Tootfinder

Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing
Yanran Wu, Inez Hua, Yi Ding
https://arxiv.org/abs/2506.22773 …

Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing
Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlooks where and when water stress is more severe. To fill in this gap, we present SCARF, the first general framework that evaluates water impact of computing by factoring in both spatial and temporal variations in water stress. SCARF calculates an Adjusted Water Impact (AWI) metric that considers both consumption volume…

What is "Spatial" about Spatial Computing?
Yibo Wang, Yuhan Luo, Janghee Cho, Junnan Yu
https://arxiv.org/abs/2508.20477 https://arxiv.org/pdf/25…

What is "Spatial" about Spatial Computing?
Recent advancements in geographic information systems and mixed reality technologies have positioned spatial computing as a transformative paradigm in computational science. However, the field remains conceptually fragmented, with diverse interpretations across disciplines like Human-Computer Interaction, Geographic Information Science, and Computer Science, which hinders a comprehensive understanding of spatial computing and poses challenges for its coherent advancement and interdisciplinary i…

Computing Multi-Lumps in Nonlinearity-Managed Spatial-Symmetric Dispersive Framework
Sudhir Singh, Tripathi P, Manikandan K, Sakkaravarthi K
https://arxiv.org/abs/2506.23391

Computing Multi-Lumps in Nonlinearity-Managed Spatial-Symmetric Dispersive Framework
We investigate the dynamics of multi-lump waves in a generalized spatial symmetric higher-dimensional dispersive water wave model using an analytical approach. This involves the construction of explicit solutions using Hirota's bilinear method and generalized polynomial expansions. The dynamical study shows that the multi-lump waves are non-interacting and reveal different geometrical patterns.

Spatial Regionalization: A Hybrid Quantum Computing Approach
Yunhan Chang, Amr Magdy, Federico M. Spedalieri, Ibrahim Sabek
https://arxiv.org/abs/2506.18799

Spatial Regionalization: A Hybrid Quantum Computing Approach
Quantum computing has shown significant potential to address complex optimization problems; however, its application remains confined to specific problems at limited scales. Spatial regionalization remains largely unexplored in quantum computing due to its complexity and large number of variables. In this paper, we introduce the first hybrid quantum-classical method to spatial regionalization by decomposing the problem into manageable subproblems, leveraging the strengths of both classical and …

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
Dirk Van Essendelft, Patrick Wingo, Terry Jordan, Ryan Smith, Wissam Saidi
https://arxiv.org/abs/2506.15875

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
We have developed a novel compiler called the Multiple-Architecture Compiler for Advanced Computing Hardware (MACH) designed specifically for massively-parallel, spatial, dataflow architectures like the Wafer Scale Engine. Additionally, MACH can execute code on traditional unified-memory devices. MACH addresses the complexities in compiling for spatial architectures through a conceptual Virtual Machine, a flexible domain-specific language, and a compiler that can lower high-level languages to m…

R\'ECITKIT: A Spatial Toolkit for Designing and Evaluating Human-Centered Immersive Data Narratives
Vidya Setlur, Samuel Ridet
https://arxiv.org/abs/2508.18670 https://

RÉCITKIT: A Spatial Toolkit for Designing and Evaluating Human-Centered Immersive Data Narratives
Spatial computing presents new opportunities for immersive data storytelling, yet there is limited guidance on how to build such experiences or adapt traditional narrative visualizations to this medium. We introduce a toolkit, RÉCITKIT for supporting spatial data narratives in head-mounted display (HMD) environments. The toolkit allows developers to create interactive dashboards, tag data attributes as spatial assets to 3D models and immersive scenes, generate text and audio narratives, enabli…

SEC filing: Augmodo, which is developing wearable tech for retail store employees, including Smartbadges that collect inventory data, raised $37.5M (Taylor Soper/GeekWire)
https://www.geekwire.com/2025/spatial-comp

Random Sampling over Spatial Range Joins
Daichi Amagata
https://arxiv.org/abs/2508.15070 https://arxiv.org/pdf/2508.15070

Random Sampling over Spatial Range Joins
Spatial range joins have many applications, including geographic information systems, location-based social networking services, neuroscience, and visualization. However, joins incur not only expensive computational costs but also too large result sets. A practical and reasonable approach to alleviating these issues is to return random samples of the join results. Although this is promising and sufficient for many applications involving spatial range joins, efficiently computing random samples …

TRMAC: A Time-Reversal-based MAC Protocol for Wireless Networks within Computing Packages
Ama Bandara, Abhijit Das, Fatima Rodriguez-Galan, Eduard Alarcon, Sergi Abadal
https://arxiv.org/abs/2506.19487

TRMAC: A Time-Reversal-based MAC Protocol for Wireless Networks within Computing Packages
As chiplet-based integration and many-core architectures become the norm in high-performance computing, on-chip wireless communication has emerged as a compelling alternative to traditional interconnects. However, scalable Medium Access Control (MAC) remains a fundamental challenge, particularly under dense traffic and limited spectral resources. This paper presents TRMAC, a novel cross-layer MAC protocol that exploits the spatial focusing capability of Time Reversal (TR) to enable multiple par…

