Showing 1–50 of 98 results for author: Chu, A

Model Card for OpenAI Privacy Filter

Authors: Charles de Bourcy, Sahra Ghalebikesabi, Avi Schwarzschild, Alex Gorbachev, Mihai Maruseac, Annie Chu, Vol Kyrylov, Tong Mu, Ally Bennett, Andy Nguyen, Casey Meehan, Jessica Gan Lee, Shane Bauer, Harold Nguyen, Rodolpho Eckhardt, Yuqi Liu, Charlie Oxborough, Marco Rougeth, Omar Chedid, Caio Costa, Yash Parikh, Yao Li, Congzheng Song, Om Thakkar, Vinnie Monaco

Abstract: OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder p… ▽ More OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across eight privacy categories and exposes configurable operating points for precision-recall tradeoffs. Privacy Filter has 1.5 billion total parameters, 50 million active parameters per token, and a 128,000-token context window. It is designed for efficient local deployment and domain-specific fine-tuning. Privacy Filter is intended as a configurable data-minimization component within layered privacy workflows, not as an anonymization or compliance guarantee. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Comments: 20 pages, 3 figures, 11 tables

QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery

Authors: Jaiman Munshi, Tanvi Tewary, Sawyer Bloom, Aidan Chu, Chetan Maviti, Kyon Winston-Bey, Harshit Badjatia, Farhan Kittur, Vardhan Madhavarapu, Varun Kota, Joshua Kwon, Nazia Rangwala-Vohra, Franz Klein

Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference. In this paper, we build on the foundational U-Net image segmentation model to develop a quantum-hybrid solution in hopes of more effectively modeling the high-dimensional spectral feature spac… ▽ More Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference. In this paper, we build on the foundational U-Net image segmentation model to develop a quantum-hybrid solution in hopes of more effectively modeling the high-dimensional spectral feature space of the Sen2Fire dataset. We inject a variational quantum circuit in the bottleneck portion of U-Net, specifically the QuFeX and QB-Net ansatzes. We test a classical Feature Pyramid Network (FPN) for further comparative analysis of the model, and we also explore classical improvements to the U-Net model and its training process, including a compression of parameters, alternative loss functions, and uniform mixing of input data. Our primary finding is that under matched conditions, both QB-Net (with an $F_1$ score of 31.18) and QuFeX ($F_1 = 30.79$) outperformed the classical U-Net baseline results ($F_1 = 28.71$). Additionally, the classical FPN achieved a comparable score of 31.13. A crucial finding was that data mixing removed a significant domain shift between the geographically-separated train and test sets, which boosted the classical FPN $F_1$ score to 39.76. We validate the architecture's robustness and generalizability to the wildfire detection problem via cross-dataset transfer on the California Burned Areas (CaBuAr) dataset. Overall, we find that quantum machine learning has potential to provide an advantage in the problem of wildfire image segmentation, and further experiments will continue to validate and expand upon this finding. △ Less

Submitted 14 July, 2026; originally announced July 2026.

Comments: 19 pages, 8 figures

Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities

Authors: Corban Villa, Sohee Kim, Austin Chu, Alon Shakevsky, Raluca Ada Popa

Abstract: AI-assisted vulnerability discovery has proven effective for bug classes like memory safety, where instrumentation confirms memory violations and efficiently filters false positives. Many dangerous vulnerability classes, such as cryptographic misuse, however, lack any comparable instrumentation. In this work, we present Chai, an AI-based system that discovers and validates cryptographic misuse vul… ▽ More AI-assisted vulnerability discovery has proven effective for bug classes like memory safety, where instrumentation confirms memory violations and efficiently filters false positives. Many dangerous vulnerability classes, such as cryptographic misuse, however, lack any comparable instrumentation. In this work, we present Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities through naturally occurring signals. To achieve this, Chai rethinks the classical technique of differential testing by leveraging AI to 1) improve precision for detecting real security issues in libraries, and 2) repurpose commonly overlooked discrepancies as leads for tangible vulnerabilities in downstream applications. In doing so, Chai inverts the prevailing paradigm of AI vulnerability discovery: instead of auditing one codebase for many flaws, it catalogs flaws at the library level and propagates them across a cryptographic dependency graph, delivering compounding efficiency gains. We evaluate Chai across X.509, JWT, and SAML libraries. Chai discovered a previously unknown critical vulnerability in an SSL library that powers billions of devices, along with security bugs in one library behind a major web browser and another in major Linux distributions. In total, these techniques surfaced over 100 vulnerabilities. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Scalable Bayesian data curation for next-generation radio experiments

Authors: S. A. K. Leeney, E. de Lera Acedo, W. J. Handley, H. T. J. Bevins, G. Allen, D. Anstey, K. Artuc, G. Bernardi, M. Bucher, S. Carey, J. Cavillot, R. Chiello, A. S. Chu, W. Croukamp, J. Cumner, S. Dasgupta, A. K. Dash, D. I. L. de Villiers, J. Dhandha, A. Dragovic, J. A. Ely, A. Fialkov, T. Gessey-Jones, C. Kirkham, G. Kulkarni , et al. (20 additional authors not shown)

Abstract: Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an exter… ▽ More Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an external pre-flag. Implemented in JAX with GPU-accelerated inference, the pipeline assigns probabilistic data-curation scores without prior knowledge and requires no thresholds, manual inspection, or subjective decisions. We demonstrate the method on the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH), applying it to 4655 observations (one year of REACH data). The pipeline assigns scores across time and frequency, enabling identification of the optimal observations to carry forward into scientific inference while reducing the risk that contaminated data bias the result. In doing so, it simultaneously recovers weather-driven systematics, instrument-component drifts, and narrow-band radio-frequency interference, while revealing complex dependencies between data quality and environmental or instrumental state that would be difficult to uncover by manual curation alone. This turns data curation from an external manual bottleneck into autonomous, inference-level infrastructure for the Square Kilometre Array era. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 12 pages, 8 figures

Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

Authors: Trong Khiem Tran, Anh Duc Chu, Quang Hung Pham, Phi Le Nguyen, Trong Nghia Hoang

Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another type of data (e.g., text/audio). Existing CMKD methods often require paired multi-modal data with aligned semantics, but obtaining such paired data are often costly and impractical. To mitigate this limitation, we develop a… ▽ More Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another type of data (e.g., text/audio). Existing CMKD methods often require paired multi-modal data with aligned semantics, but obtaining such paired data are often costly and impractical. To mitigate this limitation, we develop a new CMKD framework for the more challenging setting where paired data are unavailable. In particular, we establish a cross-modal distributional relationship between teacher and student models, which reveals two fundamental quantities governing effective distillation: feature alignment and label alignment. These quantities characterize semantic discrepancy between modalities at the levels of representation and prediction distributions, respectively. Motivated by this insight, we propose a principled framework, with theoretical guarantees, that enables effective cross-modal knowledge distillation by aligning distributions rather than individual samples. Extensive experiments across a wide range of multimodal benchmarks show that our framework is highly effective in both unpaired and paired data settings, improving significantly over prior work. △ Less

Submitted 9 June, 2026; originally announced June 2026.

FXplorer: A Map-Based Interface for Exploratory Audio Effect Design

Authors: Annie Chu, Jason Brent Smith, Bryan Pardo

Abstract: Audio effects (FX) shape sound in contemporary music practice. However, most interfaces present them as discrete modules and parameters that favor targeted adjustment over exploratory listening. This separation can make it difficult to build intuition about the broader space of possible transformations or to move fluidly between searching and refinement. We present FXplorer, an interface that orga… ▽ More Audio effects (FX) shape sound in contemporary music practice. However, most interfaces present them as discrete modules and parameters that favor targeted adjustment over exploratory listening. This separation can make it difficult to build intuition about the broader space of possible transformations or to move fluidly between searching and refinement. We present FXplorer, an interface that organizes audio effects within a perceptually informed 2D space, allowing sound transformations to be browsed as a continuous landscape rather than as isolated presets. By combining established spatial interaction approaches and interpretable DAW-style controls with recent embedding-based machine learning methods for similarity and semantic search, the system brings exploration and parameter refinement into a single workspace. FXplorer supports composition, production, or performance by allowing users to edit and interpolate between effect presets interactively. △ Less

Submitted 6 June, 2026; originally announced June 2026.

