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1. Greenbag, Worksheets.

Author: Reitan, Henry M. and Coole, Walter A.
Abstract: The "Greenbag" worksheets are an essential part of the "Greenbook System"--an integrated sequence of five programs designed for professional development for anyone interested in, or involved in, a career in higher education. In this system, each student develops an individualized "Greenbook" containing critical data and procedural information for an actual or intended professional position. Using the "Purplebook" as a procedural guide, the student uses worksheets provided in the "Greenbag" for "Greenbook" construction. These worksheets are used by the student to detail long-range goals, to list tasks necessary for the achievement of these goals, to outline strategies, to note information related to an examination of sample institutions, communities, jobs, and so forth. In addition to the worksheets, this document contains a description of methods used for task analysis, examples of functional analyses of two professional organizations, an example task analysis and standards of competence for organization members and officers, and example operation analyses for parliamentary procedure and for listening. (DC)
Published: 2024

2. Do the Effects Persist? An Examination of Long-Term Effects after Students Leave Turnaround Schools. EdWorkingPaper No. 24-1041

Author: Annenberg Institute for School Reform at Brown University, Lam D. Pham, Sean P. Corcoran, Gary T. Henry, and Ron Zimmer
Abstract: Whole-school reforms have received widespread attention, but a critical limitation of the current literature is the lack of evidence around whether these extensive and costly interventions improve students' long-term outcomes after they leave reform schools. Leveraging Tennessee's statewide turnaround reforms, we use difference-in-differences models to estimate the effect of attending a turnaround middle school on student outcomes in high school, including test scores, attendance, chronic absenteeism, disciplinary actions, drop out, and high school graduation. We find little evidence to support improved long-run student outcomes -- mostly null effects that are nearly zero in magnitude. Our results contribute to a broad call for educational researchers to examine whether school reforms meaningfully affect student outcomes beyond short-term improvements in test scores.
Published: 2024

3. The Cosmic Evolution Early Release Science Survey (CEERS)

Author: Finkelstein, Steven L., Bagley, Micaela B., Haro, Pablo Arrabal, Dickinson, Mark, Ferguson, Henry C., Kartaltepe, Jeyhan S., Kocevski, Dale D., Koekemoer, Anton M., Lotz, Jennifer M., Papovich, Casey, Perez-Gonzalez, Pablo G., Pirzkal, Nor, Somerville, Rachel S., Trump, Jonathan R., Yang, Guang, Yung, L. Y. Aaron, Fontana, Adriano, Grazian, Andrea, Grogin, Norman A., Kewley, Lisa J., Kirkpatrick, Allison, Larson, Rebecca L., Pentericci, Laura, Ravindranath, Swara, Wilkins, Stephen M., Almaini, Omar, Amorin, Ricardo O., Barro, Guillermo, Bhatawdekar, Rachana, Bisigello, Laura, Brooks, Madisyn, Buitrago, Fernando, Calabro, Antonello, Castellano, Marco, Cheng, Yingjie, Cleri, Nikko J., Cole, Justin W., Cooper, M. C., Cooper, Olivia R., Costantin, Luca, Cox, Isa G., Croton, Darren, Daddi, Emanuele, Davis, Kelcey, Dekel, Avishai, Elbaz, David, Fernandez, Vital, Fujimoto, Seiji, Gandolfi, Giovanni, Gardner, Jonathan P., Gawiser, Eric, Giavalisco, Mauro, Gomez-Guijarro, Carlos, Guo, Yuchen, Gupta, Ansh R., Hathi, Nimish P., Harish, Santosh, Henry, Aurelien, Hirschmann, Michaela, Hu, Weida, Hutchison, Taylor A., Iyer, Kartheik G., Jaskot, Anne E., Jha, Saurabh W., Jung, Intae, Kokorev, Vasily, Kurczynski, Peter, Leung, Gene C. K., Llerena, Mario, Long, Arianna S., Lucas, Ray A., Lu, Shiying, McGrath, Elizabeth J., McIntosh, Daniel H., Merlin, Emiliano, Morales, Alexa M., Napolitano, Lorenzo, Pacucci, Fabio, Pandya, Viraj, Rafelski, Marc, Rodighiero, Giulia, Rose, Caitlin, Santini, Paola, Seille, Lise-Marie, Simons, Raymond C., Shen, Lu, Straughn, Amber N., Tacchella, Sandro, Vanderhoof, Brittany N., Vega-Ferrero, Jesus, Weiner, Benjamin J., Willmer, Christopher N. A., Zhu, Peixin, Bell, Eric F., Wuyts, Stijn, Holwerda, Benne W., Wang, Xin, Wang, Weichen, and Zavala, Jorge A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables
Published: 2025

4. More vaquita porpoises survive than expected

Author: L Rojas-Bracho, B Taylor, C Booth, L Thomas, A Jaramillo-Legorreta, E Nieto-García, G Cárdenas Hinojosa, J Barlow, SL Mesnick, T Gerrodette, P Olson, A Henry, H Rizo, E Hidalgo-Pla, and A Bonilla-Garzón
Subjects: Zoology, QL1-991, Botany, QK1-989
Abstract: In 2018, it was estimated that fewer than 20 of Mexico’s endemic vaquita porpoise Phocoena sinus remained, and the species was declining by 47% yr-1. Entanglement in gillnets is the sole threat to the species, and since the last population size estimate, gillnetting has increased in the small area where most vaquitas remain—a 12 × 24 km area in the Gulf of California near San Felipe, Mexico. We conducted research efforts in 2019 and 2021 in that area to estimate the minimum numbers of adults and calves and look for any signs that vaquitas are unhealthy. Through expert elicitation, we estimated between 7 and 15 unique individuals were seen in 2019 and 5-13 were seen in 2021. Calves were seen in both years, and all vaquitas appeared healthy. Population projections from the last full survey indicated that more vaquitas have survived than expected. We suggest that these surviving adult vaquitas may have learned to avoid entanglement in gillnets. These vaquitas and their calves provide hope that the species can survive. However, given the high levels of illegal gillnetting and the theft of equipment which hindered our monitoring efforts, and with only around 10 individuals remaining, survival can only be assured if vaquita habitat is made gillnet-free.
Published: 2022
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5. MLKAPS: Machine Learning and Adaptive Sampling for HPC Kernel Auto-tuning

Author: Jam, Mathys, Petit, Eric, Castro, Pablo de Oliveira, Defour, David, Henry, Greg, and Jalby, William
Subjects: Computer Science - Performance, Computer Science - Software Engineering
Abstract: Many High-Performance Computing (HPC) libraries rely on decision trees to select the best kernel hyperparameters at runtime,depending on the input and environment. However, finding optimized configurations for each input and environment is challengingand requires significant manual effort and computational resources. This paper presents MLKAPS, a tool that automates this task usingmachine learning and adaptive sampling techniques. MLKAPS generates decision trees that tune HPC kernels' design parameters toachieve efficient performance for any user input. MLKAPS scales to large input and design spaces, outperforming similar state-of-the-artauto-tuning tools in tuning time and mean speedup. We demonstrate the benefits of MLKAPS on the highly optimized Intel MKLdgetrf LU kernel and show that MLKAPS finds blindspots in the manual tuning of HPC experts. It improves over 85% of the inputswith a geomean speedup of x1.30. On the Intel MKL dgeqrf QR kernel, MLKAPS improves performance on 85% of the inputs with ageomean speedup of x1.18.
Published: 2025

6. From Bit to Block: Decoding on Erasure Channels

Author: Pfister, Henry D., Sprumont, Oscar, and Zémor, Gilles
Subjects: Computer Science - Information Theory
Abstract: We provide a general framework for bounding the block error threshold of a linear code $C\subseteq \mathbb{F}_2^N$ over the erasure channel in terms of its bit error threshold. Our approach relies on understanding the minimum support weight of any $r$-dimensional subcode of $C$, for all small values of $r$. As a proof of concept, we use our machinery to obtain a new proof of the celebrated result that Reed-Muller codes achieve capacity on the erasure channel with respect to block error probability.
Published: 2025

7. Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Author: Deschênes, Anthony, Georges, Rémi, Subakan, Cem, Ugulino, Bruna, Henry, Antoine, and Morin, Michael
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.
Published: 2025

