Tootfinder

Using LLMs to Advance the Cognitive Science of Collectives
Ilia Sucholutsky, Katherine M. Collins, Nori Jacoby, Bill D. Thompson, Robert D. Hawkins
https://arxiv.org/abs/2506.00052

Using LLMs to Advance the Cognitive Science of Collectives
LLMs are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

System-performance and cost modeling of Large Language Model training and inference
Wenzhe Guo, Joyjit Kundu, Uras Tos, Weijiang Kong, Giuliano Sisto, Timon Evenblij, Manu Perumkunnil
https://arxiv.org/abs/2507.02456

System-performance and cost modeling of Large Language Model training and inference
Large language models (LLMs), based on transformer architectures, have revolutionized numerous domains within artificial intelligence, science, and engineering due to their exceptional scalability and adaptability. However, the exponential growth in LLM size and complexity has outpaced advancements in compute capacity, memory bandwidth, network performance, and cost efficiency, posing significant challenges to their scalability on distributed systems. To address these limitations, alternative m…

Computational complexity of covering regular trees
Jan Bok, Ji\v{r}\'i Fiala, Nikola Jedli\v{c}kov\'a, Jan Kratochv\'il
https://arxiv.org/abs/2507.00564

Computational complexity of covering regular trees
A graph covering projection, also referred to as a locally bijective homomorphism, is a mapping between the vertices and edges of two graphs that preserves incidences and is a local bijection. This concept originates in topological graph theory but has also found applications in combinatorics and theoretical computer science. In this paper we consider undirected graphs in the most general setting -- graphs may contain multiple edges, loops, and semi-edges. This is in line with recent trends in …

"The Seven Capital Sins of Open Science"
1. Worshiping the 'age factor'
2. Ignoring the value of data reuse and complexity
3. Disrespecting other disciplines
4. Publishing data without a supplementary paper
5. Creating and maintaining a nightmare for machines
6. Refusing to support investment in general infrastructure
7. Creating data without a FAIR and explicit data stewardship plan.

The Seven Capital Sins of Open Science
Years ago I made a set of cartoons depicting what I consider the seven major roadblocks to effectively realise an Open Science (rather FAIR science actually). These were distilled from my intensive work on the European Open Science Cloud, GO FAIR and also my presidency of CODATA.

Towards a Science of Developer eXperience (DevX)
Benoit Combemale
https://arxiv.org/abs/2506.23715 https://arxiv.org/pdf/2506.23715…

Towards a Science of Developer eXperience (DevX)
As software continues to permeate nearly every facet of modern life, the complexity and ubiquity of digital services underscore the need for sustainable, effective, and inclusive software development practices. Although software engineering has made significant progress in technical challenges since its inception, the human experience of those involved in software creation, broadly defined as developers, remains underexplored. This column advocates for the formal recognition of Developer eXperi…

This https://arxiv.org/abs/2409.15683 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum DeepONet: Neural operators accelerated by quantum computing
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerni…

»Wie Mathematik hilft, schneller durch die Stadt zu kommen:
Am Complexity Science Hub in Wien wird errechnet, wie Pendelzeiten für alle Verkehrsteilnehmer durch andere Ampelschaltungen verkürzt werden könnten«
Dies kann sicherlich sich auch für Radfahrer oder Fußgänger sich lohnen. Ist es 1-zu-1 auch für andere Städte oder gar für Vor- so wie Kleinstädte und/oder ländliche Umgebung umsetzbar?
🗺️

Wie Mathematik hilft, schneller durch die Stadt zu kommen
Am Complexity Science Hub in Wien wird errechnet, wie Pendelzeiten für alle Verkehrsteilnehmer durch andere Ampelschaltungen verkürzt werden könnten.

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
Amro M. Farid, Amirreza Hosseini, John C. Little
https://arxiv.org/abs/2505.24046

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
A defining feature of twenty first century engineering challenges is their inherent complexity, demanding the convergence of knowledge across diverse disciplines. Establishing consistent methodological foundations for engineering systems remains a challenge -- one that both systems engineering and network science have sought to address. Model-based systems engineering (MBSE) has recently emerged as a practical, interdisciplinary approach for developing complex systems from concept through imple…

Our chapter on Urban #mobility is finally published, in the great Compendium of Urban Complexity!
https://link.springer.com/chapter/10.1007/978-3-031-82666-5_4…

Urban Mobility
In this chapter, we discuss urban mobility from a complexity science perspective. First, we give an overview of the datasets that enable this approach, such as mobile phone records, location-based social network traces, or GPS trajectories from sensors installed on...

