Elo) increases, a larger share of a model's incorrect answers are ETR-predicted fallacies $(ρ=0.360, p=0.0265)$, while overall correctness on this dataset shows no correlation with capability; (ii) reversing premise order significantly reduces fallacy production for many models, mirroring human order effects. Methodologically, PyETR provides an open-source pipeline for unbounded, synthetic, contamination-resistant reasoning tests linked to a cognitive theory, enabling analyses that focus on error composition rather than error rate. △ Less

Submitted 20 March, 2026; v1 submitted 10 June, 2025; originally announced June 2025.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis

Authors: Anna Seo Gyeong Choi, Alexander Richardson, Ryan Partlan, Sunny Tang, Sunghye Cho

Abstract: This study compares three acoustic feature extraction toolkits (OpenSMILE, Praat, and Librosa) applied to clinical speech data from individuals with schizophrenia spectrum disorders (SSD) and healthy controls (HC). By standardizing extraction parameters across the toolkits, we analyzed speech samples from 77 SSD and 87 HC participants and found significant toolkit-dependent variations. While F0 pe… ▽ More This study compares three acoustic feature extraction toolkits (OpenSMILE, Praat, and Librosa) applied to clinical speech data from individuals with schizophrenia spectrum disorders (SSD) and healthy controls (HC). By standardizing extraction parameters across the toolkits, we analyzed speech samples from 77 SSD and 87 HC participants and found significant toolkit-dependent variations. While F0 percentiles showed high cross-toolkit correlation (r=0.962 to 0.999), measures like F0 standard deviation and formant values often had poor, even negative, agreement. Additionally, correlation patterns differed between SSD and HC groups. Classification analysis identified F0 mean, HNR, and MFCC1 (AUC greater than 0.70) as promising discriminators. These findings underscore reproducibility concerns and advocate for standardized protocols, multi-toolkit cross-validation, and transparent reporting. △ Less

Submitted 17 August, 2025; v1 submitted 1 June, 2025; originally announced June 2025.

Comments: Accepted to Interspeech 2025

Promising Topics for U.S.-China Dialogues on AI Risks and Governance

Authors: Saad Siddiqui, Lujain Ibrahim, Kristy Loke, Stephen Clare, Marianne Lu, Aris Richardson, Conor McGlynn, Jeffrey Ding

Abstract: Cooperation between the United States and China, the world's leading artificial intelligence (AI) powers, is crucial for effective global AI governance and responsible AI development. Although geopolitical tensions have emphasized areas of conflict, in this work, we identify potential common ground for productive dialogue by conducting a systematic analysis of more than 40 primary AI policy and co… ▽ More Cooperation between the United States and China, the world's leading artificial intelligence (AI) powers, is crucial for effective global AI governance and responsible AI development. Although geopolitical tensions have emphasized areas of conflict, in this work, we identify potential common ground for productive dialogue by conducting a systematic analysis of more than 40 primary AI policy and corporate governance documents from both nations. Specifically, using an adapted version of the AI Governance and Regulatory Archive (AGORA) - a comprehensive repository of global AI governance documents - we analyze these materials in their original languages to identify areas of convergence in (1) sociotechnical risk perception and (2) governance approaches. We find strong and moderate overlap in several areas such as on concerns about algorithmic transparency, system reliability, agreement on the importance of inclusive multi-stakeholder engagement, and AI's role in enhancing safety. These findings suggest that despite strategic competition, there exist concrete opportunities for bilateral U.S.-China cooperation in the development of responsible AI. Thus, we present recommendations for furthering diplomatic dialogues that can facilitate such cooperation. Our analysis contributes to understanding how different international governance frameworks might be harmonized to promote global responsible AI development. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing

Authors: Lukas Mack, Felix Grüninger, Benjamin A. Richardson, Regine Lendway, Katherine J. Kuchenbecker, Joerg Stueckler

Abstract: Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and proprioception with binary, low-resolution tactile contact measurements from across the interior surface… ▽ More Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and proprioception with binary, low-resolution tactile contact measurements from across the interior surface of an articulated robotic hand can mitigate this issue. The visuo-tactile object-pose-estimation problem is formulated probabilistically in a factor graph. The pose of the object is optimized to align with the three kinds of measurements using a robust cost function to reduce the influence of visual or tactile outlier readings. The advantages of the proposed approach are first demonstrated in simulation: a custom 15-DoF robot hand with one binary tactile sensor per link grasps 17 YCB objects while observed by an RGB-D camera. This low-resolution in-hand tactile sensing significantly improves object-pose estimates under high occlusion and also high visual noise. We also show these benefits through grasping tests with a preliminary real version of our tactile hand, obtaining reasonable visuo-tactile estimates of object pose at approximately 13.3 Hz on average. △ Less

Submitted 25 March, 2025; originally announced March 2025.

Comments: Accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA), 2025

Rethinking the Vulnerability of Concept Erasure and a New Method

Authors: Alex D. Richardson, Kaicheng Zhang, Lucas Beerens, Dongdong Chen

Abstract: The proliferation of text-to-image diffusion models has raised significant privacy and security concerns, particularly regarding the generation of copyrighted or harmful images. In response, concept erasure (defense) methods have been developed to "unlearn" specific concepts through post-hoc finetuning. However, recent concept restoration (attack) methods have demonstrated that these supposedly er… ▽ More The proliferation of text-to-image diffusion models has raised significant privacy and security concerns, particularly regarding the generation of copyrighted or harmful images. In response, concept erasure (defense) methods have been developed to "unlearn" specific concepts through post-hoc finetuning. However, recent concept restoration (attack) methods have demonstrated that these supposedly erased concepts can be recovered using adversarially crafted prompts, revealing a critical vulnerability in current defense mechanisms. In this work, we first investigate the fundamental sources of adversarial vulnerability and reveal that vulnerabilities are pervasive in the prompt embedding space of concept-erased models, a characteristic inherited from the original pre-unlearned model. Furthermore, we introduce **RECORD**, a novel coordinate-descent-based restoration algorithm that consistently outperforms existing restoration methods by up to 17.8 times. We conduct extensive experiments to assess its compute-performance tradeoff and propose acceleration strategies. △ Less

Submitted 3 October, 2025; v1 submitted 24 February, 2025; originally announced February 2025.

Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques

Authors: Anthony Richardson, Felix Putze

Abstract: Attribute manipulation deals with the problem of changing individual attributes of a data point or a time series, while leaving all other aspects unaffected. This work focuses on the domain of human motion, more precisely karate movement patterns. To the best of our knowledge, it presents the first success at manipulating attributes of human motion data. One of the key requirements for achieving a… ▽ More Attribute manipulation deals with the problem of changing individual attributes of a data point or a time series, while leaving all other aspects unaffected. This work focuses on the domain of human motion, more precisely karate movement patterns. To the best of our knowledge, it presents the first success at manipulating attributes of human motion data. One of the key requirements for achieving attribute manipulation on human motion is a suitable pose representation. Therefore, we design a novel continuous, rotation-based pose representation that enables the disentanglement of the human skeleton and the motion trajectory, while still allowing an accurate reconstruction of the original anatomy. The core idea of the manipulation approach is to use a transformer encoder for discovering high-level semantics, and a diffusion probabilistic model for modeling the remaining stochastic variations. We show that the embedding space obtained from the transformer encoder is semantically meaningful and linear. This enables the manipulation of high-level attributes, by discovering their linear direction of change in the semantic embedding space and moving the embedding along said direction. All code and data is made publicly available. △ Less

Submitted 29 July, 2025; v1 submitted 30 January, 2025; originally announced January 2025.

Comments: 9 pages, 7 figures

MSC Class: 68T07 (Primary) 68T30; 92C99 (Secondary) ACM Class: I.2.4; I.2.6

WALLABY Pilot Survey & ASymba: Comparing HI Detection Asymmetries to the SIMBA Simulation

Authors: Mathieu Perron-Cormier, Nathan Deg, Kristine Spekkens, Mark L. A. Richardson, Marcin Glowacki, Kyle A. Oman, Marc A. W. Verheijen, Nadine A. N. Hank, Sarah Blyth, Helga Dénes, Jonghwan Rhee, Ahmed Elagali, Austin Xiaofan Shen, Wasim Raja, Karen Lee-Waddell, Luca Cortese, Barbara Catinella, Tobias Westmeier

Abstract: An avenue for understanding cosmological galaxy formation is to compare morphometric parameters in observations and simulations of galaxy assembly. In this second paper of the ASymba: Asymmetries of HI in SIMBA Galaxies series, we measure atomic gas HI asymmetries in spatially-resolved detections from the untargetted WALLABY survey, and compare them to realizations of WALLABY-like mock samples fro… ▽ More An avenue for understanding cosmological galaxy formation is to compare morphometric parameters in observations and simulations of galaxy assembly. In this second paper of the ASymba: Asymmetries of HI in SIMBA Galaxies series, we measure atomic gas HI asymmetries in spatially-resolved detections from the untargetted WALLABY survey, and compare them to realizations of WALLABY-like mock samples from the SIMBA cosmological simulations. We develop a Scanline Tracing method to create mock galaxy HI datacubes which minimizes shot noise along the spectral dimension compared to particle-based methods, and therefore spurious asymmetry contributions. We compute 1D and 3D asymmetries for spatially-resolved WALLABY Pilot Survey detections, and find that the highest 3D asymmetries A3D>0.5 stem from interacting systems or detections with strong bridges or tails. We then construct a series of WALLABY-like mock realizations drawn from the SIMBA 50 Mpc simulation volume, and compare their asymmetry distributions. We find that the incidence of high A3D detections is higher in WALLABY than in the SIMBA mocks, but that difference is not statistically significant (p-value = 0.05). The statistical power of quantitative comparisons of asymmetries such as the one presented here will improve as the WALLABY survey progresses, and as simulation volumes and resolutions increase. △ Less

Submitted 16 January, 2025; originally announced January 2025.

Comments: 17 pages, 11 figures

OpenAI o1 System Card

Authors: OpenAI, :, Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, Alex Iftimie, Alex Karpenko, Alex Tachard Passos, Alexander Neitz, Alexander Prokofiev, Alexander Wei, Allison Tam, Ally Bennett, Ananya Kumar, Andre Saraiva, Andrea Vallone, Andrew Duberstein, Andrew Kondrich , et al. (240 additional authors not shown)

Abstract: The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-ar… ▽ More The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations. △ Less

Submitted 29 April, 2026; v1 submitted 21 December, 2024; originally announced December 2024.

Unravelling and circumventing failure mechanisms in chalcogenide optical phase change materials

Authors: Cosmin Constantin Popescu, Kiumars Aryana, Brian Mills, Tae Woo Lee, Louis Martin-Monier, Luigi Ranno, Jia Xu Brian Sia, Khoi Phuong Dao, Hyung-Bin Bae, Vladimir Liberman, Steven Vitale, Myungkoo Kang, Kathleen A. Richardson, Carlos A. Ríos Ocampo, Dennis Calahan, Yifei Zhang, William M. Humphreys, Hyun Jung Kim, Tian Gu, Juejun Hu

Abstract: Chalcogenide optical phase change materials (PCMs) have garnered significant interest for their growing applications in programmable photonics, optical analog computing, active metasurfaces, and beyond. Limited endurance or cycling lifetime is however increasingly becoming a bottleneck toward their practical deployment for these applications. To address this issue, we performed a systematic study… ▽ More Chalcogenide optical phase change materials (PCMs) have garnered significant interest for their growing applications in programmable photonics, optical analog computing, active metasurfaces, and beyond. Limited endurance or cycling lifetime is however increasingly becoming a bottleneck toward their practical deployment for these applications. To address this issue, we performed a systematic study elucidating the cycling failure mechanisms of Ge$_2$Sb$_2$Se$_4$Te (GSST), a common optical PCM tailored for infrared photonic applications, in an electrothermal switching configuration commensurate with their applications in on-chip photonic devices. We further propose a set of design rules building on insights into the failure mechanisms, and successfully implemented them to boost the endurance of the GSST device to over 67,000 cycles. △ Less

Submitted 18 September, 2024; originally announced September 2024.