8. Multiparticle quantum walks for distinguishing hard graphs

Author: Kasture, Sachin, Acheche, Shaheen, Henriet, Loic, and Henry, Louis-Paul
Subjects: Quantum Physics
Abstract: Quantum random walks have been shown to be powerful quantum algorithms for certain tasks on graphs like database searching, quantum simulations etc. In this work we focus on its applications for the graph isomorphism problem. In particular we look at how we can compare multi-particle quantum walks and well known classical WL tests and how quantum walks can be used to distinguish hard graphs like CFI graphs which k-WL tests cannot distinguish. We provide theoretical proofs and empirical results to show that a k-QW with input superposition states distinguishes k-CFI graphs. In addition we also prove that a k-1 QW with localized input states distinguishes k-CFI graphs. We also prove some additional results about strongly regular graphs (SRGs)., Comment: 11 pages, 6 figures
Published: 2025

9. KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs

Author: Zhou, Zelin, Conia, Simone, Lee, Daniel, Li, Min, Huang, Shenglei, Minhas, Umar Farooq, Potdar, Saloni, Xiao, Henry, and Li, Yunyao
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown that combining information from KGs in different languages aids either Knowledge Graph Completion (KGC), the task of predicting missing relations between entities, or Knowledge Graph Enhancement (KGE), the task of predicting missing textual information for entities. Although previous efforts have considered KGC and KGE as independent tasks, we hypothesize that they are interdependent and mutually beneficial. To this end, we introduce KG-TRICK, a novel sequence-to-sequence framework that unifies the tasks of textual and relational information completion for multilingual KGs. KG-TRICK demonstrates that: i) it is possible to unify the tasks of KGC and KGE into a single framework, and ii) combining textual information from multiple languages is beneficial to improve the completeness of a KG. As part of our contributions, we also introduce WikiKGE10++, the largest manually-curated benchmark for textual information completion of KGs, which features over 25,000 entities across 10 diverse languages., Comment: Camera ready for COLING 2025
Published: 2025

10. Deterministic printing and heterointegration of single colloidal quantum dot photon sources

Author: Guymon, Gregory G., Nguyen, Hao A., Sharp, David, Nguyen, Tommy, Lei, Henry, Ginger, David S., Fu, Kai-Mei C., Majumdar, Arka, Cossairt, Brandi M., and MacKenzie, J. Devin
Subjects: Physics - Optics, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract: Single nanoparticles are essential building blocks for next-generation quantum photonic technologies, however, scalable and deterministic heterointegration strategies have remained largely out of reach. Here, we present a new electrohydrodynamic (EHD) printing model that exploits nanoscale dielectrophoretics to precisely print single colloidal quantum dots (QDs) with accuracies allowing for fully-additive nanoscale photonics integration. Using colossal-shelled QDs solubilized in apolar solvents, this method overcomes continuum fluid surface energetics and stochastic limitations, achieving selective extraction and deposition of individual QDs at sub-zeptoliter volumes. Photoluminescence and autocorrelation function (g(2)) measurements confirm nanophotonic cavity-QD integration and the first single-photon emission from printed QDs. This additive, zero-waste nanomanufacturing process offers a scalable, sustainable pathway for heterointegrating nanomaterials down to the single particle level., Comment: Updated with correction to funding sources only
Published: 2025

11. A Coupled PFEM-DEM Model for Fluid-Granular Flows with Free-Surface Dynamics Applied to Landslides

Author: Leyssens, Thomas, Henry, Michel, Lambrechts, Jonathan, Legat, Vincent, and Remacle, Jean-François
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: Free surface and granular fluid mechanics problems combine the challenges of fluid dynamics with aspects of granular behaviour. This type of problem is particularly relevant in contexts such as the flow of sediments in rivers, the movement of granular soils in reservoirs, or the interactions between a fluid and granular materials in industrial processes such as silos. The numerical simulation of these phenomena is challenging because the solution depends not only on the multiple phases that strongly interact with each other, but also on the need to describe the geometric evolution of the different interfaces. This paper presents an approach to the simulation of fluid-granular phenomena involving strongly deforming free surfaces. The Discrete Element Method (DEM) is combined with the Particle Finite Element Method (PFEM) and the fluid-grain interface is treated by a two-way coupling between the two phases. The fluid-air interface is solved by a free surface model. The geometric and topological variations are therefore naturally provided by the full Lagrangian description of all phases. The approach is validated on benchmark test cases such as two-phase dam failures and then applied to a real landslide problem.
Published: 2025

12. Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap

Author: Zhang, Weizhi, Bei, Yuanchen, Yang, Liangwei, Zou, Henry Peng, Zhou, Peilin, Liu, Aiwei, Li, Yinghui, Chen, Hao, Wang, Jianling, Wang, Yu, Huang, Feiran, Zhou, Sheng, Bu, Jiajun, Lin, Allen, Caverlee, James, Karray, Fakhri, King, Irwin, and Yu, Philip S.
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract: Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research community on CSR still lacks a comprehensive review and reflection in this field. Based on this, in this paper, we stand in the context of the era of large language models and provide a comprehensive review and discussion on the roadmap, related literature, and future directions of CSR. Specifically, we have conducted an exploration of the development path of how existing CSR utilizes information, from content features, graph relations, and domain information, to the world knowledge possessed by large language models, aiming to provide new insights for both the research and industrial communities on CSR. Related resources of cold-start recommendations are collected and continuously updated for the community in https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation.
Published: 2025

13. Efficiency enhancement in regenerative amplifier free-electron lasers using a tapered undulator

Author: Freund, Henry, van der Slot, Peter, and O'Shea, Patrick
Subjects: Physics - Optics, Physics - Accelerator Physics
Abstract: Recent progress in short wavelength free-electron lasers extending from the extreme ultraviolet through the x-ray regime have opened new avenues for industrial and research applications. A high-gain/low-Q oscillator, i.e., a regenerative amplifier free-electron laser (RAFEL), is one possible concept. In this paper, we present the first analysis of efficiency enhancement in a RAFEL with a long, tapered undulator line. For the present analysis, we consider a high average power EUV RAFEL at 13.5 nm and show that the performance exceeds that of single-pass, tapered self-amplified spontaneous emission. This points the way to high power tapered undulator x-ray RAFELs., Comment: 6 pages, 8 figures
Published: 2025

14. Excess Ultraviolet Emission at High Galactic Latitudes: A New Horizons View

Author: Murthy, Jayant, Shull, J. Michael, Postman, Marc, Parker, Joel Wm., Redfield, Seth, Cunningham, Nathaniel, Gladstone, G. Randall, Pineau, Jon P., Brandt, Pontus, Verbiscer, Anne J., Singer, Kelsi N., Weaver, Harold A., Henry, Richard C., and Stern, S. Alan
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present new observations of the cosmic ultraviolet background (CUVB) at high Galactic latitudes ($|b| > 40^{\circ}$), made using the Alice UV spectrograph on board the New Horizons spacecraft. These observations were taken at about 57 AU from the Sun, outside much of the foreground emission affecting previous missions, and allowed a new determination of the spectrum of the CUVB between 912 -- 1100~\AA\ and 1400 -- 1800~\AA. We found a linear correlation between the CUVB and the Planck E(B~-~V) with offsets at zero-reddening of $221 \pm 11$ photon units at 1000~\AA\ and $264 \pm 24$ \photu\ at 1500~\AA\ ($4.4 \pm 0.2$ nW m$^{-2}$ sr$^{-1}$ at 1000~\AA\ and $5.3 \pm 0.5$ nW m$^{-2}$ sr$^{-1}$ at 1500~\AA). The former is the first firm detection of the offset in the range 912 -- 1100 \AA\ while the latter result confirms previous results from \galex, showing that there is little emission from the Solar System from 1400 -- 1800 \AA. About half of the offset may be explained by known sources (the integrated light of unresolved galaxies, unresolved stars, emission from ionized gas, and two-photon emission from warm hydrogen in the halo) with the source of the remaining emission as yet unidentified. There is no detectable emission below the Lyman limit with an upper limit of $3.2 \pm 3.0$ photon units., Comment: Accepted in AJ. 19 pages, 16 figures, 8 tables
Published: 2025