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- On the Descriptive Complexity of Groups without Abelian Normal Subgroups
Joshua A. Grochow, Michael Levet

Counting Chemical Isomers with Multivariate Generating Functions
Rana Shojaei, Thilo Gross
https://arxiv.org/abs/2507.21713 https://arxiv.org/pdf/2507.2171…

Counting Chemical Isomers with Multivariate Generating Functions
Counting the number of isomers of a chemical molecule is one of the formative problems of graph theory. However, recent progress has been slow, and the problem has largely been ignored in modern network science. Here we provide an introduction to the mathematics of counting network structures and then use it to derive results for two new classes of molecules. In contrast to previously studied examples, these classes take additional chemical complexity into account and thus require the use of mu…

Universal Quantum Computational Spectroscopy on a Quantum Chip
Chonghao Zhai, Jinzhao Sun, Jieshan Huang, Jun Mao, Hongchang Bao, Siyuan Zhang, Qihuang Gong, Vlatko Vedral, Xiao Yuan, Jianwei Wang
https://arxiv.org/abs/2506.22418

Universal Quantum Computational Spectroscopy on a Quantum Chip
Spectroscopy underpins modern scientific discovery across diverse disciplines. While experimental spectroscopy probes material properties through scattering or radiation measurements, computational spectroscopy combines theoretical models with experimental data to predict spectral properties, essential for advancements in physics, chemistry, and materials science. However, quantum systems present unique challenges for computational spectroscopy due to their inherent complexity, and current quan…

MLRU : Multiscale Lightweight Residual UNETR with Attention for Efficient 3D Medical Image Segmentation
Nand Kumar Yadav (Santosh AI Research Lab, Department of Computer Science,Biomedical,Translational Sciences, Sanford School of Medicine University Of South Dakota, Vermillion, SD, USA.), Rodrigue Rizk (Santosh AI Research Lab, Department of Computer Science,Biomedical,Translational Sciences, Sanford School of Medicine University Of South Dakota, Vermillion, SD, USA.), Willium WC C…

MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation
Accurate and efficient medical image segmentation is crucial but challenging due to anatomical variability and high computational demands on volumetric data. Recent hybrid CNN-Transformer architectures achieve state-of-the-art results but add significant complexity. In this paper, we propose MLRU++, a Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency. It introduces two key innovations: a Lightweight Channel and Bottleneck…

This https://arxiv.org/abs/2410.12128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Multimodal Fusion with Relational Learning for Molecular Property Prediction
Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant challenges due to the intricate relationships among molecules and the limited chemical knowledge utilized during training. While contrastive learning is often employed to handle molecular relationships, its reliance on binary metrics is insufficient for capturing the complexity of these interactions. Multimodal fusion ha…

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
MSR Avinash (Presidency University, Bangalore), Ismael Lachheb (EPITA School of Engineering,Computer Science, Paris, France)
https://arxiv.org/abs/2507.15904

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
Visual Assessment of Cluster Tendency (VAT) is a widely used unsupervised technique to assess the presence of cluster structure in unlabeled datasets. However, its standard implementation suffers from significant performance limitations due to its O(n^2) time complexity and inefficient memory usage. In this work, we present Fast-VAT, a high-performance reimplementation of the VAT algorithm in Python, augmented with Numba's Just-In-Time (JIT) compilation and Cython's static typing and low-level …

OceanVive: An Immersive Visualization System for Communicating Complex Oceanic Phenomena
Yang Ouyang, Yuchen Wu, Xiyuan Wang, Laixin Xie, Weicong Cheng, Jianping Gan, Quan Li, Xiaojuan Ma
https://arxiv.org/abs/2507.17218

OceanVive: An Immersive Visualization System for Communicating Complex Oceanic Phenomena
Communicating the complexity of oceanic phenomena-such as hypoxia and acidification-poses a persistent challenge for marine science. Despite advances in sensing technologies and computational models, conventional formats like static visualizations and text-based reports often fall short in conveying the dynamics of ocean changes. To address this gap, we present OceanVive, an immersive and interactive visualization system that transforms complex ocean datasets into navigable spatial narratives. …