Widespread misidentification of SEM instruments in the peer-reviewed materials science and engineering literature

Authors: Reese AK Richardson, Jeonghyun Moon, Spencer S Hong, Luís A Nunes Amaral

Abstract: Removed per arXiv policy. Please see version at https://doi.org/10.31219/osf.io/4wqcr Removed per arXiv policy. Please see version at https://doi.org/10.31219/osf.io/4wqcr △ Less

Submitted 27 August, 2024; originally announced September 2024.

Comments: arXiv admin note: Removed per arXiv policy. Please see version at https://doi.org/10.31219/osf.io/4wqcr

Stop-and-go wave super-resolution reconstruction via iterative refinement

Authors: Junyi Ji, Alex Richardson, Derek Gloudemans, Gergely Zachár, Matthew Nice, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work

Abstract: Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data r… ▽ More Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, I24-WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement enhancement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data enhancement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-sourced our trained model and code to facilitate further research and applications. △ Less

Submitted 20 April, 2025; v1 submitted 1 August, 2024; originally announced August 2024.

Cost-benefit analysis of ecosystem modelling to support fisheries management

Authors: Matthew H. Holden, Eva E. Plagányi, Elizabeth A. Fulton, Alexander B. Campbell, Rachel Janes, Robyn A. Lovett, Montana Wickens, Matthew P. Adams, Larissa Lubiana Botelho, Catherine M. Dichmont, Philip Erm, Kate J Helmstedt, Ryan F. Heneghan, Manuela Mendiolar, Anthony J. Richardson, Jacob G. D. Rogers, Kate Saunders, Liam Timms

Abstract: Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the va… ▽ More Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits. △ Less

Submitted 26 March, 2024; originally announced March 2024.

MSC Class: 92D

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

Authors: Jonathan W. Lee, Han Wang, Kathy Jang, Amaury Hayat, Matthew Bunting, Arwa Alanqary, William Barbour, Zhe Fu, Xiaoqian Gong, George Gunter, Sharon Hornstein, Abdul Rahman Kreidieh, Nathan Lichtlé, Matthew W. Nice, William A. Richardson, Adit Shah, Eugene Vinitsky, Fangyu Wu, Shengquan Xiang, Sulaiman Almatrudi, Fahd Althukair, Rahul Bhadani, Joy Carpio, Raphael Chekroun, Eric Cheng , et al. (39 additional authors not shown)

Abstract: The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experim… ▽ More The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest. △ Less

Submitted 26 February, 2024; originally announced February 2024.

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic

Authors: Han Wang, Zhe Fu, Jonathan Lee, Hossein Nick Zinat Matin, Arwa Alanqary, Daniel Urieli, Sharon Hornstein, Abdul Rahman Kreidieh, Raphael Chekroun, William Barbour, William A. Richardson, Dan Work, Benedetto Piccoli, Benjamin Seibold, Jonathan Sprinkle, Alexandre M. Bayen, Maria Laura Delle Monache

Abstract: This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on th… ▽ More This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow. △ Less

Submitted 26 February, 2024; originally announced February 2024.

Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization

Authors: Adam Richardson, Boi Faltings

Abstract: Peer prediction incentive mechanisms for crowdsourcing are generally limited to eliciting samples from categorical distributions. Prior work on extending peer prediction to arbitrary distributions has largely relied on assumptions on the structures of the distributions or known properties of the data providers. We introduce a novel class of incentive mechanisms that extend peer prediction mechanis… ▽ More Peer prediction incentive mechanisms for crowdsourcing are generally limited to eliciting samples from categorical distributions. Prior work on extending peer prediction to arbitrary distributions has largely relied on assumptions on the structures of the distributions or known properties of the data providers. We introduce a novel class of incentive mechanisms that extend peer prediction mechanisms to arbitrary distributions by replacing the notion of an exact match with a concept of neighborhood matching. We present conditions on the belief updates of the data providers that guarantee incentive-compatibility for rational data providers, and admit a broad class of possible reasonable updates. △ Less

Submitted 19 December, 2023; originally announced December 2023.

Comments: Full paper with technical Appendix to reference from AAAI conference paper

Electrically reconfigurable phase-change transmissive metasurface

Authors: Cosmin Constantin Popescu, Kiumars Aryana, Parth Garud, Khoi Phuong Dao, Steven Vitale, Vladimir Liberman, Hyung-Bin Bae, Tae-Woo Lee, Myungkoo Kang, Kathleen A. Richardson, Carlos A. Rios Ocampo, Yifei Zhang, Tian Gu, Juejun Hu, Hyun Jung Kim

Abstract: Programmable and reconfigurable optics hold significant potential for transforming a broad spectrum of applications, spanning space explorations to biomedical imaging, gas sensing, and optical cloaking. The ability to adjust the optical properties of components like filters, lenses, and beam steering devices could result in dramatic reductions in size, weight, and power consumption in future optoe… ▽ More Programmable and reconfigurable optics hold significant potential for transforming a broad spectrum of applications, spanning space explorations to biomedical imaging, gas sensing, and optical cloaking. The ability to adjust the optical properties of components like filters, lenses, and beam steering devices could result in dramatic reductions in size, weight, and power consumption in future optoelectronic devices. Among the potential candidates for reconfigurable optics, chalcogenide-based phase change materials (PCMs) offer great promise due to their non-volatile and analogue switching characteristics. Although PCM have found widespread use in electronic data storage, these memory devices are deeply sub-micron-sized. To incorporate phase change materials into free-space optical components, it is essential to scale them up to beyond several hundreds of microns while maintaining reliable switching characteristics. This study demonstrated a non-mechanical, non-volatile transmissive filter based on low-loss PCMs with a 200 $μ$m$ \times $200 $μ$m switching area. The device/metafilter can be consistently switched between low- and high-transmission states using electrical pulses with a switching contrast ratio of 5.5 dB. The device was reversibly switched for 1250 cycles before accelerated degradation took place. The work represents an important step toward realizing free-space reconfigurable optics based on PCMs. △ Less

Submitted 16 December, 2023; originally announced December 2023.