15. Dual Diffusion for Unified Image Generation and Understanding

Author: Li, Zijie, Li, Henry, Shi, Yichun, Farimani, Amir Barati, Kluger, Yuval, Yang, Linjie, and Wang, Peng
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities. Inspired by the multimodal diffusion transformer (MM-DiT) and recent advances in discrete diffusion language modeling, we leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly under a single loss function, which is back-propagated through both branches of the diffusion transformer. The resulting model is highly flexible and capable of a wide range of tasks including image generation, captioning, and visual question answering. Our model attained competitive performance compared to recent unified image understanding and generation models, demonstrating the potential of multimodal diffusion modeling as a promising alternative to autoregressive next-token prediction models.
Published: 2024

16. The Planar Lattice Two-Neighbor Graph Percolates

Author: Coupier, David, Henry, Benoît, Jahnel, Benedikt, and Köppl, Jonas
Subjects: Mathematics - Probability
Abstract: The k-neighbor graph is a directed percolation model on the hypercubic lattice Z d in which each vertex independently picks exactly k of its 2d nearest neighbors at random, and we open directed edges towards those. We prove that the 2-neighbor graph percolates on Z 2 , i.e., that the origin is connected to infinity with positive probability. The proof rests on duality, an exploration algorithm, a comparison to i.i.d. bond percolation under constraints as well as enhancement arguments. As a byproduct, we show that i.i.d. bond percolation with forbidden local patterns has a strictly larger percolation threshold than 1/2. Additionally, our main result provides further evidence that, in low dimensions, less variability is beneficial for percolation.
Published: 2024

17. Unraveling the temperature-dependent spin-polarized electron transport in iron via spin-wave Doppler shift

Author: Solano, José, Rossi, Quentin, Robert, Jerome, Lenertz, Marc, Henry, Yves, Gobaut, Benoit, Halley, David, and Bailleul, Mattieu
Subjects: Condensed Matter - Materials Science, Condensed Matter - Other Condensed Matter
Abstract: An electric current flowing in a ferromagnetic metal carries spin angular momentum, i.e. it is spin-polarized. Here, we measure the spin-wave Doppler shift induced by the transfer of angular momentum from the diffusive spin-polarized electric current onto coherent spin waves in epitaxial MgO/Fe/MgO thin films. We follow this Doppler shift as function of the temperature and determine that the degree of spin-polarization of the current increases from 77$\%$ to 86$\%$ when cooling the device from 303K down to 10K. Interpreting these measurements within the two-current model, we separate the contributions from electron-surface, electron-phonon and electron-magnon scatterings to the spin-dependent resistivity of Fe.
Published: 2024

18. Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues

Author: Xie, Henry J., Zhang, Jinghan, Zhang, Xinhao, and Liu, Kunpeng
Subjects: Computer Science - Computation and Language
Abstract: In recent years, Large Language Models (LLMs) have become increasingly more powerful in their ability to complete complex tasks. One such task in which LLMs are often employed is scoring, i.e., assigning a numerical value from a certain scale to a subject. In this paper, we strive to understand how LLMs score, specifically in the context of empathy scoring. We develop a novel and comprehensive framework for investigating how effective LLMs are at measuring and scoring empathy of responses in dialogues, and what methods can be employed to deepen our understanding of LLM scoring. Our strategy is to approximate the performance of state-of-the-art and fine-tuned LLMs with explicit and explainable features. We train classifiers using various features of dialogues including embeddings, the Motivational Interviewing Treatment Integrity (MITI) Code, a set of explicit subfactors of empathy as proposed by LLMs, and a combination of the MITI Code and the explicit subfactors. Our results show that when only using embeddings, it is possible to achieve performance close to that of generic LLMs, and when utilizing the MITI Code and explicit subfactors scored by an LLM, the trained classifiers can closely match the performance of fine-tuned LLMs. We employ feature selection methods to derive the most crucial features in the process of empathy scoring. Our work provides a new perspective toward understanding LLM empathy scoring and helps the LLM community explore the potential of LLM scoring in social science studies., Comment: Accepted by IEEE BigData 2024
Published: 2024

19. Many-body atomic response functions of xenon and germanium for leading-order sub-GeV dark matter-electron interactions in effective field theory

Author: Liu, C. -P., Pandey, Mukesh K., Singh, Lakhwinder, Wu, Chih-Pan, Chen, Jiunn-Wei, Chi, Hsin-Chang, and Wong, Henry T.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract: Direct searches of dark matter candidates with mass energies less than 1 GeV is an active research field. The energy depositions are comparable to the scale of atomic, molecular, or condensed matter systems, therefore many-body physics plays an important role in understanding the detector's response in dark matter scattering. We present in this work a comprehensive data set of atomic response functions for xenon and germanium with 12.2 and 80 eV energy thresholds, respectively, using the (multiconfiguration) relativistic random phase approximation. This approach takes into account the relativistic, exchange, and correlation effects in one self-consistent framework, and is benchmarked successfully by photoabsorption data from thresholds to 30 keV with $\lesssim5\%$ errors. Comparisons with our previous and some other independent particle approaches in literature are made. It is also found that the spin-dependent (SD) response has significant difference from the spin-independent (SI) one such that the dark matter SD and SI interactions with electrons can be distinguished in unpolarized scattering, which is typical for direct search detectors. Finally, the exclusion limits set by current experiments are updated with our new results., Comment: 25 pages,9 figures
Published: 2024

20. Unifying Tree-Reweighted Belief Propagation and Mean Field for Tracking Extended Targets

Author: Ma, Weizhen, Jing, Zhongliang, Dong, Peng, and Leung, Henry
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: This paper proposes a unified tree-reweighted belief propagation (BP) and mean field (MF) approach for scalable detection and tracking of extended targets within the framework of factor graph. The factor graph is partitioned into a BP region and an MF region so that the messages in each region are updated according to the corresponding region rules. The BP region exploits the tree-reweighted BP, which offers improved convergence than the standard BP for graphs with massive cycles, to resolve data association. The MF region approximates the posterior densities of the measurement rate, kinematic state and extent. For linear Gaussian target models and gamma Gaussian inverse Wishart distributed state density, the unified approach provides a closed-form recursion for the state density. Hence, the proposed algorithm is more efficient than particle-based BP algorithms for extended target tracking. This method also avoids measurement clustering and gating since it solves the data association problem in a probabilistic fashion. We compare the proposed approach with algorithms such as the Poisson multi-Bernoulli mixture filter and the BP-based Poisson multi-Bernoulli filter. Simulation results demonstrate that the proposed algorithm achieves enhanced tracking performance., Comment: 29 pages, 5 figures, 1 table
Published: 2024

21. Stability of instantaneous pressures in an Eulerian finite element method for moving boundary flow problems

Author: Olshanskii, Maxim and von Wahl, Henry
Subjects: Mathematics - Numerical Analysis, 65M12, 65M60, 65M85
Abstract: This paper focuses on identifying the cause and proposing a remedy for the problem of spurious pressure oscillations in a sharp-interface immersed boundary finite element method for incompressible flow problems in moving domains. The numerical method belongs to the class of Eulerian unfitted finite element methods. It employs a cutFEM discretization in space and a standard BDF time-stepping scheme, enabled by a discrete extension of the solution from the physical domain into the ambient space using ghost-penalty stabilization. To investigate the origin of spurious temporal pressure oscillations, we revisit a finite element stability analysis for the steady domain case and extend it to derive a stability estimate for the pressure in the $L^\infty(L^2)$-norm that is uniform with respect to discretization parameters. By identifying where the arguments fail in the context of a moving domain, we propose a variant of the method that ensures unconditional stability of the instantaneous pressure. As a result, the modified method eliminates spurious pressure oscillations. We also present extensive numerical studies aimed at illustrating our findings and exploring the effects of fluid viscosity, geometry approximation, mass conservation, discretization and stabilization parameters, and the choice of finite element spaces on the occurrence and magnitude of spurious temporal pressure oscillations. The results of the experiments demonstrate a significant improvement in the robustness and accuracy of the proposed method compared to existing approaches.
Published: 2024