Thermal and Optical Characterization of Near-Earth Objects: Science Commissioning of the Recently Upgraded Mid-Infrared Camera MIRSI on the NASA Infrared Telescope Facility
Andy J. L\'opez-Oquendo, Joseph L. Hora, David E. Trilling, Howard A. Smith
https://arxiv.org/abs/2506.13943

Thermal and Optical Characterization of Near-Earth Objects: Science Commissioning of the Recently Upgraded Mid-Infrared Camera MIRSI on the NASA Infrared Telescope Facility
Mid-infrared (mid-IR) observations of Near-Earth Objects (NEOs) have historically been a valuable tool for understanding their physical properties. However, the current state of mid-IR instruments on ground-based telescopes places several limitations on performing thermal characterization of NEOs. The complexity of maintaining these instruments in operational conditions on telescopes has led to their decommissioning. Here, we present the first science commissioning observations out to 12.5 micr…

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.11295

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that …

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
Vladislav Esaulov, Jieyang Chen, Norbert Podhorszki, Fred Suter, Scott Klasky, Anu G Bourgeois, Lipeng Wan
https://arxiv.org/abs/2506.17084

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
In modern science, the growing complexity of large-scale projects has increased reliance on cross-facility workflows, where institutions share resources and expertise to accelerate discovery. These workflows often involve transferring massive data over wide-area networks. While high-speed networks like ESnet and data transfer services like Globus have improved data mobility, challenges remain. Large data volumes can strain bandwidth, TCP suffers from retransmissions due to packet loss, and trad…

A statistical complexity measure can differentiate Go/NoGo trials during a visual-motor task using human electroencephalogram data
Francisco Leandro P. Carlos, Maria Carla Navas, \'Icaro Rodolfo Soares Coelho Da Paz, Helena Bordini de Lucas, Maciel-Monteiro Ubirakitan, Marcelo Cairr\~ao Ara\'ujo Rodrigues, Moises Aguilar Domingo, Eva Herrera-Guti\'errez, Osvaldo A. Rosso, Luz Bavassi, Fernanda Selingardi Matias

A statistical complexity measure can differentiate Go/NoGo trials during a visual-motor task using human electroencephalogram data
Complexity is a ubiquitous concept in contemporary science and everyday life. A complex dynamical system is usually characterized by a blend of order and disorder, as well as emergent phenomena that often span multiple temporal and spatial scales. The information processes related to different cognitive processes in the brain can be studied in light of statistical differences based on complexity measures of the electrophysiological time series from different trial types. Recently, it has been d…

A not-too-simple solution to Goodman's new riddle of induction in the age of AI
Luigi Scorzato
https://arxiv.org/abs/2507.17212 https://

A not-too-simple solution to Goodman's new riddle of induction in the age of AI
I review the works of Gärdenfors (1990) and Scorzato (2013) and show that their combination provides an elegant solution of Goodman's new riddle of induction. The solution is based on two main ideas: (1) clarifying what is expected from a solution: understanding that philosophy of science is a science itself, with the same limitations and strengths as other scientific disciplines; (2) understanding that the concept of complexity of a model's assumptions and the concept of direct measurements m…

Understanding the complex morphology of a CME: multi-view analysis and numerical modeling
Cecilia Mac Cormack (Heliospheric Physics Laboratory, Heliophysics Science Division, NASA Goddard Space Flight Center), Abril Sahade (Heliospheric Physics Laboratory, Heliophysics Science Division, NASA Goddard Space Flight Center), Angelos Vourlidas (The Johns Hopkins University Applied Physics Laboratory), Phillip Hess (U.S. Naval Research Laboratory), Robin Colaninno (U.S. Naval Research Labora…

Understanding the complex morphology of a CME: multi-view analysis and numerical modeling
Although all coronal mass ejections (CMEs) that propagate into the heliosphere should contain a magnetic flux rope (MFR) component, the majority do not exhibit the expected white-light MFR morphology of a leading edge plus cavity. This different appearance could be the result of distortion of the internal magnetic structure, merging with other structures, or simply projection effects. These factors complicate the interpretation of CMEs. This complexity is exemplified by a CME observed on 28 Mar…

Exploring Micro Frontends: A Case Study Application in E-Commerce
Ricardo Hideki Hangai Kojo (University of S\~ao Paulo), Luiz Fernando Corte Real (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Thatiane de Oliveira Rosa (University of S\~ao Paulo, Federal Institute of Tocantins), Alfredo Goldman (University of S\~ao Paulo)

