Tootfinder

Il est rare de trouver un auteur capable de restituer, en littérature, le sentiment que procure la photographie de rue. Il capture les rues, les bâtiments, les rencontres comme des archives persistantes qui se dévoilent devant nos yeux. Pourtant, il existe un auteur...
Continuer la lecture sur le Sillon https://

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Bing Song, Jianing Liu, Sisi Jian, Chenyang Wu, Vinayak Dixit
https://arxiv.org/abs/2506.23107

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generate…

I finally got some IR LED's and was able to reproduce @… 's circuit from a month ago to sense the IR signal from a remote control.
See https://fosstodon.org/deck/@kdrnic@…

Scientists Capture Sharpest Images Ever of Sun's Corona | PetaPixel https://petapixel.com/2025/05/30/scientists-capture-sharpest-images-ever-of-suns-corona/
There's video in there you might want to full-screen.

Scientists Capture Sharpest Images Ever of Sun's Corona
You've never seen the Sun like this.

MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion
Zihan Wang, Jeff Tan, Tarasha Khurana, Neehar Peri, Deva Ramanan
https://arxiv.org/abs/2507.23782 https://

MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion
We address the problem of dynamic scene reconstruction from sparse-view videos. Prior work often requires dense multi-view captures with hundreds of calibrated cameras (e.g. Panoptic Studio). Such multi-view setups are prohibitively expensive to build and cannot capture diverse scenes in-the-wild. In contrast, we aim to reconstruct dynamic human behaviors, such as repairing a bike or dancing, from a small set of sparse-view cameras with complete scene coverage (e.g. four equidistant inward-faci…

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

Substructural Abstract Syntax with Variable Binding and Single-Variable Substitution
Marcelo Fiore, Sanjiv Ranchod
https://arxiv.org/abs/2505.24812 https:/…

Substructural Abstract Syntax with Variable Binding and Single-Variable Substitution
We develop a unified categorical theory of substructural abstract syntax with variable binding and single-variable (capture-avoiding) substitution. This is done for the gamut of context structural rules given by exchange (linear theory) with weakening (affine theory) or with contraction (relevant theory) and with both (cartesian theory). Specifically, in all four scenarios, we uniformly: define abstract syntax with variable binding as free algebras for binding-signature endofunctors over variab…

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
Shixiao Wang, Yifan Zhuang, Runsheng Zhang, Zhijun Song
https://arxiv.org/abs/2507.00540

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The mod…

Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model
Vittorio La Barbera, Steven Bohez, Leonard Hasenclever, Yuval Tassa, John R. Hutchinson
https://arxiv.org/abs/2506.23768

Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model
We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomoti…

Path representations in multiparameter persistent homology
Xudong Sun, Ren\'e Corbet, Carsten Marr
https://arxiv.org/abs/2507.23762 https://arxiv.org/p…

Path representations in multiparameter persistent homology
Multiparameter persistence module can capture more topological differences across data instances compared to using a single parameter, where the well-studied matching distance investigates the distance along a straight line in the multiparameter space that gives the biggest difference. We propose to generalize the straight line to a monotone path filtration and offer software implementations.

Hybrid EEG--Driven Brain--Computer Interface: A Large Language Model Framework for Personalized Language Rehabilitation
Ismail Hossain, Mridul Banik
https://arxiv.org/abs/2507.22892

Hybrid EEG--Driven Brain--Computer Interface: A Large Language Model Framework for Personalized Language Rehabilitation
Conventional augmentative and alternative communication (AAC) systems and language-learning platforms often fail to adapt in real time to the user's cognitive and linguistic needs, especially in neurological conditions such as post-stroke aphasia or amyotrophic lateral sclerosis. Recent advances in noninvasive electroencephalography (EEG)--based brain-computer interfaces (BCIs) and transformer--based large language models (LLMs) offer complementary strengths: BCIs capture users' neural intent w…

Connecting mean-field theory with dynamo simulations
Petri J. K\"apyl\"a (KIS)
https://arxiv.org/abs/2507.00632 https://arx…

Connecting mean-field theory with dynamo simulations
Mean-field dynamo theory, describing the evolution of large-scale magnetic fields, has been the mainstay of theoretical interpretation of magnetism in astrophysical objects such as the Sun for several decades. More recently, three-dimensional magnetohydrodynamic simulations have reached a level of fidelity where they capture dynamo action self-consistently on local and global scales without resorting to parametrization of unresolved scales. Recent global simulations also capture many of the obs…

Seeing is not believing in limited visibility cops and robbers
Bojan Ba\v{s}i\'c, Alfie Davies, Aleksa D\v{z}uklevski, Strahinja Gvozdi\'c, Yannick Mogge
https://arxiv.org/abs/2507.00941

Seeing is not believing in limited visibility cops and robbers
We consider the model of limited visibility Cops and Robbers, where the cops can only see within their $l$-neighbourhood. We prove that the number of cops needed to see the robber can be arbitrarily smaller than the number needed to capture the robber, answering an open question from the literature. We then consider how close we can get to seeing the robber when we do not have enough cops, along with a probabilistic interpretation.

Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space
Guangyuan Weng, Minsuk Kim, Yong-Yeol Ahn, Esteban Moro
https://arxiv.org/abs/2506.24061

Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space
Human mobility in cities is shaped not only by visible structures such as highways, rivers, and parks but also by invisible barriers rooted in socioeconomic segregation, uneven access to amenities, and administrative divisions. Yet identifying and quantifying these barriers at scale and their relative importance on people's movements remains a major challenge. Neural embedding models, originally developed for language, offer a powerful way to capture the complexity of human mobility from large-…

This https://arxiv.org/abs/2504.01832 has been replaced.
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Quantum Meets SAR: A Novel Range-Doppler Algorithm for Next-Gen Earth Observation
Synthetic Aperture Radar (SAR) plays a vital role in remote sensing due to its ability to capture high-resolution images regardless of weather conditions or daylight. However, to transform the raw SAR signals into interpretable imagery, advanced data processing techniques are essential. A widely used technique for this purpose is the Range Doppler Algorithm (RDA), which takes advantage of Fast Fourier Transform (FFT) to convert signals into the frequency domain for further processing. However, …

@… @… one capture, not by me, was made a few hours ago:
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This https://arxiv.org/abs/2505.12018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Human Study of Cognitive Biases in CTF Challenges
Cybersecurity training has become a crucial part of computer science education and industrial onboarding. Capture the Flag (CTF) competitions have emerged as a valuable, gamified approach for developing and refining the skills of cybersecurity and software engineering professionals. However, while CTFs provide a controlled environment for tackling real world challenges, the participants' decision making and problem solving processes remain under explored. Recognizing that psychology may play a …

Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation
Oliver Bause, Julia Werner, Paul Palomero Bernardo, Oliver Bringmann
https://arxiv.org/abs/2507.23398

Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation
For many real-world applications involving low-power sensor edge devices deep neural networks used for image classification might not be suitable. This is due to their typically large model size and require- ment of operations often exceeding the capabilities of such resource lim- ited devices. Furthermore, camera sensors usually capture images with a Bayer color filter applied, which are subsequently converted to RGB images that are commonly used for neural network training. However, on resour…

FMIP: Multimodal Flow Matching for Mixed Integer Linear Programming
Hongpei Li, Hui Yuan, Han Zhang, Dongdong Ge, Mengdi Wang, Yinyu Ye
https://arxiv.org/abs/2507.23390 https://…

FMIP: Multimodal Flow Matching for Mixed Integer Linear Programming
Mixed-Integer Linear Programming (MILP) is a cornerstone of mathematical optimization, enabling the modeling of complex decision-making problems involving both integer and continuous variables. Despite its versatility, most MILP problems are NP-complete, making them challenging to solve in practice. Existing graph neural network (GNN)-based heuristics aim to reduce problem scale by predicting only the solutions on integer variables for a given instance, struggling to capture the intricate inter…

On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Cheng Long, Wei Yuan, Hongxu Chen, Xiangyu Zhao, Hongzhi Yin
https://arxiv.org/abs/2507.00479

On Mitigating Data Sparsity in Conversational Recommender Systems
Conversational recommender systems (CRSs) capture user preference through textual information in dialogues. However, they suffer from data sparsity on two fronts: the dialogue space is vast and linguistically diverse, while the item space exhibits long-tail and sparse distributions. Existing methods struggle with (1) generalizing to varied dialogue expressions due to underutilization of rich textual cues, and (2) learning informative item representations under severe sparsity. To address these …

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
Melissa Antonelli, Arnaud Durand, Juha Kontinen
https://arxiv.org/abs/2506.23404

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
In this paper, we provide a uniform framework for investigating small circuit classes and bounds through the lens of ordinary differential equations (ODEs). Following an approach recently introduced to capture the class of polynomial-time computable functions via ODE-based recursion schemas and later applied to the context of functions computed by unbounded fan-in circuits of constant depth (FAC^0), we study multiple relevant small circuit classes. In particular, we show that natural restrictio…