22. Solving Inverse Problems via Diffusion Optimal Control

Author: Li, Henry and Pereira, Marcus
Subjects: Computer Science - Machine Learning
Abstract: Existing approaches to diffusion-based inverse problem solvers frame the signal recovery task as a probabilistic sampling episode, where the solution is drawn from the desired posterior distribution. This framework suffers from several critical drawbacks, including the intractability of the conditional likelihood function, strict dependence on the score network approximation, and poor $\mathbf{x}_0$ prediction quality. We demonstrate that these limitations can be sidestepped by reframing the generative process as a discrete optimal control episode. We derive a diffusion-based optimal controller inspired by the iterative Linear Quadratic Regulator (iLQR) algorithm. This framework is fully general and able to handle any differentiable forward measurement operator, including super-resolution, inpainting, Gaussian deblurring, nonlinear deblurring, and even highly nonlinear neural classifiers. Furthermore, we show that the idealized posterior sampling equation can be recovered as a special case of our algorithm. We then evaluate our method against a selection of neural inverse problem solvers, and establish a new baseline in image reconstruction with inverse problems., Comment: Presented at NeurIPS 2024
Published: 2024

23. The Tractability Border of Reachability in Simple Vector Addition Systems with States

Author: Chistikov, Dmitry, Czerwiński, Wojciech, Mazowiecki, Filip, Orlikowski, Łukasz, Sinclair-Banks, Henry, and Węgrzycki, Karol
Subjects: Computer Science - Formal Languages and Automata Theory, Computer Science - Logic in Computer Science
Abstract: Vector Addition Systems with States (VASS), equivalent to Petri nets, are a well-established model of concurrency. The central algorithmic challenge in VASS is the reachability problem: is there a run from a given starting state and counter values to a given target state and counter values? When the input is encoded in binary, reachability is computationally intractable: even in dimension one, it is NP-hard. In this paper, we comprehensively characterise the tractability border of the problem when the input is encoded in unary. For our main result, we prove that reachability is NP-hard in unary encoded 3-VASS, even when structure is heavily restricted to be a simple linear path scheme. This improves upon a recent result of Czerwi\'nski and Orlikowski (2022), in both the number of counters and expressiveness of the considered model, as well as answers open questions of Englert, Lazi\'c, and Totzke (2016) and Leroux (2021). The underlying graph structure of a simple linear path scheme (SLPS) is just a path with self-loops at each node. We also study the exceedingly weak model of computation that is SPLS with counter updates in {-1,0,+1}. Here, we show that reachability is NP-hard when the dimension is bounded by O(\alpha(k)), where \alpha is the inverse Ackermann function and k bounds the size of the SLPS. We complement our result by presenting a polynomial-time algorithm that decides reachability in 2-SLPS when the initial and target configurations are specified in binary. To achieve this, we show that reachability in such instances is well-structured: all loops, except perhaps for a constant number, are taken either polynomially many times or almost maximally. This extends the main result of Englert, Lazi\'c, and Totzke (2016) who showed the problem is in NL when the initial and target configurations are specified in unary., Comment: Full version of FOCS'24 paper. 60 pages, 16 figures
Published: 2024
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24. Large tunable Josephson diode effect in a side-contacted topological-insulator-nanowire junction

Author: Nikodem, Ella, Schluck, Jakob, Geier, Max, Papaj, Michal, Legg, Henry F., Feng, Junya, Bagchi, Mahasweta, Fu, Liang, and Ando, Yoichi
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: A Josephson diode passes current with zero resistance in one direction but is resistive in the other direction. While such an effect has been observed in several platforms, a large and tunable Josephson diode effect has been rare. Here we report that a simple device consisting of a topological-insulator (TI) nanowire side-contacted by superconductors to form a lateral Josephson junction presents a large diode effect with the efficiency $\eta$ reaching 0.3 when a parallel magnetic field $B_{||}$ is applied. Interestingly, the sign and the magnitude of $\eta$ is found to be tunable not only by $B_{||}$ but also by the back-gate voltage. This novel diode effect can be understood by modeling the system as a nano-SQUID, in which the top and bottom surfaces of the TI nanowire each form a line junction and $B_{||}$ creates a magnetic flux to thread the SQUID loop. This model further shows that the observed diode effect is accompanied by the emergence of topological superconductivity in TI-nanowire-based Josephson junction., Comment: Slightly revised version, 29 pages total; 23 pages of main text with 5 figures, 6 pages of supplement with 8 figures. The raw data and codes are available at the online depository Zenodo with the identifier https://doi.org/10.5281/zenodo.14576330
Published: 2024

25. A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation

Author: Hosseini, Ryien, Simini, Filippo, Vishwanath, Venkatram, and Hoffmann, Henry
Subjects: Computer Science - Machine Learning
Abstract: Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for generative models suitable for applications such as data augmentation, obfuscation, and anomaly detection. However, there are few generative techniques that handle continuously changing temporal graph data; existing work largely relies on augmenting static graphs with additional temporal information to model dynamic interactions between nodes. In this work, we propose a fundamentally different approach: We instead directly model interactions as a joint probability of an edge forming between two nodes at a given time. This allows us to autoregressively generate new synthetic dynamic graphs in a largely assumption free, scalable, and inductive manner. We formalize this approach as DG-Gen, a generative framework for continuous time dynamic graphs, and demonstrate its effectiveness over five datasets. Our experiments demonstrate that DG-Gen not only generates higher fidelity graphs compared to traditional methods but also significantly advances link prediction tasks., Comment: To appear at AAAI-25
Published: 2024

26. Segmentation of arbitrary features in very high resolution remote sensing imagery

Author: Cording, Henry, Plancherel, Yves, and Brito-Parada, Pablo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Very high resolution (VHR) mapping through remote sensing (RS) imagery presents a new opportunity to inform decision-making and sustainable practices in countless domains. Efficient processing of big VHR data requires automated tools applicable to numerous geographic regions and features. Contemporary RS studies address this challenge by employing deep learning (DL) models for specific datasets or features, which limits their applicability across contexts. The present research aims to overcome this limitation by introducing EcoMapper, a scalable solution to segment arbitrary features in VHR RS imagery. EcoMapper fully automates processing of geospatial data, DL model training, and inference. Models trained with EcoMapper successfully segmented two distinct features in a real-world UAV dataset, achieving scores competitive with prior studies which employed context-specific models. To evaluate EcoMapper, many additional models were trained on permutations of principal field survey characteristics (FSCs). A relationship was discovered allowing derivation of optimal ground sampling distance from feature size, termed Cording Index (CI). A comprehensive methodology for field surveys was developed to ensure DL methods can be applied effectively to collected data. The EcoMapper code accompanying this work is available at https://github.com/hcording/ecomapper ., Comment: Main article: 18 pages, 9 figures; appendix: 17 pages, 9 figures
Published: 2024

27. Experimental Demonstration of Logical Magic State Distillation

Author: Rodriguez, Pedro Sales, Robinson, John M., Jepsen, Paul Niklas, He, Zhiyang, Duckering, Casey, Zhao, Chen, Wu, Kai-Hsin, Campo, Joseph, Bagnall, Kevin, Kwon, Minho, Karolyshyn, Thomas, Weinberg, Phillip, Cain, Madelyn, Evered, Simon J., Geim, Alexandra A., Kalinowski, Marcin, Li, Sophie H., Manovitz, Tom, Amato-Grill, Jesse, Basham, James I., Bernstein, Liane, Braverman, Boris, Bylinskii, Alexei, Choukri, Adam, DeAngelo, Robert, Fang, Fang, Fieweger, Connor, Frederick, Paige, Haines, David, Hamdan, Majd, Hammett, Julian, Hsu, Ning, Hu, Ming-Guang, Huber, Florian, Jia, Ningyuan, Kedar, Dhruv, Kornjača, Milan, Liu, Fangli, Long, John, Lopatin, Jonathan, Lopes, Pedro L. S., Luo, Xiu-Zhe, Macrì, Tommaso, Marković, Ognjen, Martínez-Martínez, Luis A., Meng, Xianmei, Ostermann, Stefan, Ostroumov, Evgeny, Paquette, David, Qiang, Zexuan, Shofman, Vadim, Singh, Anshuman, Singh, Manuj, Sinha, Nandan, Thoreen, Henry, Wan, Noel, Wang, Yiping, Waxman-Lenz, Daniel, Wong, Tak, Wurtz, Jonathan, Zhdanov, Andrii, Zheng, Laurent, Greiner, Markus, Keesling, Alexander, Gemelke, Nathan, Vuletić, Vladan, Kitagawa, Takuya, Wang, Sheng-Tao, Bluvstein, Dolev, Lukin, Mikhail D., Lukin, Alexander, Zhou, Hengyun, and Cantú, Sergio H.
Subjects: Quantum Physics, Physics - Atomic Physics
Abstract: Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily implemented on such encoded qubits is often constrained, necessitating the use of special resource states known as 'magic states' to implement universal, classically hard circuits. A key method to prepare high-fidelity magic states is to perform 'distillation', creating them from multiple lower fidelity inputs. Here we present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer. Our approach makes use of a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel. We demonstrate the distillation of magic states encoded in d=3 and d=5 color codes, observing improvements of the logical fidelity of the output magic states compared to the input logical magic states. These experiments demonstrate a key building block of universal fault-tolerant quantum computation, and represent an important step towards large-scale logical quantum processors., Comment: 8+11 pages, 4+4 figures
Published: 2024