Comments: Accepted to NIME 2026. Project page: https://anniejchu.github.io/fxplorer/

Optimisation of calibration sources for global 21-cm experiments: the REACH case

Authors: Adarsh Kumar Dash, Dominic Anstey, Harry T. J. Bevins, Eloy de Lera Acedo, Gary Allen, Kaan Artuc, Gianni Bernardi, Martin Bucher, Steve Carey, Jean Cavillot, Ricardo Chiello, Adelicia S. Chu, Wessel Croukamp, John Cumner, Saswata Dasgupta, Dirk I. L. de Villiers, Jiten Dhandha, Aleksandra Dragovic, John A. Ely, Anastasia Fialkov, Thomas Gessey-Jones, Will J. Handley, Christian Kirkham, Girish Kulkarni, Samuel A. K. Leeney , et al. (19 additional authors not shown)

Abstract: The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that coul… ▽ More The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that could hinder a detection or lead to false inferences. REACH (Radio Experiment for the Analysis of Cosmic Hydrogen) is a radiometer experiment designed to detect this sky-averaged signal in the frequency range of 50--130~MHz. Using a wide-beam antenna, REACH calibration relies on internal reference sources, covering a broad range of temperatures and reflection coefficients. The choice of type and number of calibrators used significantly influences the quality of the calibration. This work investigates these effects and introduces a novel method for selecting an optimal set of calibration sources. With an optimised set, we aim to reduce calibration time, thereby increasing sky integration time while preserving calibration accuracy. We explore two optimisation strategies: one applied across the full receiver band and another performed on a frequency-by-frequency basis. Finally, we demonstrate that, with a total calibration time comparable to the conventional full-calibrator set, an optimised set with fewer calibrators achieves approximately a $15~\%$ reduction in calibrated temperature noise and improved absolute calibration of the instrument. This has implications for better calibration strategies in similar radiometer experiments. △ Less

Submitted 31 March, 2026; originally announced April 2026.

Comments: 13 pages, 8 figures

Circuit Modeling for In Situ 21 cm Radiometer Calibration

Authors: Gary Vincent Charles Allen, Saurabh Pegwal, Dirk de Villiers, Dominic Anstey, Kaan Artuc, Harry Bevins, Gianni Bernardi, Martin Bucher, Steve Carey, Jean Cavillot, Ricardo Chiello, Adele Chu, Wessel Croukamp, John Cumner, Ardash Dash, Saswata Dasgupta, Eloy de Lera Acedo, Jiten Dhandha, Aleksandra Dragovic, John Ely, Anastasia Fialkov, Thomas Gessey-Jones, Will Handley, Christian Kirkham, Girish Kulkarny , et al. (20 additional authors not shown)

Abstract: Recent experiments in cosmology, particularly those aimed at detecting the faint, redshifted, global 21 cm hydrogen line (depth < ~200 mK, z > 7.5), have imposed stringent new requirements on radiometer calibration. In this work, we present a framework for circuit modeling and parameter inference to strengthen these calibration pipelines. This new approach enables in situ characterization of other… ▽ More Recent experiments in cosmology, particularly those aimed at detecting the faint, redshifted, global 21 cm hydrogen line (depth < ~200 mK, z > 7.5), have imposed stringent new requirements on radiometer calibration. In this work, we present a framework for circuit modeling and parameter inference to strengthen these calibration pipelines. This new approach enables in situ characterization of otherwise immeasurable systematics using physically motivated models. A combination of frequentist and Bayesian techniques are employed in a pipeline that supports iterative modeling, robust parameter estimation, and detailed uncertainty quantification. The framework is applied to the REACH telescope, where the precise correction of variations in the radio signal paths arising from component aging or environmental effects is critical. Circuit models of REACH's calibration sources are developed, with the goal of predicting source temperature corrections that are conventionally obtained from laboratory measurements. By fitting the models to measured data using a convolutional cost function, a strong agreement with RMS residuals no worse than -37 dB is obtained. However, Bayesian inference reveals that the resulting temperature corrections can have uncertainties on the order of 1 to 2 K, caused by reflection coefficient degeneracies, measurement noise, and errors in the models. To combat this, posteriors obtained from laboratory measurements are employed as updated priors, reducing correction uncertainties down to 75 mK. Ultimately, the framework provides a means of dynamically accounting for drift in system non-idealities over time, addressing the increasing precision demands of global 21 cm radio astronomy. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Comments: 29 pages, 16 figures, 2 tables, authored by the REACH collaboration, Preprint of an article submitted for consideration in Journal of Astronomical Instrumentation \c{opyright} 2026 copyright World Scientific Publishing Company https://www.worldscientific.com/worldscinet/jai

Extracting information from a superradiant burst using simple measurements

Authors: Federico Belliardo, Anjun Chu, Martin Koppenhöfer, Aashish A. Clerk

Abstract: It is well known that superradiant decay of an ensemble of $N$ spins generates a complex non-classical state of light. Here, we consider the information content of a superradiant burst of photons: how is information encoded in the initial spin state distributed among the emitted photons, and can it be extracted using simple measurements? Despite the complexity of the photonic burst state, we show… ▽ More It is well known that superradiant decay of an ensemble of $N$ spins generates a complex non-classical state of light. Here, we consider the information content of a superradiant burst of photons: how is information encoded in the initial spin state distributed among the emitted photons, and can it be extracted using simple measurements? Despite the complexity of the photonic burst state, we show that a simple homodyne measurement combined with an optimized filter and linear estimator recovers the $N$-scaling of the quantum Fisher information of the initial spin state (including cases exhibiting $N^2$ Heisenberg scaling). Even more surprising, the temporal mode with optimal information content contains a vanishing fraction of the total emitted photons in the large-$N$ limit, suggesting an effective compressing of information. Our results and setup represent a new way to perform cavity based readout of solid-state spin ensembles that allows one to utilize resonant spin-photon interactions. △ Less

Submitted 31 March, 2026; v1 submitted 13 March, 2026; originally announced March 2026.

Comments: 28 pages, 6 figures

Less is More: Optimizing Probe Selection Using Shared Latency Anomalies

Authors: Taveesh Sharma, Andrew Chu, Paul Schmitt, Francesco Bronzino, Nick Feamster, Nicole Marwell

Abstract: Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple users observe anomalies to the same destination, this may reflect shared infrastructure, routing behavior, or congestion. Inferring such shared behavior is challenging because anomaly magnitudes vary widely across devices, even within the same ISP… ▽ More Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple users observe anomalies to the same destination, this may reflect shared infrastructure, routing behavior, or congestion. Inferring such shared behavior is challenging because anomaly magnitudes vary widely across devices, even within the same ISP and geographic area, and detailed network topology information is often unavailable. We study whether devices experiencing a shared latency anomaly observe similar changes in RTT magnitude using a topology-agnostic approach. Using four months of high-frequency RTT measurements from 99 residential probes in Chicago, we detect shared anomalies and analyze their consistency in amplitude and duration without relying on traceroutes or explicit path information. Building on prior change-point detection techniques, we find that many shared anomalies exhibit similar amplitude across users, particularly within the same ISP. Motivated by this observation, we design a sampling algorithm that reduces redundancy by selecting representative devices under user-defined constraints. Our approach captures 95 percent of aggregate anomaly impact using fewer than half of the deployed probes. Compared to two baselines, it identifies significantly more unique anomalies at comparable coverage levels. We further show that geographic diversity remains important when selecting probes within a single ISP, even at city scale. Overall, our results demonstrate that anomaly amplitude and duration provide effective topology-independent signals for scalable monitoring, troubleshooting, and cost-efficient sampling in residential Internet measurement. △ Less

Submitted 3 February, 2026; originally announced February 2026.

Mix2Morph: Learning Sound Morphing from Noisy Mixes

Authors: Annie Chu, Hugo Flores García, Oriol Nieto, Justin Salamon, Bryan Pardo, Prem Seetharaman

Abstract: We introduce Mix2Morph, a text-to-audio diffusion model fine-tuned to perform sound morphing without a dedicated dataset of morphs. By finetuning on noisy surrogate mixes at higher diffusion timesteps, Mix2Morph yields stable, perceptually coherent morphs that convincingly integrate qualities of both sources. We specifically target sound infusions, a practically and perceptually motivated subclass… ▽ More We introduce Mix2Morph, a text-to-audio diffusion model fine-tuned to perform sound morphing without a dedicated dataset of morphs. By finetuning on noisy surrogate mixes at higher diffusion timesteps, Mix2Morph yields stable, perceptually coherent morphs that convincingly integrate qualities of both sources. We specifically target sound infusions, a practically and perceptually motivated subclass of morphing in which one sound acts as the dominant primary source, providing overall temporal and structural behavior, while a secondary sound is infused throughout, enriching its timbral and textural qualities. Objective evaluations and listening tests show that Mix2Morph outperforms prior baselines and produces high-quality sound infusions across diverse categories, representing a step toward more controllable and concept-driven tools for sound design. Sound examples are available at https://anniejchu.github.io/mix2morph . △ Less

Submitted 28 January, 2026; originally announced January 2026.