Covariate-informed link prediction with extreme taxonomic bias
Jennifer N. Kampe, Camille DeSisto, David B. Dunson
https://arxiv.org/abs/2506.23370 https:/…

Covariate-informed link prediction with extreme taxonomic bias
Biotic interactions provide a valuable window into the inner workings of complex ecological communities and capture the loss of ecological function often precipitated by environmental change. However, the financial and logistical challenges associated with collecting interaction data result in networks that are recorded with geographical and taxonomic bias, particularly when studies are narrowly focused. We develop an approach to reduce bias in link prediction in the common scenario in which da…

XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs
Yitian Gong, Luozhijie Jin, Ruifan Deng, Dong Zhang, Xin Zhang, Qinyuan Cheng, Zhaoye Fei, Shimin Li, Xipeng Qiu
https://arxiv.org/abs/2506.23325

XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs
Speech codecs serve as bridges between speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also capture rich semantic information. However, existing speech codecs struggle to balance high-quality audio reconstruction with ease of modeling by language models. In this study, we analyze the limitations of previous codecs in balancing semantic richness and acoustic fidelity. We propose XY-Tokenizer, a novel codec that…

Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity
Vivek Pandey, Nader Motee
https://arxiv.org/abs/2507.23489

Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity
Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robust risk framework for analyzing cascading failures in multi-agent rendezvous. To capture the complex interactions between network connectivity, system dynamics, and communication delays, we use a time-delayed network model as a benchmark. We introduce a conditional distributionally robust functional to quantify cascadi…

Accretion Regimes of Neutrino-Cooled Flows onto Black Holes
Javiera Hern\'andez-Morales, Daniel M. Siegel
https://arxiv.org/abs/2507.23691 https://arxi…

Accretion Regimes of Neutrino-Cooled Flows onto Black Holes
Neutrino-cooled accretion disks can form in the aftermath of neutron-star mergers as well as during the collapse of rapidly rotating massive stars (collapsars) and the accretion-induced collapse of rapidly rotating white dwarfs. Due to Pauli blocking as electrons become degenerate at sufficiently high accretion rates $\dot{M}$, the resulting 'self-neutronization' of the dissociated accreting plasma makes these astrophysical systems promising sources of rapid neutron capture nucleosynthesis (the…

The effect of droplet configurations within the Functional Renormalization Group of low-dimensional Ising models
Ivan Balog, Lucija Nora Farka\v{s}, Maroje Marohni\'c, Gilles Tarjus
https://arxiv.org/abs/2506.23415

The effect of droplet configurations within the Functional Renormalization Group of low-dimensional Ising models
We explore the application of the nonperturbative functional renormalization group (NPFRG) within its most common approximation scheme based on truncations of the derivative expansion to the $Z_2$-symmetric scalar $φ^4$ theory as the lower critical dimension $d_{\rm lc}$ is approached. We aim to assess whether the NPFRG - a broad, nonspecialized method which is accurate in $d\geq 2$ - can capture the effect of the localized (droplet) excitations that drive the disappearance of the phase transi…

Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Rahul Kumar Gautam
https://arxiv.org/abs/2506.23179

Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Nowadays, people in the modern world communicate with their friends, relatives, and colleagues through the internet. Persons/nodes and communication/edges among them form a network. Social media networks are a type of network where people share their views with the community. There are several models that capture human behavior, such as a reaction to the information received from friends or relatives. The two fundamental models of information diffusion widely discussed in the social networks ar…

This https://arxiv.org/abs/2410.00665 has been replaced.
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Graph-Based Representation Learning of Neuronal Dynamics and Behavior
Understanding how neuronal networks reorganize in response to external stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. However, existing methods often fail to capture the evolving structure of neural connectivity in ways that capture its relationship to behavior, especially in dynamic, uncertain, or high-dimensional settings with sufficient resolution or interpretability. We introduce the Temporal Attention-enhanced Variational Graph Recurre…

Temporally Extending Existing Web Archive Collections for Longitudinal Analysis
Lesley Frew, Michael L. Nelson, Michele C. Weigle
https://arxiv.org/abs/2505.24091

Temporally Extending Existing Web Archive Collections for Longitudinal Analysis
The Environmental Governance and Data Initiative (EDGI) regularly crawled US federal environmental websites between 2016 and 2020 to capture changes between two presidential administrations. However, because it does not include the previous administration ending in 2008, the collection is unsuitable for answering our research question, Were the website terms deleted by the Trump administration (2017--2021) added by the Obama administration (2009--2017)? Thus, like many researchers using the Way…