28. Demonstrating dynamic surface codes

Author: Eickbusch, Alec, McEwen, Matt, Sivak, Volodymyr, Bourassa, Alexandre, Atalaya, Juan, Claes, Jahan, Kafri, Dvir, Gidney, Craig, Warren, Christopher W., Gross, Jonathan, Opremcak, Alex, Miao, Nicholas Zobrist Kevin C., Roberts, Gabrielle, Satzinger, Kevin J., Bengtsson, Andreas, Neeley, Matthew, Livingston, William P., Greene, Alex, Rajeev, Acharya, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Frank, Arute, Arya, Kunal, Asfaw, Abraham, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Jenna, Bovaird, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Tim, Burger, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Alexander, Crook, L., Curtin, Ben, Das, Sayan, Barba, Alexander Del Toro, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya K., Dunsworth, Andrew, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gonzalo, Garcia, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Dietrich, Graumann, Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hiltermann, Reno, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Amir, Karamlou, H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Tiano, Lange-Dei, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Wing, Li, Yan, Lill, Alexander T., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Anthony, Megrant, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Oas, Logan, Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Rosenberg, Eliott, Rossi, Elizabeth, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Sergey, Vdovichev, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Yaxing, Zhang, Zhu, Ningfeng, Boixo, Sergio, Kelly, Julian, Smelyanskiy, Vadim, Neven, Hartmut, Bacon, Dave, Chen, Zijun, Klimov, Paul V., Roushan, Pedram, Neill, Charles, Chen, Yu, and Morvan, Alexis
Subjects: Quantum Physics
Abstract: A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information
Published: 2024

29. Scaling and logic in the color code on a superconducting quantum processor

Author: Lacroix, Nathan, Bourassa, Alexandre, Heras, Francisco J. H., Zhang, Lei M., Bausch, Johannes, Senior, Andrew W., Edlich, Thomas, Shutty, Noah, Sivak, Volodymyr, Bengtsson, Andreas, McEwen, Matt, Higgott, Oscar, Kafri, Dvir, Claes, Jahan, Morvan, Alexis, Chen, Zijun, Zalcman, Adam, Madhuk, Sid, Acharya, Rajeev, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Blackwell, Sam, Bovaird, Jenna, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hiltermann, Reno, Hilton, Jeremy, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Li, Yin, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Y., Oas, Logan, Oliver, William D., Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Resnick, Rachel, Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Rossi, Elizabeth, Roushan, Pedram, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Kohli, Pushmeet, Davies, Alex, Boixo, Sergio, Kelly, Julian, Jones, Cody, Gidney, Craig, and Satzinger, Kevin J.
Subjects: Quantum Physics
Abstract: Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.
Published: 2024

30. Discovery of dual 'little red dots' indicates excess clustering on kilo-parsec scales

Author: Tanaka, Takumi S., Silverman, John D., Shimasaku, Kazuhiro, Arita, Junya, Akins, Hollis B., Inayoshi, Kohei, Ding, Xuheng, Onoue, Masafusa, Liu, Zhaoxuan, Casey, Caitlin M., Lambrides, Erini, Kokorev, Vasily, Jin, Shuowen, Faisst, Andreas L., Drakos, Nicole, Shen, Yue, Li, Junyao, Zhuang, Mingyang, Fei, Qinyue, Ito, Kei, Ren, Wenke, Matsui, Suin, Ando, Makoto, Hatano, Shun, Fujii, Michiko S., Kartaltepe, Jeyhan S., Koekemoer, Anton M., Liu, Daizhong, McCracken, Henry Joy, Rhodes, Jason, Robertson, Brant E., Franco, Maximilien, Andika, Irham T., Cloonan, Aidan P., Fan, Xiaohui, Gozaliasl, Ghassem, Harish, Santosh, Hayward, Christopher C., Huertas-Company, Marc, Kakkad, Darshan, Kinugawa, Tomoya, Roy, Namrata, Shuntov, Marko, Talia, Margherita, Toft, Sune, Vijayan, Aswin P., and Zhang, Yiyang
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: ``Little Red Dots'' (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST). They are characterized by a red color in the rest-frame optical band, compact morphology, and broad Balmer emission lines (${\rm FWHM} \gtrsim 1000~{\rm km\,s^{-1}}$) that suggest an AGN nature. Using a method of pixel-by-pixel color selection and relaxing the compactness criteria, we identify three of the first dual LRD candidates in the COSMOS-Web survey with projected separations of $0.\!\!^{\prime\prime}2-0.\!\!^{\prime\prime}4$ (1-2 pkpc at their photometric redshifts). A comparison between existing LRD samples and mock data reveals that the projected separations of these dual LRD candidates are unlikely to result from chance projections of objects at different redshifts. In one case (CW-B5-15958), the dual LRD includes two bright sources ($m_{\rm F444W}=24.3$ and $24.8$) with characteristic V-shape spectral energy distribution (SEDs) and photometric redshifts consistent with each other. We find that CW-B5-15958 has a faint off-centered component and a companion galaxy. In the other two dual systems, the brighter LRD exhibits a V-shape SED, while the fainter LRD ($m_{\rm F444W}\gtrsim26$) is undetected in both F115W and F150W. These discoveries suggest that the angular auto-correlation function (ACF) of LRDs exhibits a significant excess ($\sim3\times10^2$ times) on sub-arcsec (kilo-parsec) separations compared to the extrapolation of a power-law ACF of JWST-found AGNs measured over $10^{\prime\prime}-100^{\prime\prime}$. Follow-up spectroscopic confirmation of their redshifts and the construction of a larger sample are essential to advance our understanding of the evolution of supermassive black holes and the importance of mergers in the early universe., Comment: 12 pages, 5 figures, and 1 table. Comments are welcome
Published: 2024

31. Quantum entropy and QCD factorization for low-$Q^2$ $\nu$DIS

Author: Bloss, Henry, Kriesten, Brandon, and Hobbs, T. J.
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Theory, Nuclear Theory, Quantum Physics
Abstract: Deeply inelastic scattering (DIS) is an essential process for exploring the structure of visible matter and testing the standard model. At the same time, the theoretical interpretation of DIS measurements depends on QCD factorization theorems whose validity deteriorates at the lower values of $Q^2$ and $W^2$ typical of neutrino DIS in accelerator-based oscillation searches. For this reason, progress in understanding the origin and limits of QCD factorization is invaluable to the accuracy and precision of predictions for these upcoming neutrino experiments. In these short proceedings, we introduce a novel approach based on the quantum entropy associated with continuous distributions in QCD, using it to characterize the limits of factorization theorems relevant for the description of neutrino DIS. This work suggests an additional avenue for dissecting factorization-breaking dynamics through the quantum entropy, which could also play a role in quantum simulations of related systems., Comment: 4 pages, 2 figures; Contribution to the 25th International Workshop on Neutrinos from Accelerators (NuFact 2024)
Published: 2024

32. Tidal contributions to the full gravitational waveform to the second-and-a-half post-Newtonian order

Author: Dones, Eve, Henry, Quentin, and Bernard, Laura
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: This paper describes the different steps to include the adiabatic tidal effects to the gravitational waveform amplitude for quasi-circular non-spinning compact binaries up to the second-and-a-half post-Newtonian (PN) order. The amplitude, that relates the two gravitational wave polarizations, is decomposed onto the basis of spin-weighted spherical harmonics of spin -2, parametrized by the two numbers $(\ell,m)$, where the modes of the waveform correspond to the coefficients of the decomposition. These modes are readily computed from the radiative multipole moments. They can be expressed in a PN-expanded form as well as in a factorized form, suitable to be directly included in effective-one-body models to describe more accurately the waveform of binary neutron stars. We also provide the energy flux and phasing evolution in time and frequency domain. The results presented in this article are collected in an ancillary file., Comment: 34 pages, 1 Table
Published: 2024