Comments: Accepted into ICASSP 2026

An enumerative min-max theorem for minimal surfaces

Authors: Adrian Chun-Pong Chu, Yangyang Li, Zhihan Wang

Abstract: We prove an enumerative min-max theorem that relates the number of genus g minimal surfaces in 3-manifolds of positive Ricci curvature to topological properties of the set of embedded surfaces of genus $\leq g$, possibly with finitely many singularities. This completes a central component of our program of using topological methods to enumerating minimal surfaces with prescribed genus. As an app… ▽ More We prove an enumerative min-max theorem that relates the number of genus g minimal surfaces in 3-manifolds of positive Ricci curvature to topological properties of the set of embedded surfaces of genus $\leq g$, possibly with finitely many singularities. This completes a central component of our program of using topological methods to enumerating minimal surfaces with prescribed genus. As an application, we show that every 3-sphere of positive Ricci curvature contains at least 4 embedded minimal surfaces of genus 2. △ Less

Submitted 4 January, 2026; originally announced January 2026.

Comments: This paper supersedes the portion of arXiv:2507.23239v1 concerning the existence of genus 2 minimal surfaces

Simulation of topological superconductors and their competing orders using photon-mediated interactions

Authors: Anjun Chu, Joyce Kwan, Eric Yilun Song, Seth Hew Peng Chew, James K. Thompson, Ana Maria Rey

Abstract: Realizing and controlling the unconventional pairing featured by topological superconductors remains a central challenge. We introduce a cavity QED quantum simulator that engineers competing chiral $p_x+ip_y$ and $d_{x^2-y^2}+id_{xy}$ orders by tailoring cavity-mediated couplings between atomic pseudospins that emulate momentum-dependent pairing channels. The desired spatially inhomogeneous cavity… ▽ More Realizing and controlling the unconventional pairing featured by topological superconductors remains a central challenge. We introduce a cavity QED quantum simulator that engineers competing chiral $p_x+ip_y$ and $d_{x^2-y^2}+id_{xy}$ orders by tailoring cavity-mediated couplings between atomic pseudospins that emulate momentum-dependent pairing channels. The desired spatially inhomogeneous cavity-mediated couplings can be engineered in a 2D optical lattice using incommensurate cavity-lattice wavelengths naturally occurring in cavity QED systems. This minimal and fully tunable platform enables controlled state preparation and continuous measurement of superconducting order parameters, revealing phases in both equilibrium and sudden-quench settings with a single dominant pairing channel, as well as coexistence regimes with competing pairing channels. Crucially, our implementation allows direct observation of topological transitions in and out of equilibrium, providing a powerful route to the quantum simulation of competing topological superconducting phases that remain elusive in solid-state and ultracold-atom systems. △ Less

Submitted 19 December, 2025; originally announced December 2025.

Comments: 11+12 pages, 5+5 figures

Collective three-body interactions enable a robust quantum speedup

Authors: Haoqing Zhang, Anjun Chu, Chengyi Luo, Chitose Maruko, Eliot A. Bohr, James K. Thompson, Ana Maria Rey

Abstract: We show that collective three-body interactions (3BIs), implementable with $N$ atoms loaded inside an optical cavity, offer a significant advantage for preparing complex multipartite entangled states. Firstly, they enable a speedup of order $\sim N$ in preparing generalized Greenberger-Horne-Zeilinger (GHZ) states, outperforming conventional methods based on all-to-all two-body Ising interactions.… ▽ More We show that collective three-body interactions (3BIs), implementable with $N$ atoms loaded inside an optical cavity, offer a significant advantage for preparing complex multipartite entangled states. Firstly, they enable a speedup of order $\sim N$ in preparing generalized Greenberger-Horne-Zeilinger (GHZ) states, outperforming conventional methods based on all-to-all two-body Ising interactions. Secondly, they saturate the Heisenberg bound in phase estimation tasks using a time-reversal protocol realized through simple rotations and followed by experimentally accessible collective spin measurements. Lastly, compared with two-body interactions (2BIs), in the presence of cavity losses and single particle decoherence, 3BIs feature a high gain in sensitivity for moderate atom numbers and in large ensembles a fast entanglement generation despite constraints in parameter regimes where they are implementable. △ Less

Submitted 5 December, 2025; originally announced December 2025.

Comments: 8+8 pages, 7+5 figures

PADM: A Physics-aware Diffusion Model for Attenuation Correction

Authors: Trung Kien Pham, Hoang Minh Vu, Anh Duc Chu, Dac Thai Nguyen, Trung Thanh Nguyen, Thao Nguyen Truong, Mai Hong Son, Thanh Trung Nguyen, Phi Le Nguyen

Abstract: Attenuation artifacts remain a significant challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often compromising diagnostic accuracy and reducing clinical interpretability. While hybrid SPECT/CT systems mitigate these artifacts through CT-derived attenuation maps, their high cost, limited accessibility, and added radiation exposure hin… ▽ More Attenuation artifacts remain a significant challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often compromising diagnostic accuracy and reducing clinical interpretability. While hybrid SPECT/CT systems mitigate these artifacts through CT-derived attenuation maps, their high cost, limited accessibility, and added radiation exposure hinder widespread clinical adoption. In this study, we propose a novel CT-free solution to attenuation correction in cardiac SPECT. Specifically, we introduce Physics-aware Attenuation Correction Diffusion Model (PADM), a diffusion-based generative method that incorporates explicit physics priors via a teacher--student distillation mechanism. This approach enables attenuation artifact correction using only Non-Attenuation-Corrected (NAC) input, while still benefiting from physics-informed supervision during training. To support this work, we also introduce CardiAC, a comprehensive dataset comprising 424 patient studies with paired NAC and Attenuation-Corrected (AC) reconstructions, alongside high-resolution CT-based attenuation maps. Extensive experiments demonstrate that PADM outperforms state-of-the-art generative models, delivering superior reconstruction fidelity across both quantitative metrics and visual assessment. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Comments: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026

Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines

Authors: Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu, Spring Berman, Theodore P. Pavlic

Abstract: In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based cont… ▽ More In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence. △ Less

Submitted 26 October, 2025; originally announced October 2025.

Reconfigurable dissipative entanglement between many spin ensembles: from robust quantum sensing to many-body state engineering

Authors: Anjun Chu, Mikhail Mamaev, Martin Koppenhöfer, Ming Yuan, Aashish A. Clerk

Abstract: An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin states, or require numerous independent and complex dissipative processes. Here, we show a surprisingly versatile scheme for many-body reservoir engineering that relies solely on fully collective single-excitation decay,… ▽ More An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin states, or require numerous independent and complex dissipative processes. Here, we show a surprisingly versatile scheme for many-body reservoir engineering that relies solely on fully collective single-excitation decay, augmented with local Hamiltonian terms. Crucially, all these ingredients are readily available in cavity QED setups. Our method is based on splitting the spin system into groups of sub-ensembles, and provides an easily tunable setup for stabilizing a broad family of pure, highly entangled states with closed-form analytic descriptions. Our results have immediate application to multi-ensemble quantum metrology, enabling Heisenberg-limited sensing of field gradients and curvatures. Notably, our approach solves an important challenge in differential quantum sensing by providing the first example of Heisenberg-limited differential sensing immune to common-mode noise and accessible with only simple one-body measurements. The same setup also allows the stabilization of an entire family of entangled states in a 1D chain of spin ensembles with symmetry-protected topological (SPT) order, and have a direct connection to the outputs of sequential unitary circuits. A special case of our protocol efficiently stabilizes the celebrated Affleck-Kennedy-Lieb-Tasaki (AKLT) state. △ Less

Submitted 11 March, 2026; v1 submitted 8 October, 2025; originally announced October 2025.

Comments: 15+14 pages, 8+7 figures

Journal ref: Phys. Rev. X 16, 021047 (2026)

Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark

Authors: Minhui Zhu, Minyang Tian, Xiaocheng Yang, Tianci Zhou, Lifan Yuan, Penghao Zhu, Eli Chertkov, Shengyan Liu, Yufeng Du, Ziming Ji, Indranil Das, Qingzhi Chen, Junyi Cao, Yufeng Du, Jiabin Yu, Peixue Wu, Jinchen He, Yifan Su, Yikun Jiang, Yujie Zhang, Chang Liu, Ze-Min Huang, Weizhen Jia, Yunkai Wang, Farshid Jafarpour , et al. (40 additional authors not shown)

Abstract: While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integr… ▽ More While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking - Physics Test, pronounced "critical point"), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 5.7%, achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools. △ Less

Submitted 8 May, 2026; v1 submitted 30 September, 2025; originally announced September 2025.