Generation of polarization-entangled Bell states in monolithic photonic waveguides by leveraging intrinsic crystal properties
Trevor G. Vrckovnik, Dennis Arslan, Falk Eilenberger, Sebastian W. Schmitt
https://arxiv.org/abs/2506.21228

Generation of polarization-entangled Bell states in monolithic photonic waveguides by leveraging intrinsic crystal properties
Advanced photonic quantum technologies -- from quantum key distribution to quantum computing -- require on-chip sources of entangled photons that are both efficient and readily scalable. In this theoretical study, we demonstrate the generation of polarization-entangled Bell states in structurally simple waveguides by exploiting the intrinsic properties of nonlinear crystals. We thereby circumvent elaborate phase-matching strategies that commonly involve the spatial modulation of a waveguide's l…

Two-Timescale Dynamic Service Deployment and Task Scheduling with Spatiotemporal Collaboration in Mobile Edge Networks
Yang Li, Xing Zhang, Yunji Zhao, Wenbo Wang
https://arxiv.org/abs/2508.16293

Two-Timescale Dynamic Service Deployment and Task Scheduling with Spatiotemporal Collaboration in Mobile Edge Networks
Collaborative edge computing addresses the resource constraints of individual edge nodes by enabling resource sharing and task co-processing across multiple nodes. To fully leverage the advantages of collaborative edge computing, joint optimization of service deployment and task scheduling is necessary. Existing optimization methods insufficiently address the collaboration across spatial and temporal dimensions, which hinders their adaptability to the spatiotemporally varying nature of user dem…

Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
Zhuoqiang Guo, Runze Mao, Lijun Liu, Guangming Tan, Weile Jia, Zhi X. Chen
https://arxiv.org/abs/2508.18969

Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and temporal scales of the physical system. We optimize the supercritical flame simulation software DeepFlame -- which incorporates deep neural networks while retaining the real-fluid mechanical and chemical accuracy -- from three perspectives: parallel computing, computational efficiency, and I/O performance. Our highly…

An Optimal In-Situ Multipole Algorithm for the Isotropic Three-Point Correlation Functions
Wenjie Ju, Longlong Feng, Zhiqi Huang, Xin Sun, Weishan Zhu
https://arxiv.org/abs/2507.15209

An Optimal In-Situ Multipole Algorithm for the Isotropic Three-Point Correlation Functions
We present an optimised multipole algorithm for computing the three-point correlation function (3PCF), tailored for application to large-scale cosmological datasets. The algorithm builds on a $in\, situ$ interpretation of correlation functions, wherein spatial displacements are implemented via translation window functions. In Fourier space, these translations correspond to plane waves, whose decomposition into spherical harmonics naturally leads to a multipole expansion framework for the 3PCF. …

A multi-stage Bayesian approach to fit spatial point process models
Rachael Ren, Mevin B. Hooten, Toryn L. J. Schafer, Nicholas M. Calzada, Benjamin Hoose, Jamie N. Womble, Scott Gende
https://arxiv.org/abs/2508.02922

A multi-stage Bayesian approach to fit spatial point process models
Spatial point process (SPP) models are commonly used to analyze point pattern data, including presence-only data in ecology. Current methods for fitting these models are computationally expensive because they require numerical quadrature and algorithm supervision (i.e., tuning) in the Bayesian setting. We propose a flexible and efficient multi-stage recursive Bayesian approach to fitting SPP models that leverages parallel computing resources to estimate point process model coefficients and deri…

CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing
Xuesi Chen, Leo Han, Anvita Bhagavathula, Udit Gupta
https://arxiv.org/abs/2507.01145

CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing
Embodied carbon footprint modeling has become an area of growing interest due to its significant contribution to carbon emissions in computing. However, the deterministic nature of the existing models fail to account for the spatial and temporal variability in the semiconductor supply chain. The absence of uncertainty modeling limits system designers' ability to make informed, carbon-aware decisions. We introduce CarbonClarity, a probabilistic framework designed to model embodied carbon footpri…

Unveiling the link between quantum ghost imaging and Grover's quantum searching algorithm
Neelan Gounden, Fazilah Nothlawala, Paola C. Obando, Thomas Konrad, Andrew Forbes, Isaac Nape
https://arxiv.org/abs/2508.11296

Unveiling the link between quantum ghost imaging and Grover's quantum searching algorithm
Photonic quantum technologies have become pivotal in the implementation of communication, imaging and computing modalities. Among these applications, quantum ghost imaging (GI) exploits photon correlations to surpass classical limits, with recent advances in spatial-mode encoding and phase imaging. In parallel, all-optical computing offers powerful, passive-light processing capabilities. Here, we explore the intersection of these domains, revealing a conceptual and operational link between GI a…