Eddy population based model for the wall-pressure spectrum at high Reynolds number
Jonathan M. O. Massey, Alexander J. Smits, Beverley J. McKeon
https://arxiv.org/abs/2507.23098

Eddy population based model for the wall-pressure spectrum at high Reynolds number
Wall-pressure fluctuations beneath turbulent boundary layers drive noise and structural fatigue through interactions between fluid and structural modes. Conventional predictive models for the spectrum--such as the widely accepted Goody model--fail to capture the energetic growth in the subconvective regime that occurs at high Reynolds number, while at the same time over-predicting the variance. To address these shortcomings, two semi-empirical models are proposed for the wall-pressure spectru…

This https://arxiv.org/abs/2505.22547 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

First measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasi-elastic-like interactions using an accelerator neutrino beam
We report the first measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasi-elastic-like interactions at the gadolinium-loaded Super-Kamiokande detector using the T2K neutrino beam, which has a peak energy of about 0.6 GeV. A total of 30 neutral-current quasi-elastic-like event candidates were selected from T2K data corresponding to an exposure of $1.76\times10^{20}$ protons on target. The $γ$ ray signals resulting from neutron captures were identified using a neur…

Bridging Unstratified and Stratified Simulations of the Streaming Instability for $\tau_s=0.1$ Grains
Jeonghoon Lim, Stanley A. Baronett, Jacob B. Simon, Chao-Chin Yang, Debanjan Sengupta, Orkan M. Umurhan, Wladimir Lyra
https://arxiv.org/abs/2505.23902

Bridging Unstratified and Stratified Simulations of the Streaming Instability for $τ_s=0.1$ Grains
The streaming instability (SI), driven by aerodynamic coupling between solids and the gas under a global radial pressure gradient, concentrates solids and facilitates planetesimal formation. Unstratified simulations are commonly used to study the SI, based on the assumption that they approximate conditions near the disk midplane. However, it remains unclear how accurately these unstratified simulations capture the midplane dust-gas dynamics in stratified disks. To address this, we examine the s…

Ionic KdV structure in weakly collisional plasmas
Renjun Duan, Zongguang Li, Dongcheng Yang, Tong Yang
https://arxiv.org/abs/2506.23159 https://

Ionic KdV structure in weakly collisional plasmas
We consider the one-dimensional ions dynamics in weakly collisional plasmas governed by the Vlasov-Poisson-Landau system under the Boltzmann relation with the small collision frequency $ν>0$. It is observed in physical experiments that the interplay of nonlinearities and dispersion may lead to the formation of ion acoustic solitons that are described by the Korteweg-de Vries equation. In this paper, to capture the ionic KdV structure in the weak-collision regime, we study the combined cold-ion…

Modeling European Electricity Market Integration during turbulent times
Francesco Ravazzolo, Luca Rossini, Andrea Viselli
https://arxiv.org/abs/2506.23289 …

Modeling European Electricity Market Integration during turbulent times
This paper introduces a novel Bayesian reverse unrestricted mixed-frequency model applied to a panel of nine European electricity markets. Our model analyzes the impact of daily fossil fuel prices and hourly renewable energy generation on hourly electricity prices, employing a hierarchical structure to capture cross-country interdependencies and idiosyncratic factors. The inclusion of random effects demonstrates that electricity market integration both mitigates and amplifies shocks. Our result…

ISMAF: Intrinsic-Social Modality Alignment and Fusion for Multimodal Rumor Detection
Zihao Yu, Xiang Li, Jing Zhang
https://arxiv.org/abs/2505.24176 https:…

ISMAF: Intrinsic-Social Modality Alignment and Fusion for Multimodal Rumor Detection
The rapid dissemination of rumors on social media highlights the urgent need for automatic detection methods to safeguard societal trust and stability. While existing multimodal rumor detection models primarily emphasize capturing consistency between intrinsic modalities (e.g., news text and images), they often overlook the intricate interplay between intrinsic and social modalities. This limitation hampers the ability to fully capture nuanced relationships that are crucial for a comprehensive …

Uncertain Mode Surfaces in 3D Symmetric Second-Order Tensor Field Ensembles
Tim Gerrits
https://arxiv.org/abs/2506.23406 https://arxi…

Uncertain Mode Surfaces in 3D Symmetric Second-Order Tensor Field Ensembles
The analysis of 3D symmetric second-order tensor fields often relies on topological features such as degenerate tensor lines, neutral surfaces, and their generalization to mode surfaces, which reveal important structural insights into the data. However, uncertainty in such fields is typically visualized using derived scalar attributes or tensor glyph representations, which often fail to capture the global behavior. Recent advances have introduced uncertain topological features for tensor field …