33. Measurement of $CP$ asymmetry in $B_s^0 \to D_s^{\mp} K^{\pm}$ decays

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. 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Subjects: High Energy Physics - Experiment
Abstract: A measurement of the $CP$-violating parameters in $B_s^0 \to D_s^{\mp} K^{\pm}$ decays is reported, based on the analysis of proton-proton collision data collected by the LHCb experiment corresponding to an integrated luminosity of $6\,\mathrm{fb}^{-1}$ at a centre-of-mass energy of $13 \,\mathrm{TeV}$. The measured parameters are $C_f = 0.791 \pm 0.061 \pm 0.022$, $A_f^{\Delta\Gamma} = -0.051 \pm 0.134 \pm 0.058$, $A_{\overline{f}}^{\Delta\Gamma} = -0.303 \pm 0.125 \pm 0.055$, $S_f = -0.571 \pm 0.084 \pm 0.023$ and $S_{\overline{f}} = -0.503 \pm 0.084 \pm 0.025$, where the first uncertainty is statistical and the second systematic. Together with the value of the Bs mixing phase $-2\beta_s$, these parameters are used to obtain a measurement of the CKM angle $\gamma$ equal to $ (74\pm12)^\circ$ modulo $180^{\circ}$, where the uncertainty contains both statistical and systematic contributions. This result is combined with the previous LHCb measurement in this channel using $3\,\mathrm{fb}^{-1}$ resulting in a determination of $\gamma= (81^{+12}_{-11})^\circ$., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3575/ (LHCb public pages)
Published: 2024

34. Measurement of $CP$ asymmetries in $\Lambda_b^0\to ph^{-}$ decays

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bottalico, E., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Brandenburg, J. D., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonincontri, L., Marcos, M. Burgos, Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. 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X., Xu, A., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A search for $CP$ violation in $\Lambda_b^0\rightarrow pK^-$ and $\Lambda_b^0\rightarrow p\pi^-$ decays is presented using the full Run 1 and Run 2 data samples of $pp$ collisions collected with the LHCb detector, corresponding to an integrated luminosity of 9 $\mathrm{fb}^{-1}$ at center-of-mass energies of 7, 8, and 13 TeV. For the Run 2 data sample, the $CP$-violating asymmetries are measured to be $A_{CP}^{pK^-} = (-1.4 \pm 0.7 \pm 0.4)\%$ and $A_{CP}^{p\pi^-} = (0.4 \pm 0.9 \pm 0.4)\%$, where the first uncertainty is statistical and the second is systematic. Following significant improvements in the evaluation of systematic uncertainties compared to the previous LHCb measurement, the Run 1 dataset is reanalyzed to update the corresponding results. When combining the Run 2 and updated Run 1 measurements, the final results are found to be $A_{CP}^{pK^-} = (-1.1 \pm 0.7 \pm 0.4)\%$ and $A_{CP}^{p\pi^-} = (0.2 \pm 0.8 \pm 0.4)\%$, constituting the most precise measurements of these asymmetries to date., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3533/ (LHCb public pages)
Published: 2024

35. Compositional Generalization Across Distributional Shifts with Sparse Tree Operations

Author: Soulos, Paul, Conklin, Henry, Opper, Mattia, Smolensky, Paul, Gao, Jianfeng, and Fernandez, Roland
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Neural networks continue to struggle with compositional generalization, and this issue is exacerbated by a lack of massive pre-training. One successful approach for developing neural systems which exhibit human-like compositional generalization is \textit{hybrid} neurosymbolic techniques. However, these techniques run into the core issues that plague symbolic approaches to AI: scalability and flexibility. The reason for this failure is that at their core, hybrid neurosymbolic models perform symbolic computation and relegate the scalable and flexible neural computation to parameterizing a symbolic system. We investigate a \textit{unified} neurosymbolic system where transformations in the network can be interpreted simultaneously as both symbolic and neural computation. We extend a unified neurosymbolic architecture called the Differentiable Tree Machine in two central ways. First, we significantly increase the model's efficiency through the use of sparse vector representations of symbolic structures. Second, we enable its application beyond the restricted set of tree2tree problems to the more general class of seq2seq problems. The improved model retains its prior generalization capabilities and, since there is a fully neural path through the network, avoids the pitfalls of other neurosymbolic techniques that elevate symbolic computation over neural computation., Comment: NeurIPS 2024. Code available at https://github.com/psoulos/sdtm
Published: 2024

36. FlexCache: Flexible Approximate Cache System for Video Diffusion

Author: Sun, Desen, Tian, Henry, Lu, Tim, and Liu, Sihang
Subjects: Computer Science - Multimedia, Computer Science - Machine Learning
Abstract: Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a single video. While prior research has addressed the computational overhead in text-to-image diffusion models, the techniques developed are not directly suitable for video diffusion models due to the significantly larger cache requirements and enhanced computational demands associated with video generation. We present FlexCache, a flexible approximate cache system that addresses the challenges in two main designs. First, we compress the caches before saving them to storage. Our compression strategy can reduce 6.7 times consumption on average. Then we find that the approximate cache system can achieve higher hit rate and computation savings by decoupling the object and background. We further design a tailored cache replacement policy to support the two techniques mentioned above better. Through our evaluation, FlexCache reaches 1.26 times higher throughput and 25% lower cost compared to the state-of-the-art diffusion approximate cache system.
Published: 2024

37. Black Hole Merger Rates in AGN: contribution from gas-captured binaries

Author: Rowan, Connar, Whitehead, Henry, and Kocsis, Bence
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: It has been suggested that merging black hole (BH) binaries in active galactic nucleus (AGN) discs formed through two-body scatterings via the gas-capture process may explain a significant fraction of BH mergers in AGN and a non-negligible contribution to the observed rate from LIGO-VIRGO-KAGRA. We perform Monte Carlo simulations of BH and binary BH formation, evolution and mergers across the observed AGN mass function using a novel physically motivated treatment for the gas-capture process derived from hydrodynamical simulations of BH-BH encounters in AGN and varying assumptions on the AGN disc physics. The results suggest that gas-captured binaries could result in merger rates of 0.73 - 7.1Gpc$^{-3}$yr$^{-1}$. Most mergers take place near the outer boundary of the accretion disk, but this may be subject to change when migration is considered. The BH merger rate in the AGN channel in the Universe is dominated by AGN with supermassive BH masses on the order of 10$^{7} M_\odot$ , with 90% of mergers occurring in the range 10$^{6} M_\odot$ - 10$^{8} M_\odot$ . The merging mass distribution is flatter than the initial BH mass power law by a factor $\Delta \xi$ = 1.1 to 1.2, as larger BHs can align with the disc and successfully form binaries more efficiently. Similarly, the merging mass ratio distribution is flatter, therefore the AGN channel could easily explain the high mass and unequal mass ratio detections such as GW190521 and GW190814. When modelling the BH binary formation process using a simpler dynamical friction treatment, we observe very similar results, where the primary bottleneck is the alignment time with the disk. We find the most influential parameters on the rates are the anticipated number of BHs and their mass function. We conclude that AGN remain an important channel for consideration, particularly for gravitational wave detections involving one or two high mass BHs., Comment: 14 pages, 11 figures
Published: 2024

38. Test of lepton flavour universality with $B^+ \to K^+\pi^+\pi^-\ell^+\ell^-$ decays

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bottalico, E., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Brandenburg, J. D., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonincontri, L., Marcos, M. Burgos, Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Govorkova, E., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Ordonez, G. Loachamin, Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pilato, R. N., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stefaniak, M., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Tork, T., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, van Eldik, J., Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xing, T. X., Xu, A., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: The first test of lepton flavour universality between muons and electrons using $B^+ \to K^+\pi^+\pi^-\ell^+\ell^-$ ($\ell=e,\mu$) decays is presented. The measurement is performed with data from proton-proton collisions collected by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of $9\mathrm{fb}^{-1}$. The ratio of branching fractions between $B^+ \to K^+\pi^+\pi^-e^+e^-$ and $B^+ \to K^+\pi^+\pi^-\mu^+\mu^-$decays is measured in the dilepton invariant-mass-squared range $1.1 < q^2 < 7.0~\mathrm{GeV}^2/c^4$ and is found to be $R_{K\pi\pi}^{-1} = 1.31^{+0.18}_{-0.17} \;(\mathrm{stat})\;^{+0.12}_{-0.09} \;(\mathrm{syst})$, in agreement with the Standard Model prediction. The first observation of the $B^+ \to K^+\pi^+\pi^-e^+e^-$ decay is also reported., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1606/ (LHCb public pages)
Published: 2024