Enabling Mixed Autonomy Traffic Control

Authors: Matthew Nice, Matt Bunting, Alex Richardson, Gergely Zachar, Jonathan W. Lee, Alexandre Bayen, Maria Laura Delle Monache, Benjamin Seibold, Benedetto Piccoli, Jonathan Sprinkle, Dan Work

Abstract: We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given… ▽ More We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable and affordable platform for experimental control. This paper introduces an extensible open-source hardware and software platform, enabling a team of 100 vehicles to execute several different vehicular control algorithms as a collaborative fleet, composed of three different makes and models, which drove 22752 miles in a combined 1022 hours, over 5 days in Nashville, TN in November 2022. △ Less

Submitted 28 October, 2023; originally announced October 2023.

Learning spatio-temporal patterns with Neural Cellular Automata

Authors: Alex D. Richardson, Tibor Antal, Richard A. Blythe, Linus J. Schumacher

Abstract: Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We exte… ▽ More Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation. △ Less

Submitted 22 April, 2024; v1 submitted 23 October, 2023; originally announced October 2023.

Comments: For videos referenced in appendix, see: https://github.com/AlexDR1998/NCA/tree/main/Videos

A real-time, scalable, fast and highly resource efficient decoder for a quantum computer

Authors: Ben Barber, Kenton M. Barnes, Tomasz Bialas, Okan Buğdaycı, Earl T. Campbell, Neil I. Gillespie, Kauser Johar, Ram Rajan, Adam W. Richardson, Luka Skoric, Canberk Topal, Mark L. Turner, Abbas B. Ziad

Abstract: To unleash the potential of quantum computers, noise effects on qubits' performance must be carefully managed. The decoders responsible for diagnosing noise-induced computational errors must use resources efficiently to enable scaling to large qubit counts and cryogenic operation. Additionally, they must operate at speed, to avoid an exponential slowdown in the logical clock rate of the quantum co… ▽ More To unleash the potential of quantum computers, noise effects on qubits' performance must be carefully managed. The decoders responsible for diagnosing noise-induced computational errors must use resources efficiently to enable scaling to large qubit counts and cryogenic operation. Additionally, they must operate at speed, to avoid an exponential slowdown in the logical clock rate of the quantum computer. To overcome such challenges, we introduce the Collision Clustering decoder and implement it on FPGA and ASIC hardware. We simulate logical memory experiments using the leading quantum error correction scheme, the surface code, and demonstrate MHz decoding speed - matching the requirements of fast-operating modalities such as superconducting qubits - up to an 881 and 1057 qubits surface code with the FPGA and ASIC, respectively. The ASIC design occupies 0.06 mm$^2$ and consumes only 8 mW of power. Our decoder is both highly performant and resource efficient, unlocking a viable path to practically realising fault-tolerant quantum computers. △ Less

Submitted 24 September, 2024; v1 submitted 11 September, 2023; originally announced September 2023.

Comments: 13 pages, 7 figures

Journal ref: Nature Electronics (2025)

Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineation

Authors: Ashlin Richardson, Donald Leckie

Abstract: A clustering termination procedure which is locally adaptive (with respect to the hierarchical tree of sets representative of the agglomerative merging) is proposed, for agglomerative hierarchical clustering on a set equipped with a distance function. It represents a multi-scale alternative to conventional scale dependent threshold based termination criteria. A clustering termination procedure which is locally adaptive (with respect to the hierarchical tree of sets representative of the agglomerative merging) is proposed, for agglomerative hierarchical clustering on a set equipped with a distance function. It represents a multi-scale alternative to conventional scale dependent threshold based termination criteria. △ Less

Submitted 1 December, 2022; originally announced December 2022.

Comments: 8 pages, 14 figures

MSC Class: 62H30 (Primary) ACM Class: I.5.3; I.5.4

Rapid, remote and low-cost finger vasculature mapping for heart rate monitoring

Authors: Akhil Kallepalli, David B James, Mark A Richardson

Abstract: Today's diagnostics include devices such as pulse oximeters, blood pressure monitors, and temperature measurements. These devices provide vital information to medical personnel when making treatment decisions. Drawing inspiration from the fundamental utility of pulse oximeters, we present a methodology for a robust low-cost approach to imaging subsurface vasculature and monitoring heart rate. The… ▽ More Today's diagnostics include devices such as pulse oximeters, blood pressure monitors, and temperature measurements. These devices provide vital information to medical personnel when making treatment decisions. Drawing inspiration from the fundamental utility of pulse oximeters, we present a methodology for a robust low-cost approach to imaging subsurface vasculature and monitoring heart rate. The approach uses off-the-shelf equipment, set up in free space without physical contact and exploits the nature of the interaction between light at near-infrared wavelengths with tissue. Image processing algorithms extract heart rate information from the snapshot and video sequence captured at a stand-off distance. The method can be applied in a room with ambient light and remains robust to scenarios comparable to medical situations. This research sets the platform for future diagnostic devices based on imaging systems and algorithms for non-contact point-of-care investigations. △ Less

Submitted 15 August, 2022; originally announced August 2022.

Comments: 9 pages, 7 figures

A New Path to Nanoscale Cellular Analysis with Monochromated Electron Energy-Loss Spectroscopy

Authors: Jordan A. Hachtel, Jacek Jakowski, Jingsong Huang, Santa Jansone-Popova, Ilja Popovs, Elizabeth A. Richardson, Barbara R. Evans, Peter Rez, Eric V. Formo

Abstract: High-spatial-resolution vibrational spectroscopy is one of the principal techniques for nanoscale compositional analysis in biological materials. Here, we present a new method for the analysis of whole-cell biological specimens through nanoscale vibrational electron energy-loss spectroscopy (EELS) in the monochromated scanning transmission electron microscope. Using the combined spatial and spectr… ▽ More High-spatial-resolution vibrational spectroscopy is one of the principal techniques for nanoscale compositional analysis in biological materials. Here, we present a new method for the analysis of whole-cell biological specimens through nanoscale vibrational electron energy-loss spectroscopy (EELS) in the monochromated scanning transmission electron microscope. Using the combined spatial and spectral resolution of the technique, we examine the vascular system of a cucumber stem and identify clear physical and vibrational signatures from the different cellular regions with high spatial resolution. Furthermore, using first-principles calculations combined with optical and EELS spectroscopy on the individual components that make up the cucumber stem, we unravel the physical mechanisms of the vibrational signatures and directly assign compositional origins to the cell walls and bodies of different cellular regions. These results demonstrate that monochromated electron energy-loss spectroscopy is a promising technique for nanoscale spatial mapping of the chemical composition of biological materials. △ Less

Submitted 8 June, 2022; originally announced June 2022.