Comments: 40 pages, 6 figures, 6 tables

Improving systematic uncertainties on precision two-body mass measurements

Authors: Allison Chu, Yiming Liu, Matthew Needham

Abstract: To make precision particle mass measurements in charged spectrometers detailed understanding of the influence of detector effects is critical. In this paper the influence of detector-related uncertainties on the determination of the parent particle mass in two-body decays is investigated. It is shown how the dependence of observed mass shifts on the sum and difference of the daughter particle mome… ▽ More To make precision particle mass measurements in charged spectrometers detailed understanding of the influence of detector effects is critical. In this paper the influence of detector-related uncertainties on the determination of the parent particle mass in two-body decays is investigated. It is shown how the dependence of observed mass shifts on the sum and difference of the daughter particle momenta can be used to determine the physical causes of a bias more rigorously than the \textit{ad hoc} rules that are often adopted. The approach is illustrated using the case of measuring the $Λ$ hyperon mass. This observable is of interest because our current knowledge relies on information from a single experiment that has not been updated to account for changes in the value of the $\textrm{K}_{\textrm{s}}^0$ mass used for calibration. With the approach developed in the paper it shown that the LHCb experiment has the capability to make a measurement of the $Λ$ mass with systematic uncertainties from the tracking system controlled to $0.7\,$keV/$c^2$. This allows a total precision of $2.2\,$keV/$c^2$ to be achieved, dominated by the knowledge of the $\textrm{K}_{\textrm{s}}^0$ mass used for calibration. This would improve the current knowledge of the $Λ$ hyperon mass by a factor of three. △ Less

Submitted 23 April, 2026; v1 submitted 30 September, 2025; originally announced September 2025.

Comments: 18 pages, 6 figures

Journal ref: EPJ Techn Instrum 13, 3 (2026)

Ethics Statements in AI Music Papers: The Effective and the Ineffective

Authors: Julia Barnett, Patrick O'Reilly, Jason Brent Smith, Annie Chu, Bryan Pardo

Abstract: While research in AI methods for music generation and analysis has grown in scope and impact, AI researchers' engagement with the ethical consequences of this work has not kept pace. To encourage such engagement, many publication venues have introduced optional or required ethics statements for AI research papers. Though some authors use these ethics statements to critically engage with the broade… ▽ More While research in AI methods for music generation and analysis has grown in scope and impact, AI researchers' engagement with the ethical consequences of this work has not kept pace. To encourage such engagement, many publication venues have introduced optional or required ethics statements for AI research papers. Though some authors use these ethics statements to critically engage with the broader implications of their research, we find that the majority of ethics statements in the AI music literature do not appear to be effectively utilized for this purpose. In this work, we conduct a review of ethics statements across ISMIR, NIME, and selected prominent works in AI music from the past five years. We then offer suggestions for both audio conferences and researchers for engaging with ethics statements in ways that foster meaningful reflection rather than formulaic compliance. △ Less

Submitted 29 September, 2025; originally announced September 2025.

Comments: Accepted to the 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: AI for Music

Galaxy groups in various evolutionary stages

Authors: Kethelin Parra Ramos, C. Adami, N. Clerc, A. Chu, F. Durret, G. B. Lima Neto, I. Márquez, L. Paquereau, F. Sarron, G. Soucail, P. Amram, Q. Moysan, D. Russeil

Abstract: The formation process of galaxy groups is not yet fully understood. In particular, that of fossil groups (FGs) is still under debate. Due to the relative rarity of FGs, large samples of such objects are still missing. The present paper aims to analyse the properties of groups in various evolutionary stages (FGs, "almost" FGs, and non-FGs), and to increase the sample of FG candidates. We have spect… ▽ More The formation process of galaxy groups is not yet fully understood. In particular, that of fossil groups (FGs) is still under debate. Due to the relative rarity of FGs, large samples of such objects are still missing. The present paper aims to analyse the properties of groups in various evolutionary stages (FGs, "almost" FGs, and non-FGs), and to increase the sample of FG candidates. We have spectroscopically observed galaxies in four groups and ten candidate FGs detected in the Canada France Hawaii Telescope Legacy Survey. We searched for substructures by applying the Serna-Gerbal dendrogram method to analyse the dynamical structure of each group. By applying the FIREFLY software to the continuum and PIPE_VIS to the emission lines, we derived the stellar population properties in various regions for each group. A roughly continuous variation in properties is found between a group that is still building up (XCLASS 1330), a well-formed massive group (MCG+00-27-023), a dynamically complex non-FG (NGC 4065), and a near-FG (NGC 4104). We also optically confirm two FGs in the Canada France Hawaii Telescope Legacy Survey, but their X-ray luminosity is still unknown. We observe that the lower the mass of the substructure, the more recent the stellar population in the considered groups. We also show an apparent lack of high-mass substructures for low-metallicity systems. These results are consistent with the generally adopted model of energy transfer during interactions of the galaxies with the group and cluster potential wells. Furthermore, the fossil status of a group might be related to the large-scale environment. Therefore, studying the positions of non-FGs, near-FGs, and FGs within the cosmic web can provide insights into the process of how fossil systems come into being in the Universe. △ Less

Submitted 23 September, 2025; originally announced September 2025.

Comments: 20 pages, 12 figures. Accepted for publication in A&A, proofs version

Minimal surface doublings and electrostatics for Schrödinger operators

Authors: Adrian Chun-Pong Chu, Daniel Stern

Abstract: Twenty years ago, N. Kapouleas introduced a singular perturbation construction known as "doubling", which produces sequences of high-genus minimal surfaces converging to a given minimal surface with multiplicity two. Doubling constructions have since been implemented successfully in several settings, with deep work of Kapouleas-McGrath reducing their existence theory to the problem of finding suit… ▽ More Twenty years ago, N. Kapouleas introduced a singular perturbation construction known as "doubling", which produces sequences of high-genus minimal surfaces converging to a given minimal surface with multiplicity two. Doubling constructions have since been implemented successfully in several settings, with deep work of Kapouleas-McGrath reducing their existence theory to the problem of finding suitable families of ansatz data on the initial minimal surface. In this paper, we introduce a variational approach to the existence of minimal doublings, relating the Kapouleas-McGrath construction to the study of nondegenerate critical points for a Coulomb-type interaction energy. By analyzing the minimizers of this energy, we prove that, in a generic closed 3-manifold, every two-sided, embedded minimal surface of index one admits a sequence of minimal doublings. As a corollary, we find that a generic 3-manifold contains an infinite sequence of embedded minimal surfaces with bounded area and arbitrarily large genus, whose geometry can be described with some precision. △ Less

Submitted 23 September, 2025; originally announced September 2025.

Comments: 82 pages

The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling

Authors: Patrick O'Reilly, Julia Barnett, Hugo Flores García, Annie Chu, Nathan Pruyne, Prem Seetharaman, Bryan Pardo

Abstract: Musicians and nonmusicians alike use rhythmic sound gestures, such as tapping and beatboxing, to express drum patterns. While these gestures effectively communicate musical ideas, realizing these ideas as fully-produced drum recordings can be time-consuming, potentially disrupting many creative workflows. To bridge this gap, we present TRIA (The Rhythm In Anything), a masked transformer model for… ▽ More Musicians and nonmusicians alike use rhythmic sound gestures, such as tapping and beatboxing, to express drum patterns. While these gestures effectively communicate musical ideas, realizing these ideas as fully-produced drum recordings can be time-consuming, potentially disrupting many creative workflows. To bridge this gap, we present TRIA (The Rhythm In Anything), a masked transformer model for mapping rhythmic sound gestures to high-fidelity drum recordings. Given an audio prompt of the desired rhythmic pattern and a second prompt to represent drumkit timbre, TRIA produces audio of a drumkit playing the desired rhythm (with appropriate elaborations) in the desired timbre. Subjective and objective evaluations show that a TRIA model trained on less than 10 hours of publicly-available drum data can generate high-quality, faithful realizations of sound gestures across a wide range of timbres in a zero-shot manner. △ Less

Submitted 19 September, 2025; originally announced September 2025.

Comments: ISMIR 2025

Minimal surfaces with arbitrary genus in 3-spheres of positive Ricci curvature

Authors: Adrian Chun-Pong Chu

Abstract: We describe some topological structure in the set of all surfaces with finitely many singularities in the 3-sphere. As an application, we prove that every Riemannian 3-sphere of positive Ricci curvature contains, for every g, a genus g embedded minimal surface with area at most twice the first Simon-Smith width of the ambient 3-sphere. We describe some topological structure in the set of all surfaces with finitely many singularities in the 3-sphere. As an application, we prove that every Riemannian 3-sphere of positive Ricci curvature contains, for every g, a genus g embedded minimal surface with area at most twice the first Simon-Smith width of the ambient 3-sphere. △ Less

Submitted 8 August, 2025; originally announced August 2025.