39. On-the-Fly Interrogation of Mobile Passive Sensors from the Fusion of Optical and Radar Data

Author: Djilani, Ali Hadj, Henry, Dominique, Pons, Patrick, and Aubert, Herve
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: In this paper a new method based on the fusion of optical and radar data is proposed to detect and remotely interrogate mobile and passive sensors. The sensors are detected in real time by using an optical camera, while their remote reading is carried out on the fly using a Frequency-Modulated Continuous-Wave radar. The proof-of-concept is established from the interrogation of a microfluidic temperature sensor placed on a conveyer belt.
Published: 2024
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40. Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate

Author: Li, Fengpei, Chen, Haoxian, Lin, Jiahe, Gupta, Arkin, Tan, Xiaowei, Xu, Gang, Nevmyvaka, Yuriy, Capponi, Agostino, and Lam, Henry
Subjects: Statistics - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Quantitative Finance - Pricing of Securities
Abstract: Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though this introduces challenges in understanding the resulting errors.We introduce a Prediction-Enhanced Monte Carlo (PEMC) framework where we leverage machine learning prediction as control variates, thus maintaining unbiased evaluations instead of the direct use of ML predictors. Traditional control variate methods require knowledge of means and focus on per-sample variance reduction. In contrast, PEMC aims at overall cost-aware variance reduction, eliminating the need for mean knowledge. PEMC leverages pre-trained neural architectures to construct effective control variates and replaces computationally expensive sample-path generation with efficient neural network evaluations. This allows PEMC to address scenarios where no good control variates are known. We showcase the efficacy of PEMC through two production-grade exotic option-pricing problems: swaption pricing in HJM model and the variance swap pricing in a stochastic local volatility model.
Published: 2024

41. AI and the Future of Digital Public Squares

Author: Goldberg, Beth, Acosta-Navas, Diana, Bakker, Michiel, Beacock, Ian, Botvinick, Matt, Buch, Prateek, DiResta, Renée, Donthi, Nandika, Fast, Nathanael, Iyer, Ravi, Jalan, Zaria, Konya, Andrew, Danciu, Grace Kwak, Landemore, Hélène, Marwick, Alice, Miller, Carl, Ovadya, Aviv, Saltz, Emily, Schirch, Lisa, Shalom, Dalit, Siddarth, Divya, Sieker, Felix, Small, Christopher, Stray, Jonathan, Tang, Audrey, Tessler, Michael Henry, and Zhang, Amy
Subjects: Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract: Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI., Comment: 40 pages, 5 figures
Published: 2024

42. Search for $D^0$ meson decays to $\pi^+ \pi^- e^+ e^-$ and $K^+ K^- e^+ e^-$ final states

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bottalico, E., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Brandenburg, J. D., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonincontri, L., Marcos, M. Burgos, Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Govorkova, E., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Ordonez, G. Loachamin, Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Moeser, L., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pilato, R. N., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stefaniak, M., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Tork, T., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, van Eldik, J., Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xing, T. X., Xu, A., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A search for $D^0$ meson decays to the $\pi^+\pi^-e^+e^-$ and $K^+K^-e^+e^-$ final states is reported using a sample of proton-proton collisions collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb$^{-1}$. The decay $D^0 \rightarrow \pi^+\pi^-e^+e^-$ is observed for the first time when requiring that the two electrons are consistent with coming from the decay of a $\phi$ or $\rho^0/\omega$ meson. The corresponding branching fractions are measured relative to the $D^0 \rightarrow K^-\pi^-[e^+e^-]_{\rho^0/\omega}$ decay, where the two electrons are consistent with coming from the decay of a $\rho^0$ or $\omega$ meson. No evidence is found for the $D^0 \rightarrow K^+K^-e^+e^-$ decay and world-best limits are set on its branching fraction. The results are compared to, and found to be consistent with, the branching fractions of the $D^0 \rightarrow \pi^+\pi^-\mu^+\mu^-$ and $D^0 \rightarrow K^+K^-\mu^+\mu^-$ decays recently measured by LHCb and confirm lepton universality at the current precision., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1611/ (LHCb public pages)
Published: 2024

43. Cluster Decomposition for Improved Erasure Decoding of Quantum LDPC Codes

Author: Yao, Hanwen, Gökduman, Mert, and Pfister, Henry D.
Subjects: Computer Science - Information Theory, Quantum Physics
Abstract: We introduce a new erasure decoder that applies to arbitrary quantum LDPC codes. Dubbed the cluster decoder, it generalizes the decomposition idea of Vertical-Horizontal (VH) decoding introduced by Connelly et al. in 2022. Like the VH decoder, the idea is to first run the peeling decoder and then post-process the resulting stopping set. The cluster decoder breaks the stopping set into a tree of clusters which can be solved sequentially via Gaussian Elimination (GE). By allowing clusters of unconstrained size, this decoder achieves maximum-likelihood (ML) performance with reduced complexity compared with full GE. When GE is applied only to clusters whose sizes are less than a constant, the performance is degraded but the complexity becomes linear in the block length. Our simulation results show that, for hypergraph product codes, the cluster decoder with constant cluster size achieves near-ML performance similar to VH decoding in the low-erasure-rate regime. For the general quantum LDPC codes we studied, the cluster decoder can be used to estimate the ML performance curve with reduced complexity over a wide range of erasure rates., Comment: 12 pages, 8 figures
Published: 2024

44. Coherent3D: Coherent 3D Portrait Video Reconstruction via Triplane Fusion

Author: Wang, Shengze, Li, Xueting, Liu, Chao, Chan, Matthew, Stengel, Michael, Fuchs, Henry, De Mello, Shalini, and Nagano, Koki
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Recent breakthroughs in single-image 3D portrait reconstruction have enabled telepresence systems to stream 3D portrait videos from a single camera in real-time, democratizing telepresence. However, per-frame 3D reconstruction exhibits temporal inconsistency and forgets the user's appearance. On the other hand, self-reenactment methods can render coherent 3D portraits by driving a 3D avatar built from a single reference image, but fail to faithfully preserve the user's per-frame appearance (e.g., instantaneous facial expression and lighting). As a result, none of these two frameworks is an ideal solution for democratized 3D telepresence. In this work, we address this dilemma and propose a novel solution that maintains both coherent identity and dynamic per-frame appearance to enable the best possible realism. To this end, we propose a new fusion-based method that takes the best of both worlds by fusing a canonical 3D prior from a reference view with dynamic appearance from per-frame input views, producing temporally stable 3D videos with faithful reconstruction of the user's per-frame appearance. Trained only using synthetic data produced by an expression-conditioned 3D GAN, our encoder-based method achieves both state-of-the-art 3D reconstruction and temporal consistency on in-studio and in-the-wild datasets. https://research.nvidia.com/labs/amri/projects/coherent3d, Comment: arXiv admin note: substantial text overlap with arXiv:2405.00794
Published: 2024

45. BLADE: Single-view Body Mesh Learning through Accurate Depth Estimation

Author: Wang, Shengze, Li, Jiefeng, Li, Tianye, Yuan, Ye, Fuchs, Henry, Nagano, Koki, De Mello, Shalini, and Stengel, Michael
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pose and 2D alignment at the same time. Error is mainly introduced by inaccurate perspective projection heuristically derived from orthographic parameters. To resolve this long-standing challenge, we present our method BLADE which accurately recovers perspective parameters from a single image without heuristic assumptions. We start from the inverse relationship between perspective distortion and the person's Z-translation Tz, and we show that Tz can be reliably estimated from the image. We then discuss the important role of Tz for accurate human mesh recovery estimated from close-range images. Finally, we show that, once Tz and the 3D human mesh are estimated, one can accurately recover the focal length and full 3D translation. Extensive experiments on standard benchmarks and real-world close-range images show that our method is the first to accurately recover projection parameters from a single image, and consequently attain state-of-the-art accuracy on 3D pose estimation and 2D alignment for a wide range of images. https://research.nvidia.com/labs/amri/projects/blade/
Published: 2024