Simulating a pulsed power-driven plasma with ideal MHD

Authors: A. Beresnyak, A. L. Velikovich, J. L. Giuliani, S. L. Jackson, J. T. Engelbrecht, A. S. Richardson, A. Dasgupta

Abstract: We describe a simple practical numerical method for simulating plasma driven within a vacuum chamber by a pulsed power generator. Typically, in this type of simulation, the vacuum region adjacent to the plasma is approximated as a highly resistive, light fluid; this involves computationally expensive solvers describing the diffusion of the magnetic field through this fluid. Instead, we provide a r… ▽ More We describe a simple practical numerical method for simulating plasma driven within a vacuum chamber by a pulsed power generator. Typically, in this type of simulation, the vacuum region adjacent to the plasma is approximated as a highly resistive, light fluid; this involves computationally expensive solvers describing the diffusion of the magnetic field through this fluid. Instead, we provide a recipe for coupling pulsed power generators to the MHD domain by approximating the perfectly insulating vacuum as a light, perfectly conducting, inviscid MHD fluid and discuss the applicability of this counter-intuitive technique. This, much more affordable ideal MHD representation, is particularly useful in situations where a plasma exhibits interesting three-dimensional phenomena, either due to the design of the experiment or due to developing instabilities. We verified that this coupling recipe works by modeling an exactly solvable flux compression generator as well as a self-similar Noh-like solution and demonstrated convergence to the theoretical solution. We also showed examples of simulating complex three-dimensional pulsed power devices with this technique. We release our code implementation to the public. △ Less

Submitted 6 May, 2022; originally announced May 2022.

Comments: 10 pages, 10 figures, accepted to PoP

The Effect of Multiple Imputation of Routine Pathology Variables on Laboratory Diagnosis of Hepatitis C Infection

Authors: N. Menon, B. A. Lidbury, A. M. Richardson

Abstract: Pathology tests are central to modern healthcare in terms of diagnosis and patient management. Aggregated pathology results provide opportunities for research into fundamental and applied questions in health and medicine, but data analytic challenges appear since test profiles vary between medical practitioners, resulting in missing data. In this study we provide an analytical investigation of the… ▽ More Pathology tests are central to modern healthcare in terms of diagnosis and patient management. Aggregated pathology results provide opportunities for research into fundamental and applied questions in health and medicine, but data analytic challenges appear since test profiles vary between medical practitioners, resulting in missing data. In this study we provide an analytical investigation of the laboratory diagnosis of Hepatitis C (HCV) infection and focus on how to maximize the predictive value of routine pathology data. We recommend using the Influx - Outflux measures to help construct the imputation model when using multiple imputation. Data from 14,320 community-patients aged 15 - 100 years were accessed via ACT Pathology (The Canberra Hospital, Australia). Influx and Outflux were calculated to identify which variables were potentially powerful predictors of missing values. Available Case analysis and Multiple Imputation were used to accommodate missing values in the dataset. Logistic regression model and stepwise selection method were used for analysing the imputed datasets. The predictive power of all methods was compared. The predictive power of the models on multiply imputed data was similar to the power of the models based on complete data. The advantage of multiply imputed data was that it allowed for the inclusion of all the completed variables in the logistic models, thus identifying a broader selection of test results that could lead to the enhanced laboratory prediction of HCV. Multiple imputation is an important statistical resource allowing all individuals in a study to contribute whatever data they have supplied to the analysis. MI in combination with the values of Influx and Outflux identifies potential predictors of HepC infection. Variables age, gender and alanine aminotransferase have been shown to be strong laboratory predictors of HCV infection. △ Less

Submitted 3 May, 2022; originally announced May 2022.

Comments: 26 pages, 5 figures, 5 tables

MSC Class: 62P10

Electrical Programmable Multi-Level Non-volatile Photonic Random-Access Memory

Authors: Jiawei Meng, Yaliang Gui, Behrouz Movahhed Nouri, Gelu Comanescu, Xiaoxuan Ma, Yifei Zhang, Cosmin-Constantin Popescu, Myungkoo Kang, Mario Miscuglio, Nicola Peserico, Kathleen A. Richardson, Juejun Hu, Hamed Dalir, Volker J. Sorger

Abstract: Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumptio… ▽ More Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multi-state electrically-programmed low-loss non-volatile photonic memory based on a broadband transparent phase change material (Ge2Sb2Se5, GSSe) with ultra-low absorption in the amorphous state. A zero-static-power and electrically-programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/μm and an ultra-low insertion loss of total 0.12 dB for a 4-bit memory showing a 100x improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual micro-heaters validating performance tradeoffs. Experimentally we demonstrate a half-a million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example. △ Less

Submitted 21 June, 2022; v1 submitted 24 March, 2022; originally announced March 2022.

Textual analysis of clinical notes on pathology request forms to determine sensitivity and specificity of Hepatitis B and C virus infection status

Authors: Eric H. Kim, Brett A. Lidbury, Alice M. Richardson

Abstract: Background: It is not established whether clinical notes provided on pathology request forms are useful as decision support data when assessing Hepatitis B and C viral infection status. Objective: To determine sensitivity, specificity, and predictive value of clinical notes for identifying infection status of Hepatitis B and C. Methods: The study comprises 179 cases and 166 cases tested for HBsAg… ▽ More Background: It is not established whether clinical notes provided on pathology request forms are useful as decision support data when assessing Hepatitis B and C viral infection status. Objective: To determine sensitivity, specificity, and predictive value of clinical notes for identifying infection status of Hepatitis B and C. Methods: The study comprises 179 cases and 166 cases tested for HBsAg and anti-HCV serological markers, respectively, and accompanied by a written description (clinical note) provided on pathology request forms by the clinician on duty. The clinical note sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were calculated using serological HBsAg and anti-HCV tests as gold standards. Results: The sensitivity and specificity of clinical notes for Hepatitis B infection status were 90 percent and 56 percent, respectively. The sensitivity and specificity of clinical notes for Hepatitis C infection status were 86 percent and 21 percent, respectively. Conclusions: Clinical note information identifies moderate-to-high sensitivity with regards to Hepatitis B and C viral infection status, however, given low specificity in both groups, the clinical note is not favourable for ruling disease in, possibly due to high rate of false positives. △ Less

Submitted 27 February, 2022; originally announced February 2022.