Comments: 60 pages

Min-max theory and minimal surfaces with prescribed genus

Authors: Adrian Chun-Pong Chu, Yangyang Li, Zhihan Wang

Abstract: We establish a general min-max type theorem that produces minimal surfaces with prescribed genus in 3-manifolds with positive Ricci curvature. An important intermediate step is to show that, in a generic metric with positive Ricci curvature, any family of surfaces with possibly finitely many singularities can be deformed into a certain topologically optimal family. Results in this paper will be… ▽ More We establish a general min-max type theorem that produces minimal surfaces with prescribed genus in 3-manifolds with positive Ricci curvature. An important intermediate step is to show that, in a generic metric with positive Ricci curvature, any family of surfaces with possibly finitely many singularities can be deformed into a certain topologically optimal family. Results in this paper will be crucial to our program on the construction of multiple minimal surfaces with prescribed genus in 3-spheres via topological methods. △ Less

Submitted 19 August, 2026; v1 submitted 31 July, 2025; originally announced July 2025.

Comments: v1 is superseded by the current version and arXiv:2601.01736. v3: Added a proof sketch. v4: Minor revision for Prop 5.7

Text-to-SQL for Enterprise Data Analytics

Authors: Albert Chen, Manas Bundele, Gaurav Ahlawat, Patrick Stetz, Zhitao Wang, Qiang Fei, Donghoon Jung, Audrey Chu, Bharadwaj Jayaraman, Ayushi Panth, Yatin Arora, Sourav Jain, Renjith Varma, Alexey Ilin, Iuliia Melnychuk, Chelsea Chueh, Joyan Sil, Xiaofeng Wang

Abstract: The introduction of large language models has brought rapid progress on Text-to-SQL benchmarks, but it is not yet easy to build a working enterprise solution. In this paper, we present insights from building an internal chatbot that enables LinkedIn's product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake. Our approach features three component… ▽ More The introduction of large language models has brought rapid progress on Text-to-SQL benchmarks, but it is not yet easy to build a working enterprise solution. In this paper, we present insights from building an internal chatbot that enables LinkedIn's product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake. Our approach features three components. First, we construct a knowledge graph that captures up-to-date semantics by indexing database metadata, historical query logs, wikis, and code. We apply clustering to identify relevant tables for each team or product area. Second, we build a Text-to-SQL agent that retrieves and ranks context from the knowledge graph, writes a query, and automatically corrects hallucinations and syntax errors. Third, we build an interactive chatbot that supports various user intents, from data discovery to query writing to debugging, and displays responses in rich UI elements to encourage follow-up chats. Our chatbot has over 300 weekly users. Expert review shows that 53% of its responses are correct or close to correct on an internal benchmark set. Through ablation studies, we identify the most important knowledge graph and modeling components, offering a practical path for developing enterprise Text-to-SQL solutions. △ Less

Submitted 18 July, 2025; originally announced July 2025.

Comments: 11 pages, 8 figures, Workshop on Agentic AI for Enterprise at KDD '25

Minimum-norm interpolation for unknown surface reconstruction

Authors: Alex Shiu Lun Chu, Leevan Ling, Ka Chun Cheung

Abstract: We study algorithms to estimate geometric properties of raw point cloud data through implicit surface representations. Given that any level-set function with a constant level set corresponding to the surface can be used for such estimations, numerical methods need not specify a unique target function for these domain-type interpolation problems. In this paper, we focus on kernel-based interpolatio… ▽ More We study algorithms to estimate geometric properties of raw point cloud data through implicit surface representations. Given that any level-set function with a constant level set corresponding to the surface can be used for such estimations, numerical methods need not specify a unique target function for these domain-type interpolation problems. In this paper, we focus on kernel-based interpolation by radial basis functions (RBF) and reformulate the uniquely solvable interpolation problem into a constrained optimization model. This model minimizes some user-defined norm while enforcing all interpolation conditions. To enable nontrivial feasible solutions, we propose to enhance the trial space with 1D kernel basis functions inspired by Kolmogorov-Arnold Networks (KANs). Numerical experiments demonstrate that our proposed mixed-dimensional trial space significantly improves surface reconstruction from raw point clouds. This is particularly evident in the precise estimation of surface normals, outperforming traditional RBF trial spaces including the one for Hermite interpolation. This framework not only enhances processing of raw point cloud data but also shows potential for further contributions to computational geometry. We demonstrate this with a point cloud processing example. △ Less

Submitted 31 March, 2026; v1 submitted 11 July, 2025; originally announced July 2025.

Atomic Coherence of 2 minutes and Instability of 1.5E-18 at 1 s in a Wannier-Stark Lattice Clock

Authors: Kyungtae Kim, Alexander Aeppli, William Warfield, Anjun Chu, Ana Maria Rey, Jun Ye

Abstract: We explore the limits of atomic coherence and measurement precision in a 87Sr optical lattice clock. We perform a detailed characterization of key effects, including lattice Raman scattering and atomic collisions in a shallow lattice configuration, determining a 174(28) s 3P0 clock state lifetime. Investigation of atomic coherence across a range of lattice depths and atomic densities reveals decoh… ▽ More We explore the limits of atomic coherence and measurement precision in a 87Sr optical lattice clock. We perform a detailed characterization of key effects, including lattice Raman scattering and atomic collisions in a shallow lattice configuration, determining a 174(28) s 3P0 clock state lifetime. Investigation of atomic coherence across a range of lattice depths and atomic densities reveals decoherence mechanisms related to photon scattering and atomic interaction. At a reduced density, we observe a coherence time of 118(9) s, approaching the fundamental limit set by spontaneous emission. Guided by this coherence understanding, we demonstrate a clock instability of 1.5E-18 at 1 s in fractional frequency units. Our results are important for further advancing the state-of-the-art of an optical lattice clock for fundamental physics applications. △ Less

Submitted 9 May, 2025; originally announced May 2025.

Solitons in arbitrary dimensions stabilized by photon-mediated interactions

Authors: Haoqing Zhang, Anjun Chu, Chengyi Luo, James K. Thompson, Ana Maria Rey

Abstract: We propose a scheme to generate solitons in arbitrary dimensions, in a matter-wave interferometer, without the need of quantum degeneracy. In our setting, solitons emerge by balancing the single-particle dispersion with engineered cavity-mediated exchange interactions between two wave packets, which, at the appropriate conditions, remain bound to each other and dispersion-free. For detection in th… ▽ More We propose a scheme to generate solitons in arbitrary dimensions, in a matter-wave interferometer, without the need of quantum degeneracy. In our setting, solitons emerge by balancing the single-particle dispersion with engineered cavity-mediated exchange interactions between two wave packets, which, at the appropriate conditions, remain bound to each other and dispersion-free. For detection in thermal gases, we propose an interferometric probing scheme instead of traditional time-of-flight imaging. △ Less

Submitted 23 April, 2025; originally announced April 2025.

Comments: 8+5 pages, 6+2 figures

Journal ref: Phys. Rev. Lett. 135, 173402 (2025)

NetSSM: Multi-Flow and State-Aware Network Trace Generation using State Space Models

Authors: Andrew Chu, Xi Jiang, Shinan Liu, Arjun Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster

Abstract: Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. Previous efforts explore this challenge by generating synthetic network data, but fail to reliably handle multi-flow sessions, struggle to reason about stateful communication in mod… ▽ More Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. Previous efforts explore this challenge by generating synthetic network data, but fail to reliably handle multi-flow sessions, struggle to reason about stateful communication in moderate to long-duration network sessions, and lack robust evaluations tied to real-world utility. We propose a new method based on state space models called NetSSM that generates raw network traffic at the packet-level granularity. Our approach captures interactions between multiple, interleaved flows -- an objective unexplored in prior work -- and effectively reasons about flow-state in sessions to capture traffic characteristics. NetSSM accomplishes this by learning from and producing traces 8x and 78x longer than existing transformer-based approaches. Evaluation results show that our method generates high-fidelity traces that outperform prior efforts in existing benchmarks. We also find that NetSSM's traces have high semantic similarity to real network data regarding compliance with standard protocol requirements and flow and session-level traffic characteristics. △ Less

Submitted 21 January, 2026; v1 submitted 28 March, 2025; originally announced March 2025.

Comments: Accepted to PACMNET V4, CoNEXT1, March 2026 Issue

Robust Flower Cluster Matching Using The Unscented Transform

Authors: Andy Chu, Rashik Shrestha, Yu Gu, Jason N. Gross

Abstract: Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angle… ▽ More Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angles. Plants flower in a manner that produces distinct clusters on branches. This paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster. The proposed approach leverages the Unscented Transform to efficiently estimate plant descriptor uncertainty tolerances, enabling a robust image-registration process despite temporal changes. The Unscented Transform is used to handle the nonlinear transformations by propagating the uncertainty of flower positions to determine the variations in the descriptor domain. A Monte Carlo simulation is used to validate the Unscented Transform results, confirming our method's effectiveness for flower cluster matching. Therefore, it can facilitate improved robotics pollination in dynamic environments. △ Less

Submitted 13 June, 2025; v1 submitted 26 March, 2025; originally announced March 2025.