46. Early Results from GLASS-JWST. XXV. Electron Density in the Interstellar Medium at $0.7\lesssim z\lesssim 9.3$ with NIRSpec High-resolution Spectroscopy

Author: Li, Sijia, Wang, Xin, Chen, Yuguang, Jones, Tucker, Treu, Tommaso, Glazebrook, Karl, He, Xianlong, Henry, Alaina, Meng, Xiao-Lei, Morishita, Takahiro, Roberts-Borsani, Guido, Yang, Lilan, Yu, Hao-Ran, Calabrò, Antonello, Castellano, Marco, Leethochawalit, Nicha, Metha, Benjamin, Nanayakkara, Themiya, Roy, Namrata, and Vulcani, Benedetta
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The electron density (${n_{\rm e}}$) of the interstellar medium (ISM) in star-forming galaxies is intimately linked to star formation and ionization condition. Using the high-resolution spectra obtained from the JWST NIRSpec micro shutter assembly (MSA) as part of the GLASS-JWST program, we have assembled the largest sample to date (34 galaxies) with individual ${n_{\rm e}}$ measurements derived from the [OII] $\lambda\lambda$3726,29 and/or [SII] $\lambda\lambda$6718,32 doublets at $0.7\lesssim z\lesssim 9.3$. The gravitational lensing magnification by the foreground Abell~2744 cluster allows us to probe ${n_{\rm e}}$ in galaxies with stellar masses ($M_{*}$) down to $\simeq 10^{7.5} M_\odot$ across the entire redshift range. Our analysis reveals that the [OII] flux ratios are marginally anti-correlated with specific star formation rate (sSFR) within a 1-$\sigma$ confidence interval, whereas the [SII] flux ratios show no significant correlation with sSFR. Despite clear correlation between sSFR and redshift within our sample, we find no apparent redshift evolution of ${n_{\rm e}}$ at $z \simeq 1-9$. Our dataset also includes 13 galaxies where ${n_{\rm e}}$ can be measured from both [OII] and [SII]. Contrary to findings at lower redshifts, we observe considerable scatter in ${n_{\rm e}}$ measurements from [OII] and [SII], indicating a complex gaseous environment with significant variations in ${n_{\rm e}}$ in high-redshift galaxies. This work highlights the unique capability of JWST NIRSpec/MSA high-resolution spectroscopy to characterize the detailed physical properties of the ISM in individual high-redshift galaxies., Comment: 15 pages, 7 figures, 2 tables; Accepted for publication in ApJL
Published: 2024

47. Two Distinct Populations of Dark Comets Delineated by Orbits and Sizes

Author: Seligman, Darryl Z., Farnocchia, Davide, Micheli, Marco, Hainaut, Olivier R., Hsieh, Henry H., Feinstein, Adina D., Chesley, Steven R., Taylor, Aster G., Masiero, Joseph, and Meech, Karen J.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: Small bodies are capable of delivering essential prerequisites for the development of life, such as volatiles and organics, to the terrestrial planets. For example, empirical evidence suggests that water was delivered to the Earth by hydrated planetesimals from distant regions of the Solar System. Recently, several morphologically inactive near-Earth objects (NEOs) were reported to experience significant nongravitational accelerations inconsistent with radiation-based effects, and possibly explained by volatile-driven outgassing. However, these "dark comets" display no evidence of comae in archival images, which are the defining feature of cometary activity. Here we report detections of nongravitational accelerations on seven additional objects previously classified as inactive (doubling the population) that could also be explainable by asymmetric mass loss. A detailed search of archival survey and targeted data rendered no detection of dust activity in any of these objects in individual or stacked images. We calculate dust production limits of $\sim10$, $0.1$, and $0.1$ kg s$^{-1}$ for 1998 FR$_{11}$, 2001 ME$_{1}$, and 2003 RM with these data, indicating little or no dust surrounding the objects during the observations. This set of dark comets reveals the delineation between two distinct populations: larger, "outer" dark comets on eccentric orbits that are end members of a continuum in activity level of comets, and smaller, "inner" dark comets on near-circular orbits that could signify a new population. These objects may trace various stages in the life cycle of a previously undetected, but potentially numerous, volatile-rich population that may have provided essential material to the Earth., Comment: 12 pages, 11 figures, Author's version of Proc. Natl. Acad. Sci. Vol. 121 No. 51 published at https://www.pnas.org/doi/10.1073/pnas.2406424121
Published: 2024
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48. Topological columnar nano-SQUID based on a 3D topological insulator

Author: Nikodem, Ella, Schluck, Jakob, Legg, Henry F., Geier, Max, Papaj, Michal, Bagchi, Mahasweta, Fu, Liang, and Ando, Yoichi
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Superconductivity
Abstract: Finding an ideal platform to generate non-Abelian Majorana zero-modes (MZMs) is a crucial first step for topological quantum computing. A 3D topological insulator (TI) is promising for this purpose due to the spin-momentum-locked surface state. Here we propose a novel yet simple TI platform that gives rise to a robust topological phase and can realize a well-defined fermion parity necessary for quantum computing. It consists of a bulk-insulating rectangular TI nanowire laterally sandwiched by two superconductors. In this structure, the top and bottom surfaces individually work as SNS line junctions, forming a nanometer-scale columnar SQUID in which the nanowire cross-section defines the threading flux $\Phi$ in axial magnetic fields. We demonstrate that a TI device of this structure indeed presents SQUID-type oscillations of the critical current $I_c$ as a function of $\Phi$ with period $\Phi_{0}^s=\frac{h}{2e}$. It can be tuned by a back gate to attain a full $I_c$ suppression and then a nearly-full recovery as a function of $\Phi$, an indication that the superconductivity is completely surface-dominated. Our theory shows that, when the two junctions are asymmetric, a robust topological phase hosting MZMs occurs periodically for $(n-\frac{1}{2})\Phi_{0}^s < \Phi < (n+\frac{1}{2})\Phi_{0}^s$ with odd-integer $n$., Comment: Slightly revised version, 22 pages total; 9 pages of main text with 4 figures, 13 pages of supplement with 11 figures. The raw data and codes are available at the online depository Zenodo with the identifier https://doi.org/10.5281/zenodo.14331680
Published: 2024

49. Non-Normal Diffusion Models

Author: Li, Henry
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments. This reveals a previously unconsidered parameter in the design of diffusion models: the distribution of the diffusion step $\Delta x_k := x_{k} - x_{k + 1}$. This parameter is implicitly set by default to be normally distributed in most diffusion models. By lifting this assumption, we generalize the framework for designing diffusion models and establish an expanded class of diffusion processes with greater flexibility in the choice of loss function used during training. We demonstrate the effectiveness of these models on density estimation and generative modeling tasks on standard image datasets, and show that different choices of the distribution of $\Delta x_k$ result in qualitatively different generated samples.
Published: 2024

50. Boosting Alignment for Post-Unlearning Text-to-Image Generative Models

Author: Ko, Myeongseob, Li, Henry, Wang, Zhun, Patsenker, Jonathan, Wang, Jiachen T., Li, Qinbin, Jin, Ming, Song, Dawn, and Jia, Ruoxi
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. Driven by these concerns, machine unlearning has become crucial to effectively purge undesirable knowledge from models. While existing literature has studied various unlearning techniques, these often suffer from either poor unlearning quality or degradation in text-image alignment after unlearning, due to the competitive nature of these objectives. To address these challenges, we propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives. We further derive the characterization of such an update. In addition, we design procedures to strategically diversify the unlearning and remaining datasets to boost performance improvement. Our evaluation demonstrates that our method effectively removes target classes from recent diffusion-based generative models and concepts from stable diffusion models while maintaining close alignment with the models' original trained states, thus outperforming state-of-the-art baselines. Our code will be made available at \url{https://github.com/reds-lab/Restricted_gradient_diversity_unlearning.git}., Comment: 22 pages, The Thirty-Eighth Annual Conference on Neural Information Processing Systems
Published: 2024

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