Comments: Word count: 3638; Number of Tables: 5; Number of Figures: 3; Supplementary Material (tables and figures): 3

An ultrasonography-based approach for optical diagnostics and phototherapy treatment strategies

Authors: Akhil Kallepalli, James Halls, David B. James, Mark A. Richardson

Abstract: Currently, diagnostic medicine uses a multitude of tools ranging from ionising radiation to histology analysis. With advances in piezoelectric crystal technology, high-frequency ultrasound imaging has developed to achieve comparatively high resolution without the drawbacks of ionising radiation. This research proposes a low-cost, non-invasive and real-time protocol for informing photo-therapy proc… ▽ More Currently, diagnostic medicine uses a multitude of tools ranging from ionising radiation to histology analysis. With advances in piezoelectric crystal technology, high-frequency ultrasound imaging has developed to achieve comparatively high resolution without the drawbacks of ionising radiation. This research proposes a low-cost, non-invasive and real-time protocol for informing photo-therapy procedures using ultrasound imaging. We combine currently available ultrasound procedures with Monte Carlo methods for assessing light transport and photo-energy deposition in the tissue. The measurements from high-resolution ultrasound scans is used as input for optical simulations. Consequently, this provides a pipeline that will inform medical practitioners for better therapy strategy planning. While validating known inferences of light transport through biological tissue, our results highlight the range of information such as temporal monitoring and energy deposition at varying depths. This process also retains the flexibility of testing various wavelengths for individual-specific geometries and anatomy. △ Less

Submitted 12 January, 2022; originally announced January 2022.

Comments: Article accepted in Journal of Biophotonics with title "An ultrasonography-based approach for tissue modelling to inform phototherapy treatment strategies", status: IN PRESS, DOI not generated

Waveguide-Integrated Mid-Infrared Photodetection using Graphene on a Scalable Chalcogenide Glass Platform

Authors: Jordan Goldstein, Hongtao Lin, Skylar Deckoff-Jones, Marek Hempel, Ang-Yu Lu, Kathleen A. Richardson, Tomas Palacios, Jing Kong, Juejun Hu, Dirk Englund

Abstract: The development of compact and fieldable mid-infrared (mid-IR) spectroscopy devices represents a critical challenge for distributed sensing with applications from gas leak detection to environmental monitoring. Recent work has focused on mid-IR photonic integrated circuit (PIC) sensing platforms and waveguide-integrated mid-IR light sources and detectors based on semiconductors such as PbTe, black… ▽ More The development of compact and fieldable mid-infrared (mid-IR) spectroscopy devices represents a critical challenge for distributed sensing with applications from gas leak detection to environmental monitoring. Recent work has focused on mid-IR photonic integrated circuit (PIC) sensing platforms and waveguide-integrated mid-IR light sources and detectors based on semiconductors such as PbTe, black phosphorus and tellurene. However, material bandgaps and reliance on SiO$_2$ substrates limit operation to wavelengths $λ\lesssim4\,μ\textrm{m}$. Here we overcome these challenges with a chalcogenide glass-on-CaF$_2$ PIC architecture incorporating split-gate photothermoelectric graphene photodetectors. Our design extends operation to $λ=5.2\,μ\textrm{m}$ with a Johnson noise-limited noise-equivalent power of $1.1\,\mathrm{nW}/\mathrm{Hz}^{1/2}$, no fall-off in photoresponse up to $f = 1\,\mathrm{MHz}$, and a predicted 3-dB bandwidth of $f_{3\textrm{dB}}>1\,\mathrm{GHz}$. This mid-IR PIC platform readily extends to longer wavelengths and opens the door to applications from distributed gas sensing and portable dual comb spectroscopy to weather-resilient free space optical communications. △ Less

Submitted 29 December, 2021; originally announced December 2021.

Comments: 15 pages, 11 figures

Towards convergence of turbulent dynamo amplification in cosmological simulations of galaxies

Authors: Sergio Martin-Alvarez, Julien Devriendt, Adrianne Slyz, Debora Sijacki, Mark L. A. Richardson, Harley Katz

Abstract: Our understanding of the process through which magnetic fields reached their observed strengths in present-day galaxies remains incomplete. One of the advocated solutions is a turbulent dynamo mechanism that rapidly amplifies weak magnetic field seeds to the order of ${\sim}μ$G. However, simulating the turbulent dynamo is a very challenging computational task due to the demanding span of spatial s… ▽ More Our understanding of the process through which magnetic fields reached their observed strengths in present-day galaxies remains incomplete. One of the advocated solutions is a turbulent dynamo mechanism that rapidly amplifies weak magnetic field seeds to the order of ${\sim}μ$G. However, simulating the turbulent dynamo is a very challenging computational task due to the demanding span of spatial scales and the complexity of the required numerical methods. In particular, turbulent velocity and magnetic fields are extremely sensitive to the spatial discretisation of simulated domains. To explore how refinement schemes affect galactic turbulence and amplification of magnetic fields in cosmological simulations, we compare two refinement strategies. A traditional quasi-Lagrangian adaptive mesh refinement approach focusing spatial resolution on dense regions, and a new refinement method that resolves the entire galaxy with a high resolution quasi-uniform grid. Our new refinement strategy yields much faster magnetic energy amplification than the quasi-Lagrangian method, which is also significantly greater than the adiabatic compressional estimate indicating that the extra amplification is produced through stretching of magnetic field lines. Furthermore, with our new refinement the magnetic energy growth factor scales with resolution following $\propto \Dres^{-1/2}$, in much better agreement with small-scale turbulent box simulations. Finally, we find evidence suggesting most magnetic amplification in our simulated galaxies occurs in the warm phase of their interstellar medium, which has a better developed turbulent field with our new refinement strategy. △ Less

Submitted 21 April, 2022; v1 submitted 12 November, 2021; originally announced November 2021.