Time-resolved and three-dimensional elucidation of the complex refractive index alteration induced by ultrashort laser pulses
Takumi Koike, Yusuke Ito, Naohiko Sugita
https://arxiv.org/abs/2506.22582

Time-resolved and three-dimensional elucidation of the complex refractive index alteration induced by ultrashort laser pulses
Ultrashort pulse lasers (USPLs) have attracted attention as tools capable of inducing unique phenomena by instantaneously generating regions with transiently modified properties (filaments) in the material. However, a comprehensive understanding of USPL-induced filaments remains elusive due to the complexity of their dynamics and insufficient imaging techniques to accurately capture them. In this study, we propose a novel methodology to measure the transient complex refractive index of filament…

GATSim: Urban Mobility Simulation with Generative Agents
Qi Liu, Can Li, Wanjing Ma
https://arxiv.org/abs/2506.23306 https://arxiv.or…

GATSim: Urban Mobility Simulation with Generative Agents
Traditional agent-based urban mobility simulations rely on rigid rule-based systems that fail to capture the complexity, adaptability, and behavioral diversity characteristic of human travel decision-making. Recent advances in large language models and AI agent technology offer opportunities to create agents with reasoning capabilities, persistent memory, and adaptive learning mechanisms. We propose GATSim (Generative-Agent Transport Simulation), a novel framework that leverages these advances …

Consistent line clustering using geometric hypergraphs
Kalle Alaluusua, Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskel\"a
https://arxiv.org/abs/2505.24868

Consistent line clustering using geometric hypergraphs
Traditional data analysis often represents data as a weighted graph with pairwise similarities, but many problems do not naturally fit this framework. In line clustering, points in a Euclidean space must be grouped so that each cluster is well approximated by a line segment. Since any two points define a line, pairwise similarities fail to capture the structure of the problem, necessitating the use of higher-order interactions modeled by geometric hypergraphs. We encode geometry into a 3-unifor…

Exploring Dynamic Parameters for Vietnamese Gender-Independent ASR
Sotheara Leang (CADT, M-PSI), \'Eric Castelli (M-PSI), Dominique Vaufreydaz (M-PSI), Sethserey Sam (CADT)
https://arxiv.org/abs/2507.22964

Exploring Dynamic Parameters for Vietnamese Gender-Independent ASR
The dynamic characteristics of speech signal provides temporal information and play an important role in enhancing Automatic Speech Recognition (ASR). In this work, we characterized the acoustic transitions in a ratio plane of Spectral Subband Centroid Frequencies (SSCFs) using polar parameters to capture the dynamic characteristics of the speech and minimize spectral variation. These dynamic parameters were combined with Mel-Frequency Cepstral Coefficients (MFCCs) in Vietnamese ASR to capture …

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
Yating Wang, Haoyi Zhu, Mingyu Liu, Jiange Yang, Hao-Shu Fang, Tong He
https://arxiv.org/abs/2507.01016

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
In this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more data than previous approaches. This extensive dataset enables our tokenizer to capture rich spatiotemporal dynamics, resulting in a model that not only accelerates inference but also generates smoother and more coherent action outputs. Once trained, the tokenizer can be seamlessly adapted to a wide range of downstream…

⚓ Meet the cement transport ship that makes cement ingredients while sailing
https://techcrunch.com/2025/07/16/meet-the-cement-transport-ship-that-makes-cement-ingredients-while-sailing/

Meet the cement transport ship that makes cement ingredients while sailing | TechCrunch
London-based Seabound has developed a carbon capture system that transforms CO2 from a ship's engine into limestone, which Heidelberg Materials will use to make cement.

Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios
Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi
https://arxiv.org/abs/2505.24250

Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios
This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in tu…

You cannot capture the essence of capitalism in a single pho- https://me.dm/@davidtoddmccarty/114944150346707896

David Todd McCarty (@davidtoddmccarty@me.dm)
Attached: 1 image So clearly the most hateful and soulless solution to a problem that might exist.