Comments: *CASE2025 Accepted*

Preparing for Rubin-LSST -- Detecting Brightest Cluster Galaxies with Machine Learning in the LSST DP0.2 simulation

Authors: Aline Chu, Ludvig Doeser, Simon Ding, Jens Jasche

Abstract: The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with unrivalled coverage, depth and uniformity. The study of galaxy clusters informs us on the effect of environmental processes on galactic formation, which directly tra… ▽ More The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with unrivalled coverage, depth and uniformity. The study of galaxy clusters informs us on the effect of environmental processes on galactic formation, which directly translates onto the formation of the brightest cluster galaxy (BCG). These massive galaxies present traces of the whole merger history of their host clusters, which can be in the shape of intra-cluster light (ICL) that surrounds them, tidal streams, or simply by the accumulated stellar mass that has been acquired over the past 10 billion years as they have cannibalized other galaxies in their surroundings. In an era where new data is being generated faster than humans can deal with, new methods involving machine learning have been emerging more and more in the most recent years. In the aim of preparing for the future LSST data release which will allow the observations of more than 20000 clusters and BCGs, we present in this paper different methods based on machine learning to detect these BCGs on LSST-like optical images. This study is done by making use of the simulated LSST Data Preview images. We find that the use of machine learning allows to accurately identify the BCG in up to 95% of clusters in our sample. Compared to more conventional red sequence extraction methods, the use of machine learning appears to be faster, more efficient and consistent, and does not require much, if any, pre-processing. △ Less

Submitted 20 March, 2025; originally announced March 2025.

Comments: Submitted to Astronomy & Astrophysics

Journal ref: A&A 706, A317 (2026)

A symmetry-protected topological optical lattice clock

Authors: Tianrui Xu, Anjun Chu, Kyungtae Kim, James K. Thompson, Jun Ye, Tilman Esslinger, Ana Maria Rey

Abstract: We theoretically propose a tunable implementation of symmetry-protected topological phases in a synthetic superlattice, taking advantage of the long coherence time and exquisite spectral resolutions offered by gravity-tilted optical lattice clocks. We describe a protocol similar to Rabi spectroscopy that can be used to probe the distinct topological properties of our system. We then demonstrate ho… ▽ More We theoretically propose a tunable implementation of symmetry-protected topological phases in a synthetic superlattice, taking advantage of the long coherence time and exquisite spectral resolutions offered by gravity-tilted optical lattice clocks. We describe a protocol similar to Rabi spectroscopy that can be used to probe the distinct topological properties of our system. We then demonstrate how the sensitivity of clocks and interferometers can be improved by the protection to unwanted experimental imperfections offered by the underlying topological robustness. The proposed implementation opens a path to exploit the unique opportunities offered by symmetry-protected topological phases in state-of-the-art quantum sensors. △ Less

Submitted 2 July, 2025; v1 submitted 16 January, 2025; originally announced January 2025.

Comments: 16 pages, 10 figures

Hierarchical Classification for Predicting Metastasis Using Elastic-Net Regularization on Gene Expression Data

Authors: Benjamin Osafo Agyare, Alec Chu, Blessing Oloyede

Abstract: Metastasis is a leading cause of cancer-related mortality and remains challenging to detect during early stages. Accurate identification of cancers likely to metastasize can improve treatment strategies and patient outcomes. This study leverages publicly available gene expression profiles from primary cancers, with and without distal metastasis, to build predictive models. We utilize elastic net r… ▽ More Metastasis is a leading cause of cancer-related mortality and remains challenging to detect during early stages. Accurate identification of cancers likely to metastasize can improve treatment strategies and patient outcomes. This study leverages publicly available gene expression profiles from primary cancers, with and without distal metastasis, to build predictive models. We utilize elastic net regularization within a hierarchical classification framework to predict both the tissue of origin and the metastasis status of primary tumors. Our elastic net-based hierarchical classification achieved a tissue-of-origin prediction accuracy of 97%, and a metastasis prediction accuracy of 90%. Notably, mitochondrial gene expression exhibited significant negative correlations with metastasis, providing potential biological insights into the underlying mechanisms of cancer progression. △ Less

Submitted 29 November, 2024; v1 submitted 22 October, 2024; originally announced October 2024.

Sound Check: Auditing Audio Datasets

Authors: William Agnew, Julia Barnett, Annie Chu, Rachel Hong, Michael Feffer, Robin Netzorg, Harry H. Jiang, Ezra Awumey, Sauvik Das

Abstract: Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we hav… ▽ More Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we have little understanding of similar issues with generative audio datasets, including those related to bias, toxicity, and intellectual property. To bridge this gap, we conducted a literature review of hundreds of audio datasets and selected seven of the most prominent to audit in more detail. We found that these datasets are biased against women, contain toxic stereotypes about marginalized communities, and contain significant amounts of copyrighted work. To enable artists to see if they are in popular audio datasets and facilitate exploration of the contents of these datasets, we developed a web tool audio datasets exploration tool at https://audio-audit.vercel.app. △ Less

Submitted 16 October, 2024; originally announced October 2024.

Realization of three and four-body interactions between momentum states in a cavity through optical dressing

Authors: Chengyi Luo, Haoqing Zhang, Chitose Maruko, Eliot A. Bohr, Anjun Chu, Ana Maria Rey, James K. Thompson

Abstract: Paradigmatic spin Hamiltonians in condensed matter and quantum sensing typically utilize pair-wise or 2-body interactions between constituents in the material or ensemble. However, there is growing interest in exploring more general $n$-body interactions for $n >2$, with examples including more efficient quantum gates or the realization of exotic many-body fracton states. Here we realize an effect… ▽ More Paradigmatic spin Hamiltonians in condensed matter and quantum sensing typically utilize pair-wise or 2-body interactions between constituents in the material or ensemble. However, there is growing interest in exploring more general $n$-body interactions for $n >2$, with examples including more efficient quantum gates or the realization of exotic many-body fracton states. Here we realize an effective $n=3$-body Hamiltonian interaction using an ensemble of laser-cooled atoms in a high finesse optical cavity with the pseudo-spin 1/2 encoded by two atomic momentum states. To realize this interaction, we apply two dressing tones that coax the atoms to exchange photons via the cavity to realize a virtual 6-photon process, while the lower-order interactions destructively interfere. The resulting photon mediated interactions are not only $n>2$-body but also all-to-all(-to-all) and therefore of great interest for fast entanglement generation and quantum simulation of exotic phases such as the long sought but not yet observed charge-Qe superconductors, with $Q=2n$ . The versatility of our experimental system can also allow for extending to 3-body interactions in multi-level systems or to higher-order interactions, such as the signature of a $n=4$-body interaction mediated by a virtual eight photon process that we also observe. △ Less

Submitted 15 October, 2024; originally announced October 2024.

Text2FX: Harnessing CLAP Embeddings for Text-Guided Audio Effects

Authors: Annie Chu, Patrick O'Reilly, Julia Barnett, Bryan Pardo

Abstract: This work introduces Text2FX, a method that leverages CLAP embeddings and differentiable digital signal processing to control audio effects, such as equalization and reverberation, using open-vocabulary natural language prompts (e.g., "make this sound in-your-face and bold"). Text2FX operates without retraining any models, relying instead on single-instance optimization within the existing embeddi… ▽ More This work introduces Text2FX, a method that leverages CLAP embeddings and differentiable digital signal processing to control audio effects, such as equalization and reverberation, using open-vocabulary natural language prompts (e.g., "make this sound in-your-face and bold"). Text2FX operates without retraining any models, relying instead on single-instance optimization within the existing embedding space, thus enabling a flexible, scalable approach to open-vocabulary sound transformations through interpretable and disentangled FX manipulation. We show that CLAP encodes valuable information for controlling audio effects and propose two optimization approaches using CLAP to map text to audio effect parameters. While we demonstrate with CLAP, this approach is applicable to any shared text-audio embedding space. Similarly, while we demonstrate with equalization and reverberation, any differentiable audio effect may be controlled. We conduct a listener study with diverse text prompts and source audio to evaluate the quality and alignment of these methods with human perception. Demos and code are available at anniejchu.github.io/text2fx. △ Less

Submitted 20 February, 2025; v1 submitted 27 September, 2024; originally announced September 2024.