Comments: Replaced to match version accepted for publication in MNRAS. 20 pages, 17 figures and 2 appendices

User-friendly Composition of FAIR Workflows in a Notebook Environment

Authors: Robin A Richardson, Remzi Celebi, Sven van der Burg, Djura Smits, Lars Ridder, Michel Dumontier, Tobias Kuhn

Abstract: There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles to scientific computational workflows. Jupyter notebooks are a very popular medium by which to program and communicate computational scientific analyses. However… ▽ More There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles to scientific computational workflows. Jupyter notebooks are a very popular medium by which to program and communicate computational scientific analyses. However, they present unique challenges when it comes to reuse of only particular steps of an analysis without disrupting the usual flow and benefits of the notebook approach, making it difficult to fully comply with the FAIR principles. Here we present an approach and toolset for adding the power of semantic technologies to Python-encoded scientific workflows in a simple, automated and minimally intrusive manner. The semantic descriptions are published as a series of nanopublications that can be searched and used in other notebooks by means of a Jupyter Lab plugin. We describe the implementation of the proposed approach and toolset, and provide the results of a user study with 15 participants, designed around image processing workflows, to evaluate the usability of the system and its perceived effect on FAIRness. Our results show that our approach is feasible and perceived as user-friendly. Our system received an overall score of 78.75 on the System Usability Scale, which is above the average score reported in the literature. △ Less

Submitted 1 November, 2021; originally announced November 2021.

Journal ref: Proceedings of the 11th Knowledge Capture Conference (K-CAP '21), December 2-3, 2021, Virtual Event, USA

Generalized Poincaré Orthogonality: A New Approach to POLSAR Data Analysis

Authors: Shane R. Cloude, Ashlin Richardson

Abstract: In this paper we outline a new approach to the analysis of polarimetric synthetic aperture (POLSAR) data. Here we exploit target orthogonality as a multi-dimensional extension of wave orthogonality, familiar on the Poincaré sphere. We first show how to formulate a general basis for a complex orthogonal scattering space using a generalization of the Poincaré formulation, and then show how to optimi… ▽ More In this paper we outline a new approach to the analysis of polarimetric synthetic aperture (POLSAR) data. Here we exploit target orthogonality as a multi-dimensional extension of wave orthogonality, familiar on the Poincaré sphere. We first show how to formulate a general basis for a complex orthogonal scattering space using a generalization of the Poincaré formulation, and then show how to optimize the backscattered signal in this space for both monostatic and bistatic radar systems. We illustrate application of the new approach, first to ship detection, using data collected off the north-west of Scotland and then land-use applications in a mixed scene around Glasgow, Scotland, both using L-band ALOS-2 POLSAR data. △ Less

Submitted 19 September, 2021; originally announced September 2021.

Comments: 6 pages, 7 figures

A System-on-Chip for Closed-loop Optogenetic Sleep Modulation

Authors: Xilin Liu, Andrew G. Richardson

Abstract: Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a fully integrated, low-power system-on-chip (SoC) for real-time sleep stage classification and stage-specific optical stimulation. The SoC consists of a 4… ▽ More Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a fully integrated, low-power system-on-chip (SoC) for real-time sleep stage classification and stage-specific optical stimulation. The SoC consists of a 4-channel analog front-end for recording polysomnography signals, a mixed-signal machine-learning (ML) core, and a 16-channel optical stimulation back-end. A novel ML algorithm and innovative circuit design techniques improved the online classification performance while minimizing power consumption. The SoC was designed and simulated in 180 nm CMOS technology. In an evaluation using an expert labeled sleep database with 20 subjects, the SoC achieves a high sensitivity of 0.806 and a specificity of 0.947 in discriminating 5 sleep stages. Overall power consumption in continuous operation is 97 uW. △ Less

Submitted 16 July, 2021; originally announced August 2021.

Comments: 8 pages, 9 figures, 2 tables

Modeling Intense-Electron-Beam Generated Plasmas Using a Rigid-Beam Approximation

Authors: A. S. Richardson, S. B. Swanekamp, N. D. Isner, D. D. Hinshelwood, D. Mosher, P. E. Adamson, I. M. Rittersdorf, Tz. B. Petrova, D. J. Watkins

Abstract: A model of an electron-beam-plasma system is introduced to model the electrical breakdown physics of low-pressure nitrogen irradiated by an intense pulsed electron beam. The rapidly rising beam current induces an electric field which drives a return current in the plasma. The rigid-beam model is a reduction of the problem geometry to cylindrical coordinates and simplifications to Maxwell's equatio… ▽ More A model of an electron-beam-plasma system is introduced to model the electrical breakdown physics of low-pressure nitrogen irradiated by an intense pulsed electron beam. The rapidly rising beam current induces an electric field which drives a return current in the plasma. The rigid-beam model is a reduction of the problem geometry to cylindrical coordinates and simplifications to Maxwell's equations that are driven by a prescribed electron beam current density. The model is convenient for comparing various reductions of the plasma dynamics and plasma chemistry while maintaining a good approximation to the overall magnitude of the beam-created electric field. The usefulness of this model is demonstrated by coupling the rigid-beam model to a fluid plasma model and a simplified nitrogen plasma chemistry. The dynamics of this coupled system are computed for a range of background gas pressures, and the results are compared with experimental measurements. At pressures 1 Torr and above, the simulated line-integrated electron densities are within a factor of two of measurements, and show the same trend with pressure as observed in experiment. △ Less

Submitted 24 August, 2021; originally announced August 2021.

Comments: Accepted for publication in Physics of Plasmas

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

Authors: Duarte Rondao, Nabil Aouf, Mark A. Richardson

Abstract: This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategie… ▽ More This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with red-green-blue (RGB) inputs, thus mitigating the effects of artefacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data. △ Less

Submitted 23 August, 2021; originally announced August 2021.