From Capture to Collapse: Revisiting Black Hole formation by Fermionic Asymmetric Dark Matter in Neutron Stars
Sandra Robles, Drona Vatsyayan, Giorgio Busoni
https://arxiv.org/abs/2507.22881

From Capture to Collapse: Revisiting Black Hole formation by Fermionic Asymmetric Dark Matter in Neutron Stars
Fermionic asymmetric dark matter (ADM) can be captured in neutron stars (NSs) via scatterings with the star constituents. The absence of dark matter annihilation due to its asymmetric nature leads to ADM accumulation in the NS core, potentially reaching densities sufficient to exceed the Chandrasekhar limit and trigger its gravitational collapse into a black hole (BH), eventually consuming the NS from within. Therefore, the existence and observation of old neutron stars provides a means to cons…

This https://arxiv.org/abs/2505.01875 has been replaced.
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MolQAE: Quantum Autoencoder for Molecular Representation Learning
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecul…

MedRegion-CT: Region-Focused Multimodal LLM for Comprehensive 3D CT Report Generation
Sunggu Kyung, Jinyoung Seo, Hyunseok Lim, Dongyeong Kim, Hyungbin Park, Jimin Sung, Jihyun Kim, Wooyoung Jo, Yoojin Nam, Namkug Kim
https://arxiv.org/abs/2506.23102

MedRegion-CT: Region-Focused Multimodal LLM for Comprehensive 3D CT Report Generation
The recent release of RadGenome-Chest CT has significantly advanced CT-based report generation. However, existing methods primarily focus on global features, making it challenging to capture region-specific details, which may cause certain abnormalities to go unnoticed. To address this, we propose MedRegion-CT, a region-focused Multi-Modal Large Language Model (MLLM) framework, featuring three key innovations. First, we introduce Region Representative ($R^2$) Token Pooling, which utilizes a 2D-…

Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment
Julian Acosta, Scott Adams, Julius Kernbach, Romain Hardy, Sung Eun Kim, Luyang Luo, Xiaoman Zhang, Shreya Johri, Mohammed Baharoon, Pranav Rajpurkar
https://arxiv.org/abs/2507.22898

Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment
We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conversation, while also enabling smartphone video capture of key examination components for documentation and potential expert review. This addresses a critical gap in emergency care: current stroke recognition by first responders is inconsistent and often inaccurate, with sensitivity for stroke detection as low as 58%, c…

I wonder if the old magic is still out there to capture, or if it only lived in the unknowing

This https://arxiv.org/abs/2505.18893 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects
Conventional AI evaluation approaches concentrated within the AI stack exhibit systemic limitations for exploring, navigating and resolving the human and societal factors that play out in real world deployment such as in education, finance, healthcare, and employment sectors. AI capability evaluations can capture detail about first-order effects, such as whether immediate system outputs are accurate, or contain toxic, biased or stereotypical content, but AI's second-order effects, i.e. any long…

Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution
Yifan Yu, Shengjie Xiu, Daniel P. Palomar
https://arxiv.org/abs/2507.22343

Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution
State-space models are pivotal for dynamic system analysis but often struggle with outlier data that deviates from Gaussian distributions, frequently exhibiting skewness and heavy tails. This paper introduces a robust extension utilizing the asymmetric Laplace distribution, specifically tailored to capture these complex characteristics. We propose an efficient variational Bayes algorithm and a novel single-loop parameter estimation strategy, significantly enhancing the efficiency of the filteri…

Rises for Measuring Local Distributivity in Lattices
Mohammad Abdulla, Tobias Hille, Dominik D\"urrschnabel, Gerd Stumme
https://arxiv.org/abs/2506.23168

Rises for Measuring Local Distributivity in Lattices
Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change …

Graph neural networks for residential location choice: connection to classical logit models
Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang
https://arxiv.org/abs/2507.21334

Graph neural networks for residential location choice: connection to classical logit models
Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cannot explicitly capture the relationship among choice alternatives, which has been a long-lasting focus in classical discrete choice models. To address the gap, this paper introduces Graph Neural Network (GNN) as a novel framework to analyze residential location choice. The GNN-ba…

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
Yang Sui, Qi Xu, Ting Li, Yang Bai, Annie Qu
https://arxiv.org/abs/2505.24259

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
This paper is motivated by the heterogeneous associations among demographic covariates, imaging data, and cognitive performances across different diagnostic groups within the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a novel PArtially-shared Imaging Regression (PAIR) model with smooth spatial component integration to capture heterogeneous imaging coefficients across multiple data sources. The model assumes that each imaging coefficient can be represented as a weighted…

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
Grace Nansamba, Evelyn Namugwanya, David Boehme, Dewi Yokelson, Riley Shipley, Derek Schafer, Michael McKinsey, Olga Pearce, Anthony Skjellum
https://arxiv.org/abs/2507.22372

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
We introduce ``communication regions'' into the widely used Caliper HPC profiling tool. A communication region is an annotation enabling capture of metrics about the data being communicated (including statistics of these metrics), and metrics about the MPI processes involved in the communications, something not previously possible in Caliper. We explore the utility of communication regions with three representative modeling and simulation applications, AMG2023, Kripke, and Laghos, all part of t…