Comments: Accepted to ICASSP 2025. Source code and audio examples: https://anniejchu.github.io/text2fx/

Many-body gap protection of motional dephasing of an optical clock transition

Authors: Zhijing Niu, Vera M. Schäfer, Haoqing Zhang, Cameron Wagner, Nathan R. Taylor, Dylan J. Young, Eric Yilun Song, Anjun Chu, Ana Maria Rey, James K. Thompson

Abstract: Quantum simulation and metrology with atoms, ions, and molecules often rely on using light fields to manipulate their internal states. The absorbed momentum from the light fields can induce spin-orbit coupling and associated motional-induced (Doppler) dephasing, which may limit the coherence time available for metrology and simulation. We experimentally demonstrate the suppression of Doppler depha… ▽ More Quantum simulation and metrology with atoms, ions, and molecules often rely on using light fields to manipulate their internal states. The absorbed momentum from the light fields can induce spin-orbit coupling and associated motional-induced (Doppler) dephasing, which may limit the coherence time available for metrology and simulation. We experimentally demonstrate the suppression of Doppler dephasing on a strontium optical clock transition by enabling atomic interactions through a shared mode in a high-finesse optical ring cavity. The interactions create a many-body energy gap that increases with atom number, suppressing motional dephasing when it surpasses the dephasing energy scale. This collective approach offers an alternative to traditional methods, like Lamb-Dicke confinement or Mössbauer spectroscopy, for advancing optical quantum sensors and simulations. △ Less

Submitted 24 September, 2024; originally announced September 2024.

Comments: Main Text with Supplementary Material, 8 pages, 4 figures

Existence of 5 minimal tori in 3-spheres of positive Ricci curvature

Authors: Adrian Chun-Pong Chu, Yangyang Li

Abstract: In 1989, B. White conjectured that every Riemannian 3-sphere has at least 5 embedded minimal tori. We confirm this conjecture for 3-spheres of positive Ricci curvature. While our proof uses min-max theory, the underlying heuristics are largely inspired by mean curvature flow. In 1989, B. White conjectured that every Riemannian 3-sphere has at least 5 embedded minimal tori. We confirm this conjecture for 3-spheres of positive Ricci curvature. While our proof uses min-max theory, the underlying heuristics are largely inspired by mean curvature flow. △ Less

Submitted 31 July, 2025; v1 submitted 14 September, 2024; originally announced September 2024.

Comments: v2: Minor revisions, revised section 8.1

Dynamic Bayesian Networks with Conditional Dynamics in Edge Addition and Deletion

Authors: Lupe S. H. Chan, Amanda M. Y. Chu, Mike K. P. So

Abstract: This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mixture network approach, which restricts the number of possible edge changes, or structural priors to induce gradual changes, which can lead to unclear… ▽ More This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mixture network approach, which restricts the number of possible edge changes, or structural priors to induce gradual changes, which can lead to unclear network evolution, our model induces more frequent and intuitive edge change dynamics. We employ Markov chain Monte Carlo (MCMC) sampling to estimate the model structures and parameters and demonstrate the model's effectiveness in a portfolio selection application. △ Less

Submitted 7 May, 2025; v1 submitted 13 September, 2024; originally announced September 2024.

MSC Class: 62F15 ACM Class: G.3

De novo design of high-affinity protein binders with AlphaProteo

Authors: Vinicius Zambaldi, David La, Alexander E. Chu, Harshnira Patani, Amy E. Danson, Tristan O. C. Kwan, Thomas Frerix, Rosalia G. Schneider, David Saxton, Ashok Thillaisundaram, Zachary Wu, Isabel Moraes, Oskar Lange, Eliseo Papa, Gabriella Stanton, Victor Martin, Sukhdeep Singh, Lai H. Wong, Russ Bates, Simon A. Kohl, Josh Abramson, Andrew W. Senior, Yilmaz Alguel, Mary Y. Wu, Irene M. Aspalter , et al. (7 additional authors not shown)

Abstract: Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learni… ▽ More Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization. △ Less

Submitted 12 September, 2024; originally announced September 2024.

Comments: 45 pages, 17 figures

Time-resolved pairing gap spectroscopy in a quantum simulator of fermionic superfluidity inside an optical cavity

Authors: Dylan J. Young, Eric Yilun Song, Anjun Chu, Diego Barberena, Zhijing Niu, Vera M. Schäfer, Robert J. Lewis-Swan, Ana Maria Rey, James K. Thompson

Abstract: We use an ensemble of laser-cooled strontium atoms in a high-finesse cavity to cleanly emulate the technique of rf spectroscopy employed in studies of BEC-BCS physics in fermionic superfluids of degenerate cold gases. Here, we leverage the multilevel internal structure of the atoms to study the physics of Cooper pair breaking in this system. In doing so, we observe and distinguish the properties o… ▽ More We use an ensemble of laser-cooled strontium atoms in a high-finesse cavity to cleanly emulate the technique of rf spectroscopy employed in studies of BEC-BCS physics in fermionic superfluids of degenerate cold gases. Here, we leverage the multilevel internal structure of the atoms to study the physics of Cooper pair breaking in this system. In doing so, we observe and distinguish the properties of two distinct many-body gaps, the BCS pairing gap and the spectral gap, using nondestructive readout techniques. The latter is found to depend on the populations of the internal atomic states, reflecting the chemical potential dependence predicted in fermionic superfluids. This work opens the path for more fully exploiting the rich internal structure of atoms in cavity QED emulators to study both analogous systems and also more exotic states yet to be realized. △ Less

Submitted 22 August, 2024; originally announced August 2024.

Comments: Main Text with Supplementary Material, 15 pages, 5 figures

A dissipation-induced superradiant transition in a strontium cavity-QED system

Authors: Eric Yilun Song, Diego Barberena, Dylan J. Young, Edwin Chaparro, Anjun Chu, Sanaa Agarwal, Zhijing Niu, Jeremy T. Young, Ana Maria Rey, James K. Thompson

Abstract: In cavity quantum electrodynamics (QED), emitters and a resonator are coupled together to enable precise studies of quantum light-matter interactions. Over the past few decades, this has led to a variety of quantum technologies such as more precise inertial sensors, clocks, memories, controllable qubits, and quantum simulators. Furthermore, the intrinsically dissipative nature of cavity QED platfo… ▽ More In cavity quantum electrodynamics (QED), emitters and a resonator are coupled together to enable precise studies of quantum light-matter interactions. Over the past few decades, this has led to a variety of quantum technologies such as more precise inertial sensors, clocks, memories, controllable qubits, and quantum simulators. Furthermore, the intrinsically dissipative nature of cavity QED platforms makes them a natural testbed for exploring driven-dissipative phenomena in open quantum systems as well as equilibrium and non-equilibrium phase transitions in quantum optics. One such model, the so-called cooperative resonance fluorescence (CRF) model, concerns the behavior of coherently driven emitters in the presence of collective dissipation (superradiance). Despite tremendous interest, this model has yet to be realized in a clean experimental system. Here we provide an observation of the continuous superradiant phase transition predicted in the CRF model using an ensemble of ultracold $^{88}$Sr atoms coupled to a driven high-finesse optical cavity on a long-lived optical transition. Below a critical drive, atoms quickly reach a steady state determined by the self-balancing of the drive and the collective dissipation. The steady state possesses a macroscopic dipole moment and corresponds to a superradiant phase. Above a critical drive strength, the atoms undergo persistent Rabi-like oscillations until other decoherence processes kick in. In fact, our platform also allows us to witness the change of this phase transition from second to first order induced by single-particle spontaneous emission, which pushes the system towards a different steady state. Our observations are a first step towards finer control of driven-dissipative systems, which have been predicted to generate quantum states that can be harnessed for quantum information processing and in particular quantum sensing. △ Less

Submitted 26 August, 2024; v1 submitted 20 August, 2024; originally announced August 2024.

Comments: 12 pages, 7 figures (main text and methods); 11 pages, 4 figures (supplementary information); typos corrected

Graphical copula GARCH modeling with dynamic conditional dependence

Authors: Lupe Shun Hin Chan, Amanda Man Ying Chu, Mike Ka Pui So

Abstract: Modeling returns on large portfolios is a challenging problem as the number of parameters in the covariance matrix grows as the square of the size of the portfolio. Traditional correlation models, for example, the dynamic conditional correlation (DCC)-GARCH model, often ignore the nonlinear dependencies in the tail of the return distribution. In this paper, we aim to develop a framework to model t… ▽ More Modeling returns on large portfolios is a challenging problem as the number of parameters in the covariance matrix grows as the square of the size of the portfolio. Traditional correlation models, for example, the dynamic conditional correlation (DCC)-GARCH model, often ignore the nonlinear dependencies in the tail of the return distribution. In this paper, we aim to develop a framework to model the nonlinear dependencies dynamically, namely the graphical copula GARCH (GC-GARCH) model. Motivated from the capital asset pricing model, to allow modeling of large portfolios, the number of parameters can be greatly reduced by introducing conditional independence among stocks given some risk factors. The joint distribution of the risk factors is factorized using a directed acyclic graph (DAG) with pair-copula construction (PCC) to enhance the modeling of the tails of the return distribution while offering the flexibility of having complex dependent structures. The DAG induces topological orders to the risk factors, which can be regarded as a list of directions of the flow of information. The conditional distributions among stock returns are also modeled using PCC. Dynamic conditional dependence structures are incorporated to allow the parameters in the copulas to be time-varying. Three-stage estimation is used to estimate parameters in the marginal distributions, the risk factor copulas, and the stock copulas. The simulation study shows that the proposed estimation procedure can estimate the parameters and the underlying DAG structure accurately. In the investment experiment of the empirical study, we demonstrate that the GC-GARCH model produces more precise conditional value-at-risk prediction and considerably higher cumulative portfolio returns than the DCC-GARCH model. △ Less

Submitted 21 June, 2024; originally announced June 2024.