Comments: This work has been submitted to the IEEE for possible publication

Active learning for seismic processing parameterisation, with an application to first break picking

Authors: Alan Richardson

Abstract: Parameter values for seismic processing steps are often chosen on a regular grid of samples and interpolated. Active learning instead attempts to optimally select the samples on which parameter values are chosen. For parameters that do not vary smoothly, this often reduces the number of samples that need to be labelled in order to achieve a desired accuracy on the whole dataset. In regression task… ▽ More Parameter values for seismic processing steps are often chosen on a regular grid of samples and interpolated. Active learning instead attempts to optimally select the samples on which parameter values are chosen. For parameters that do not vary smoothly, this often reduces the number of samples that need to be labelled in order to achieve a desired accuracy on the whole dataset. In regression tasks this is typically achieved using a query by committee strategy that selects the samples on which a committee of models is most uncertain. I implement such a strategy for the first break picking task, where the parameters to be chosen are the centre and width of the picking window for each trace. For the committee members I use the centre of the picking window and three popular picking algorithms. Applying this to a real dataset, and with samples corresponding to shot gathers, the active learning approach primarily selects gathers near a jump in the first breaks, and achieves similar levels of accuracy on the whole dataset with about half the number samples picked as when the samples are randomly selected. △ Less

Submitted 17 June, 2021; originally announced June 2021.

Comments: 14 pages, 7 figures

Ultra-compact nonvolatile phase shifter based on electrically reprogrammable transparent phase change materials

Authors: Carlos Ríos, Qingyang Du, Yifei Zhang, Cosmin-Constantin Popescu, Mikhail Y. Shalaginov, Paul Miller, Christopher Roberts, Myungkoo Kang, Kathleen A. Richardson, Tian Gu, Steven A. Vitale, Juejun Hu

Abstract: Energy-efficient programmable photonic integrated circuits (PICs) are the cornerstone of on-chip classical and quantum optical technologies. Optical phase shifters constitute the fundamental building blocks which enable these programmable PICs. Thus far, carrier modulation and thermo-optical effect are the chosen phenomena for ultrafast and low-loss phase shifters, respectively; however, the state… ▽ More Energy-efficient programmable photonic integrated circuits (PICs) are the cornerstone of on-chip classical and quantum optical technologies. Optical phase shifters constitute the fundamental building blocks which enable these programmable PICs. Thus far, carrier modulation and thermo-optical effect are the chosen phenomena for ultrafast and low-loss phase shifters, respectively; however, the state and information they carry are lost once the power is turned off-they are volatile. The volatility not only compromises energy efficiency due to their demand for constant power supply, but also precludes them from emerging applications such as in-memory computing. To circumvent this limitation, we introduce a novel phase shifting mechanism that exploits the nonvolatile refractive index modulation upon structural phase transition of Sb$_{2}$Se$_{3}$, a bi-stable transparent phase change material. A zero-static power and electrically-driven phase shifter was realized on a foundry-processed silicon-on-insulator platform, featuring record phase modulation up to 0.09 $π$/$μ$m and a low insertion loss of 0.3 dB/$π$, which can be further improved upon streamlined design. We also pioneered a one-step partial amorphization scheme to enhance the speed and energy efficiency of PCM devices. A diverse cohort of programmable photonic devices were demonstrated based on the ultra-compact PCM phase shifter. △ Less

Submitted 21 March, 2022; v1 submitted 12 May, 2021; originally announced May 2021.

Comments: 15 pages with 6 figures and 1 table

The rigid-beam model for simulating plasmas generated by intense electron beams

Authors: S. B. Swanekamp, A. S. Richardson, Tz. B. Petrova, P. E. Adamson

Abstract: We introduce a simplified model of the electron-beam/plasma system to model the electrical breakdown caused by the inductive electric field created by a rapidly rising electron beam current. The rigid-beam model is a reduction to the problem geometry to cylindrical coordinated and simplifications to Maxwell's equations that are driven by a prescribed electron beam current density. The model is ver… ▽ More We introduce a simplified model of the electron-beam/plasma system to model the electrical breakdown caused by the inductive electric field created by a rapidly rising electron beam current. The rigid-beam model is a reduction to the problem geometry to cylindrical coordinated and simplifications to Maxwell's equations that are driven by a prescribed electron beam current density. The model is very convenient for comparing various reductions of the plasma dynamics and air chemistry equation while maintaining a good approximation to the overall magnitude of the beam-created electric field. The usefulness of this model is demonstrated by comparing results for two different fluid reductions of the plasma dynamics: one where the collision rates are computed from the local reduced electric field (E/p) and another where the collision rates are determined from the mean energy per particle. We find that the two methods give similar results at higher pressures where the energy relation rate is large but differs significantly at lower pressures where the characteristic inelastic energy loss time scale is comparable to or greater than the rise time of the electron beam current. △ Less

Submitted 3 March, 2021; originally announced March 2021.

The search for low-mass axion dark matter with ABRACADABRA-10cm

Authors: Chiara P. Salemi, Joshua W. Foster, Jonathan L. Ouellet, Andrew Gavin, Kaliroe M. W. Pappas, Sabrina Cheng, Kate A. Richardson, Reyco Henning, Yonatan Kahn, Rachel Nguyen, Nicholas L. Rodd, Benjamin R. Safdi, Lindley Winslow

Abstract: Two of the most pressing questions in physics are the microscopic nature of the dark matter that comprises 84% of the mass in the universe and the absence of a neutron electric dipole moment. These questions would be resolved by the existence of a hypothetical particle known as the quantum chromodynamics (QCD) axion. In this work, we probe the hypothesis that axions constitute dark matter, using t… ▽ More Two of the most pressing questions in physics are the microscopic nature of the dark matter that comprises 84% of the mass in the universe and the absence of a neutron electric dipole moment. These questions would be resolved by the existence of a hypothetical particle known as the quantum chromodynamics (QCD) axion. In this work, we probe the hypothesis that axions constitute dark matter, using the ABRACADABRA-10cm experiment in a broadband configuration, with world-leading sensitivity. We find no significant evidence for axions, and we present 95% upper limits on the axion-photon coupling down to the world-leading level $g_{aγγ}<3.2 \times10^{-11}$ GeV$^{-1}$, representing one of the most sensitive searches for axions in the 0.41 - 8.27 neV mass range. Our work paves a direct path for future experiments capable of confirming or excluding the hypothesis that dark matter is a QCD axion in the mass range motivated by String Theory and Grand Unified Theories. △ Less

Submitted 12 February, 2021; originally announced February 2021.

Comments: 17 pages, 12 figures

Journal ref: Phys. Rev. Lett. 127, 081801 (2021)