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova
https://arxiv.org/abs/2507.00353

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Accurate electrochemical models are essential for the safe and efficient operation of lithium-ion batteries in real-world applications such as electrified vehicles and grid storage. Reduced-order models (ROM) offer a balance between fidelity and computational efficiency but often struggle to capture complex and nonlinear behaviors, such as the dynamics in the cell voltage response under high C-rate conditions. To address these limitations, this study proposes an Adaptive Ensemble Sparse Identif…

This https://arxiv.org/abs/2501.16815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From the perspective of optimization, we revisit the classical selection and elimination criteria in traditional best subset selection algorithms, revealing that these classical criteria capture only partial variations of the objective function after the entry or exit of features. By form…

Infinite Traces by Finality: a Sheaf-Theoretic Approach
Marco Peressotti
https://arxiv.org/abs/2507.22536 https://arxiv.org/pdf/2507.22536

Infinite Traces by Finality: a Sheaf-Theoretic Approach
Kleisli categories have long been recognised as a setting for modelling the linear behaviour of various types of systems. However, the final coalgebra in such settings does not, in general, correspond to a fixed notion of linear semantics. While there are well-understood conditions under which final coalgebras capture finite trace semantics, a general account of infinite trace semantics via finality has remained elusive. In this work, we present a sheaf-theoretic framework for infinite trace se…

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
Martin Obaidi, Jakob Droste, Hannah Deters, Marc Herrmann, Raymond Ochsner, Jil Kl\"under, Kurt Schneider
https://arxiv.org/abs/2505.23684

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the efficiency and effectiveness of three commonly used elicitation methods - focus groups, interviews, and online surveys - while also assessing the role of taxonomy usage in structuring a…

Finished the SFP28 carrier board. Still waiting on final design review before I send it out to OSHPark, but it's done-enough that I could export a STEP and pull into the enclosure model.
Placement of all the front panel stuff is pretty definite at this point, the power supply and PDU are likely to move.
Logic board hasn't even started schematic capture yet.
There will probably be at least a couple of fans but I haven't figured out where or how many yet.

A Cop-Win Graph with Maximum Capture Time $\omega$
Tom\'a\v{s} Fl\'idr, Maria-Romina Ivan
https://arxiv.org/abs/2507.22276 https://arxiv.org/pdf/25…

A Cop-Win Graph with Maximum Capture Time $ω$
The game of cops and robbers is played on a fixed (finite or infinite) graph $G$. The cop chooses his starting position, then the robber chooses his. After that, they take turns and move to adjacent vertices, or stay at their current vertex, with the cop moving first. The game finishes if the cop lands on the robber's vertex. In that case we say that the cop wins, while if the robber is never caught then we say that the robber wins. The graph $G$ is called cop-win if the cop has a winning strat…

This https://arxiv.org/abs/2505.20929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Two-step dimensionality reduction of human mobility data: From potential landscapes to spatiotemporal insights
Understanding the spatiotemporal patterns of human mobility is crucial for addressing societal challenges, such as epidemic control and urban transportation optimization. Despite advancements in data collection, the complexity and scale of mobility data continue to pose significant analytical challenges. Existing methods often result in losing location-specific details and fail to fully capture the intricacies of human movement. This study proposes a two-step dimensionality reduction framework …

This https://arxiv.org/abs/2505.22737 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Nonlinear Diffusion and Decay of an Expanding Turbulent Blob
Turbulence, left unforced, decays and invades the surrounding quiescent fluid. Though ubiquitous, this simple phenomenon has proven hard to capture within a simple and general framework. Experiments in conventional turbulent flow chambers are inevitably complicated by proximity to boundaries and mean flow, obscuring the fundamental aspects of the relaxation to the quiescent fluid state. Here, we circumvent these issues by creating a spatially-localized blob of turbulent fluid using eight conver…

A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks
Hang Su, Yunlong Feng, Daniel Gehrig, Panfeng Jiang, Ling Gao, Xavier Lagorce, Laurent Kneip
https://arxiv.org/abs/2507.22733

A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks
Structure and continuous motion estimation from point correspondences is a fundamental problem in computer vision that has been powered by well-known algorithms such as the familiar 5-point or 8-point algorithm. However, despite their acclaim, these algorithms are limited to processing point correspondences originating from a pair of views each one representing an instantaneous capture of the scene. Yet, in the case of rolling shutter cameras, or more recently, event cameras, this synchronizati…