Exploring Micro Frontends: A Case Study Application in E-Commerce
In the micro frontends architectural style, the frontend is divided into smaller components, which can range from a simple button to an entire page. The goal is to improve scalability, resilience, and team independence, albeit at the cost of increased complexity and infrastructure demands. This paper seeks to understand when it is worth adopting micro frontends, particularly in the context of industry. To achieve this, we conducted an investigation into the state of the art of micro frontends, …

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- The Complexity of Pure Strategy Relevant Equilibria in Concurrent Games
Purandar Bhaduri

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar
https://arxiv.org/abs/2506.04584

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and…

Hourglass Sorting: A novel parallel sorting algorithm and its implementation
Daniel Bascones, Borja Morcillo
https://arxiv.org/abs/2507.16326 https://

Hourglass Sorting: A novel parallel sorting algorithm and its implementation
Sorting is one of the fundamental problems in computer science. Playing a role in many processes, it has a lower complexity bound imposed by $\mathcal{O}(n\log{n})$ when executing on a sequential machine. This limit can be brought down to sub-linear times thanks to parallelization techniques that increase the number of comparisons done in parallel. This, however, increases the cost of implementation, which limits the application of such techniques. Moreover, as the size of the arrays increases,…

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models
Shanshan Wang, Dian Xu, Jianmin Shen, Feng Gao, Wei Li, Weibing Deng
https://arxiv.org/abs/2507.14159

Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models
Percolation theory serves as a cornerstone for studying phase transitions and critical phenomena, with broad implications in statistical physics, materials science, and complex networks. However, most machine learning frameworks for percolation analysis have focused on two-dimensional systems, oversimplifying the spatial correlations and morphological complexity of real-world three-dimensional materials. To bridge this gap and improve label efficiency and scalability in 3D systems, we propose a…

Lower bound of computational complexity of knapsack problems
Zhidong Zhang
https://arxiv.org/abs/2506.12080 https://arxiv.org/pdf/250…

Lower bound of computational complexity of knapsack problems
The quantum statistics mechanism is very powerful for investigating the equilibrium states and the phase transitions in complex spin disorder systems. The spin disorder systems act as an interdisciplinary platform for solving the optimum processes in computer science. In this work, I determined the lower bound of the computational complexity of knapsack problems. I investigated the origin of nontrivial topological structures in these hard problems. It was uncovered that the nontrivial topologic…

(AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation
Eunhye Grace Ko, Soo Hyoung Joo
https://arxiv.org/abs/2506.06165

(AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation
While the complexity of 21st-century demands has promoted pedagogical approaches to foster complex competencies, a persistent gap remains between in-class learning activities and individualized learning or assessment practices. To address this, studies have explored the use of AI-generated characters in learning and assessment. One attempt is scenario-based assessment (SBA), a technique that not only measures but also fosters the development of competencies throughout the assessment process. SB…

XRISM Spectroscopy of the Stellar-Mass Black Hole 4U 1630-472 in Outburst
Jon M. Miller (Univ. of Michigan), Misaki Mizumoto (Univ. of Teacher Education Fukuoka), Megumi Shidatsu (Ehime University), Ralf Ballhausen (Univ. of Maryland, College Park, NASA/GSFC, CRESST), Ehud Behar (Technion), Maria Diaz Trigo (ESO), Chris Done (Univ. of Durham), Tadayasu Dotani (ISAS), Javier Garcia (Caltech), Timothy Kallman (NASA/GSFC), Shogo B. Kobayashi (Tokyo Univ. of Science), Aya Kubota (Shibaura …

XRISM Spectroscopy of the Stellar-Mass Black Hole 4U 1630-472 in Outburst
We report on XRISM/Resolve spectroscopy of the recurrent transient and well-known black hole candidate 4U 1630$-$472 during its 2024 outburst. The source was captured at the end of a disk-dominated high/soft state, at an Eddington fraction of $λ_\mathrm{Edd} \sim 0.05~(10 M_{\odot}/M_\mathrm{BH})$. A variable absorption spectrum with unprecedented complexity is revealed with the Resolve calorimeter. This marks one of the lowest Eddington fractions at which highly ionized absorption has been de…

Like a coin spinning in the air: the effect of (non-)metaphorical explanations on comprehension and attitudes towards quantum technology
Aletta Lucia Meinsma, W. Gudrun Reijnierse, Julia Cramer
https://arxiv.org/abs/2506.10539