Sizable superconducting gap and anisotropic chiral topological superconductivity in the Weyl semimetal PtBi$_2$
Xiaochun Huang, Lingxiao Zhao, Sebastian Schimmel, Julia Besproswanny, Patrick H\"artl, Christian Hess, Bernd B\"uchner, Matthias Bode
https://arxiv.org/abs/2507.13843

Sizable superconducting gap and anisotropic chiral topological superconductivity in the Weyl semimetal PtBi$_2$
Topological superconductors offer a fertile ground for realizing Majorana zero modes -- topologically protected, zero-energy quasiparticles that are resilient to local perturbations and hold great promise for fault-tolerant quantum computing. Recent studies have presented encouraging evidence for intrinsic topological superconductivity in the Weyl semimetal trigonal PtBi$_2$, hinting at a robust surface phase potentially stable beyond the McMillan limit. However, due to substantial spatial vari…

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
https://arxiv.org/abs/2508.10505

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, Li…

Convergence of linear programming hierarchies for Gibbs states of spin systems
Hamza Fawzi, Omar Fawzi
https://arxiv.org/abs/2506.06125 https://

Convergence of linear programming hierarchies for Gibbs states of spin systems
We consider the problem of computing expectation values of local functions under the Gibbs distribution of a spin system. In particular, we study two families of linear programming hierarchies for this problem. The first hierarchy imposes local spin flip equalities and has been considered in the bootstrap literature in high energy physics. For this hierarchy, we prove fast convergence under a spatial mixing (decay of correlations) condition. This condition is satisfied for example above the cri…

Wave Computing based on Dynamical Networks: Applications in Optimization Problems
Yunwen Liu, Jiang Xiao
https://arxiv.org/abs/2508.05014 https://arxiv.org…

Wave Computing based on Dynamical Networks: Applications in Optimization Problems
We develop a computing framework that leverages wave propagation within an interconnected network, where nodes and edges possess wave manipulation capabilities, such as frequency mixing or time delay. This computing paradigm can not only achieve intrinsic parallelism like existing works by the exploration of an exponential number of possibilities simultaneously with very small number of hardware units, but also extend this unique characteristic to a multidimensional space including spatial, tem…

Mixed Precision Photonic Computing with 3D Electronic-Photonic Integrated Circuits
Georgios Charalampous, Rui Chen, Mehmet Berkay On, Aslan Nasirov, Chun-Yi Cheng, Mahmoud AbdelGhany, Arka Majumdar, Ji Wang, Jennifer A. Black, Rajkumar Chinnakonda Kubendran, Caglar Oskay, Zhaojun Bai, Sam Palermo, Scott B. Papp, S. J. Ben Yoo
https://arxiv…

Mixed Precision Photonic Computing with 3D Electronic-Photonic Integrated Circuits
We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits), scalability (greater than 1024 by 1024), and massive parallelism (greater than 1 million operations) across the wavelength, spatial, and temporal domains at ultra-low power (less than 1 watt per PetaOPS). Monolithically integrated hybrid PCM-AlGaAs memory reso…

Holovibes: real-time ultrahigh-speed digital hologram rendering and short-time analysis
Marius Dubosc, Maxime Boy-Arnould, Jules Guillou, Titouan Gragnic, Arthur Courselle, Gustave Herv\'e, Alexis Pinson, Etienne Senigout, Bastien Gaulier, Simon Riou, Chlo\'e Magnier, No\'e Topeza, Oscar Morand, Thomas Xu, Samuel Goncalves, Edgar Delaporte, Adrien Langou, Paul Duhot, Julien Nicolle, Sacha Bellier, David Chemaly, Damien Didier, Philippe Bernet, Eliott Bouhana, Fabien Colmagr…

Holovibes: real-time ultrahigh-speed digital hologram rendering and short-time analysis
Real-time ultrahigh-speed rendering of digital holograms from high-bitrate interferogram streams demands robust parallel computing and efficient data handling with minimal latency. We present Holovibes, a high-performance software engine that enables real-time holographic image reconstruction and short-time analysis at unprecedented throughput. Holovibes integrates spatial demodulation techniques, such as Fresnel transformations and angular spectrum propagation, with temporal analysis methods i…

A scalable and programmable optical neural network in a time-synthetic dimension
Bei Wu, Yudong Ren, Rui Zhao, Haiyao Luo, Fujia Chen, Li Zhang, Hongsheng Chen, Yihao Yang
https://arxiv.org/abs/2507.02297

A scalable and programmable optical neural network in a time-synthetic dimension
Programmable optical neural networks (ONNs) can offer high-throughput and energy-efficient solutions for accelerating artificial intelligence (AI) computing. However, existing ONN architectures, typically based on cascaded unitary transformations such as Mach-Zehnder interferometer meshes, face inherent scalability limitations due to spatial encoding, which causes optical components and system complexity to scale quadratically with network size. A promising solution to this challenge is the use…