MSC Class: 62F15 ACM Class: G.3

Human-AI collectives produce the most accurate differential diagnoses

Authors: N. Zöller, J. Berger, I. Lin, N. Fu, J. Komarneni, G. Barabucci, K. Laskowski, V. Shia, B. Harack, E. A. Chu, V. Trianni, R. H. J. M. Kurvers, S. M. Herzog

Abstract: Artificial intelligence systems, particularly large language models (LLMs), are increasingly being employed in high-stakes decisions that impact both individuals and society at large, often without adequate safeguards to ensure safety, quality, and equity. Yet LLMs hallucinate, lack common sense, and are biased - shortcomings that may reflect LLMs' inherent limitations and thus may not be remedied… ▽ More Artificial intelligence systems, particularly large language models (LLMs), are increasingly being employed in high-stakes decisions that impact both individuals and society at large, often without adequate safeguards to ensure safety, quality, and equity. Yet LLMs hallucinate, lack common sense, and are biased - shortcomings that may reflect LLMs' inherent limitations and thus may not be remedied by more sophisticated architectures, more data, or more human feedback. Relying solely on LLMs for complex, high-stakes decisions is therefore problematic. Here we present a hybrid collective intelligence system that mitigates these risks by leveraging the complementary strengths of human experience and the vast information processed by LLMs. We apply our method to open-ended medical diagnostics, combining 40,762 differential diagnoses made by physicians with the diagnoses of five state-of-the art LLMs across 2,133 medical cases. We show that hybrid collectives of physicians and LLMs outperform both single physicians and physician collectives, as well as single LLMs and LLM ensembles. This result holds across a range of medical specialties and professional experience, and can be attributed to humans' and LLMs' complementary contributions that lead to different kinds of errors. Our approach highlights the potential for collective human and machine intelligence to improve accuracy in complex, open-ended domains like medical diagnostics. △ Less

Submitted 21 June, 2024; originally announced June 2024.

Entangled Matter-waves for Quantum Enhanced Sensing

Authors: John Drew Wilson, Jarrod T. Reilly, Haoqing Zhang, Chengyi Luo, Anjun Chu, James K. Thompson, Ana Maria Rey, Murray J. Holland

Abstract: The ability to create and harness entanglement is crucial to the fields of quantum sensing and simulation, and ultracold atom-cavity systems offer pristine platforms for this undertaking. Here, we present a method for creating and controlling entanglement between solely the motional states of atoms in a cavity without the need for electronic interactions. We show this interaction arises from a gen… ▽ More The ability to create and harness entanglement is crucial to the fields of quantum sensing and simulation, and ultracold atom-cavity systems offer pristine platforms for this undertaking. Here, we present a method for creating and controlling entanglement between solely the motional states of atoms in a cavity without the need for electronic interactions. We show this interaction arises from a general atom-cavity model, and discuss the role of the cavity frequency shift in response to atomic motion. This cavity response leads to many different squeezing interactions between the atomic momentum states. Furthermore, we show that when the atoms form a density grating, the collective motion leads to one-axis twisting, a many-body energy gap, and metrologically useful entanglement even in the presence of noise. Noteably, an experiment has recently demonstrated this regime leads to an effective momentum-exchange interaction between atoms in a common cavity mode. This system offers a highly tunable, many-body quantum sensor and simulator. △ Less

Submitted 12 August, 2024; v1 submitted 19 June, 2024; originally announced June 2024.

Exploring the dynamical interplay between mass-energy equivalence, interactions and entanglement in an optical lattice clock

Authors: Anjun Chu, Victor J. Martínez-Lahuerta, Maya Miklos, Kyungtae Kim, Peter Zoller, Klemens Hammerer, Jun Ye, Ana Maria Rey

Abstract: We propose protocols that probe manifestations of the mass-energy equivalence in an optical lattice clock (OLC) interrogated with spin coherent and entangled quantum states. To tune and uniquely distinguish the mass-energy equivalence effects (gravitational redshift and second order Doppler shift) in such a setting, we devise a dressing protocol using an additional nuclear spin state. We then anal… ▽ More We propose protocols that probe manifestations of the mass-energy equivalence in an optical lattice clock (OLC) interrogated with spin coherent and entangled quantum states. To tune and uniquely distinguish the mass-energy equivalence effects (gravitational redshift and second order Doppler shift) in such a setting, we devise a dressing protocol using an additional nuclear spin state. We then analyze the dynamical interplay between photon-mediated interactions and gravitational redshift and show that such interplay can lead to entanglement generation and frequency synchronization dynamics. In the regime where all atomic spins synchronize, we show the synchronization time depends on the initial entanglement of the state and can be used as a proxy of its metrological gain compared to a classical state. Our work opens new possibilities for exploring the effects of general relativity on quantum coherence and entanglement in OLC experiments. △ Less

Submitted 3 March, 2025; v1 submitted 6 June, 2024; originally announced June 2024.

Comments: 8+8 pages, 5+1 figures

Journal ref: Phys. Rev. Lett. 134, 093201 (2025)

Feasibility of State Space Models for Network Traffic Generation

Authors: Andrew Chu, Xi Jiang, Shinan Liu, Arjun Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster

Abstract: Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these collections is limited due to privacy concerns, data staleness, and low representativeness. While methods for generating data to augment collections exist, they often fall short in replicating the quality of rea… ▽ More Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these collections is limited due to privacy concerns, data staleness, and low representativeness. While methods for generating data to augment collections exist, they often fall short in replicating the quality of real-world traffic In this paper, we i) survey the evolution of traffic simulators/generators and ii) propose the use of state-space models, specifically Mamba, for packet-level, synthetic network trace generation by modeling it as an unsupervised sequence generation problem. Early evaluation shows that state-space models can generate synthetic network traffic with higher statistical similarity to real traffic than the state-of-the-art. Our approach thus has the potential to reliably generate realistic, informative synthetic network traces for downstream tasks. △ Less

Submitted 4 June, 2024; originally announced June 2024.

Comments: 7 pages, 3 figures, 4 tables

Design of Stickbug: a Six-Armed Precision Pollination Robot

Authors: Trevor Smith, Madhav Rijal, Christopher Tatsch, R. Michael Butts, Jared Beard, R. Tyler Cook, Andy Chu, Jason Gross, Yu Gu

Abstract: This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug ac… ▽ More This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 50% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug's software and design files. △ Less

Submitted 4 April, 2024; originally announced April 2024.

Comments: 7 pages, 7 figures

Hamiltonian Engineering of collective XYZ spin models in an optical cavity

Authors: Chengyi Luo, Haoqing Zhang, Anjun Chu, Chitose Maruko, Ana Maria Rey, James K. Thompson

Abstract: Quantum simulation using synthetic quantum systems offers unique opportunities to explore open questions in many-body physics and a path for the generation of useful entangled states. Nevertheless, so far many quantum simulators have been fundamentally limited in the models they can mimic. Here, we are able to realize an all-to-all interaction with arbitrary quadratic Hamiltonian or effectively an… ▽ More Quantum simulation using synthetic quantum systems offers unique opportunities to explore open questions in many-body physics and a path for the generation of useful entangled states. Nevertheless, so far many quantum simulators have been fundamentally limited in the models they can mimic. Here, we are able to realize an all-to-all interaction with arbitrary quadratic Hamiltonian or effectively an infinite range tunable Heisenberg XYZ model. This is accomplished by engineering cavity-mediated four-photon interactions between 700 rubidium atoms in which we harness a pair of momentum states as the effective pseudo spin or qubit degree of freedom. Using this capability we realize for the first time the so-called two-axis counter-twisting model at the mean-field level. The versatility of our platform to include more than two relevant momentum states, combined with the flexibility of the simulated Hamiltonians by adding cavity tones opens rich opportunities for quantum simulation and quantum sensing in matter-wave interferometers and other quantum sensors such as optical clocks and magnetometers △ Less

Submitted 2 July, 2024; v1 submitted 29 February, 2024; originally announced February 2024.