Like a coin spinning in the air: the effect of (non-)metaphorical explanations on comprehension and attitudes towards quantum technology
The complexity of the science underlying quantum technology may pose a barrier to its democratization. This study investigated whether metaphors improve comprehension of, and shape attitudes toward, quantum technology. In an online experiment (n = 1,167 participants representative of the Dutch population), participants read a news article that included a metaphorical, non-metaphorical, or no explanation of a quantum phenomenon. Both explanation types reduced perceived comprehension of the news …

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials
Bowen Hou, Xian Xu, Jinyuan Wu, Diana Y. Qiu
https://arxiv.org/abs/2507.05480 …

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials
Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to understanding excited-state properties, ranging from transport to optics -- remains challenging due to the complexity of the nonlocal and energy-dependent interactions. Here, we propose a symmetry-aware, grid-free, transformer-based model, MBFormer, that is desi…

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

Mutual Correlation
Francesco A. Evangelista
https://arxiv.org/abs/2506.07344 https://arxiv.org/pdf/2506.07344

Mutual Correlation
Quantifying correlation and complexity in quantum many-body states is central to advancing theoretical and computational chemistry, physics, and quantum information science. This work introduces a novel framework, mutual correlation, based on the Frobenius norm squared of the two-body reduced density matrix cumulant. Through systematic partitioning of the cumulant norm, mutual correlation quantifies nonadditive correlations among interacting subsystems. Benchmark studies on model systems, inclu…

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
Guang-Xing Li
https://arxiv.org/abs/2506.05758 …

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often un…

The Multiple Time-Stepping Method for 3-Body Interactions in High Performance Molecular Dynamics Simulations
David Martin, Samuel James Newcome, Markus M\"uhlh\"au{\ss}er, Manish Kumar Mishra, Fabio Alexander Gratl, Hans-Joachim Bungartz
https://arxiv.org/abs/2507.11172

The Multiple Time-Stepping Method for 3-Body Interactions in High Performance Molecular Dynamics Simulations
Understanding the complex behavior of molecular systems is fundamental to fields such as physics, materials science, and biology. Molecular dynamics (MD) simulations are crucial tools for studying atomic-level dynamics. This work focuses on improving the efficiency of MD simulations involving two-body and three-body interactions. Traditional two-body potentials often can not fully capture the complexity of molecular systems, making the inclusion of three-body interactions important. However, th…

Quantum Entanglement with Geometric Measures
Xuanran Zhu
https://arxiv.org/abs/2506.11453 https://arxiv.org/pdf/2506.11453

Quantum Entanglement with Geometric Measures
Quantifying quantum entanglement is a pivotal challenge in quantum information science, particularly for high-dimensional systems, due to its computational complexity. This thesis extends the geometric measure of entanglement (GME) to introduce and investigate a suite of GME-based entanglement monotones tailored for diverse quantum contexts, including pure states, subspaces, and mixed states. These monotones are applicable to both bipartite and multipartite systems, offering a unified framework…

This https://arxiv.org/abs/2408.08294 has been replaced.
link: https://scholar.google.com/scholar?q=a

eGAD! double descent is explained by Generalized Aliasing Decomposition
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, over-parameterized models exhibit counterintuitive behaviors, such as "double descent" in which models of increasing complexity exhibit decreasing generalization error. Others may e…

This https://arxiv.org/abs/2503.10569 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Low-Rank Matrix Regression via Least-Angle Regression
Low-rank matrix regression is a fundamental problem in data science with various applications in systems and control. Nuclear norm regularization has been widely applied to solve this problem due to its convexity. However, it suffers from high computational complexity and the inability to directly specify the rank. This work introduces a novel framework for low-rank matrix regression that addresses both unstructured and Hankel matrices. By decomposing the low-rank matrix into rank-1 bases, the …

Quantum Algorithms for Projection-Free Sparse Convex Optimization
Jianhao He, John C. S. Lui
https://arxiv.org/abs/2507.08543 https://

Quantum Algorithms for Projection-Free Sparse Convex Optimization
This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in machine learning and data science. For the vector domain $\mathcal{D} \subset \mathbb{R}^d$, we propose two quantum algorithms for sparse constraints that finds a $\varepsilon$-optimal solution with the query complexity of $O(\sqrt{d}/\varepsilon)$ and $O(1/\varepsilon)$ by using the function value oracle, reducing a fa…