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan, Xiang Li, Yixuan Yuan
https://arxiv.org/abs/2505.24687

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks that fail to capture the gradual transitions characteristic of tumor boundaries. We present TumorGen, a novel Boundary-Aware Tumor-Mask Sy…

HASD: Hierarchical Adaption for pathology Slide-level Domain-shift
Jingsong Liu, Han Li, Chen Yang, Michael Deutges, Ario Sadafi, Xin You, Katharina Breininger, Nassir Navab, Peter J. Sch\"uffler
https://arxiv.org/abs/2506.23673

HASD: Hierarchical Adaption for pathology Slide-level Domain-shift
Domain shift is a critical problem for pathology AI as pathology data is heavily influenced by center-specific conditions. Current pathology domain adaptation methods focus on image patches rather than WSI, thus failing to capture global WSI features required in typical clinical scenarios. In this work, we address the challenges of slide-level domain shift by proposing a Hierarchical Adaptation framework for Slide-level Domain-shift (HASD). HASD achieves multi-scale feature consistency and comp…

Characterization Of Diseases In Temporal Comorbidity Networks
Yuri Gardinazzi, Roger Gonzal\'ez March, Suprabhath Kalahasti, Andrea Monta\~no Ramirez, Matteo Neri, Cicely Nguyen, Giovanni Palermo, Erik Weis, Katharina Ledebur, Elma Dervi\'c
https://arxiv.org/abs/2506.22136

Characterization Of Diseases In Temporal Comorbidity Networks
Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian ho…

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Benedikt Roth, Stephan Rappensperger, Tianming Qiu, Hamza Imamovi\'c, Julian W\"ormann, Hao Shen
https://arxiv.org/abs/2507.22729

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Large Language Models (LLMs) have become a cornerstone in Natural Language Processing (NLP), achieving impressive performance in text generation. Their token-level representations capture rich, human-aligned semantics. However, pooling these vectors into a text embedding discards crucial information. Nevertheless, many non-generative downstream tasks, such as clustering, classification, or retrieval, still depend on accurate and controllable sentence- or document-level embeddings. We explore se…

This https://arxiv.org/abs/2505.23449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in visual reasoning and text generation. While previous studies have explored the application of MLLM for detecting out-of-context (OOC) misinformation, our empirical analysis reveals two persisting challenges of this paradigm. Evaluating the representative GPT-4o model on direct reasoning and evidence augmented reasoning, results indicate that MLLM struggle to capture the deeper relationships-specifically, cases…

Change Data Capture is a powerful technique enabling organisations to respond to real-time data changes. At Berlin Buzzwords, Muhammet Orazov explored FlinkCDC, a component of Apache Flink and discussed how it uses Flink's robust stream processing capabilities to provide a CDC pattern.
Watch the full session: https://

Winning the NFC West: Reasons the 49ers, Rams, Seahawks and Cardinals can capture the division crown

https://www.cbssports.com/nfl/news/winning

Neural models of multiscale systems: conceptual limitations, stochastic parametrizations, and a climate application
Fabrizio Falasca
https://arxiv.org/abs/2506.22552

Neural models of multiscale systems: conceptual limitations, stochastic parametrizations, and a climate application
This work explores key conceptual limitations in data-driven modeling of multiscale dynamical systems, focusing on neural emulators and stochastic climate modeling. A skillful climate model should capture both stationary statistics and responses to external perturbations. While current autoregressive neural models often reproduce the former, they typically struggle with the latter. We begin by analyzing a low-dimensional dynamical system to expose, by analogy, fundamental limitations that persi…

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
Jiahao Yang, Ran Fang, Ming Zhang, Jun Zhou
https://arxiv.org/abs/2505.22678

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask diffe…

The macOS experience in 2025. The main reason I now use Linux instead of my Mac.

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat-Seng Chua
https://arxiv.org/abs/2506.21579

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have mo…

Fundamental Limits of Rigid Body Localization
Niclas F\"uhrling, Ivan Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, Gonzalo Seco-Granados, David Gonz\'alez G., Osvaldo Gonsa
https://arxiv.org/abs/2507.22573

Fundamental Limits of Rigid Body Localization
We consider a novel approach to formulate the Cramér-Rao Lower Bound (CRLB) for the rigid body localization (RBL) problem, which allows us to assess the fundamental accuracy limits on the estimation of the translation and rotation of a rigid body with respect to a known reference. To that end, we adopt an information-centric construction of the Fisher information matrix (FIM), which allows to capture the contribution of each measurement towards the FIM, both in terms of input measurement types…

🪤 Young forests could help to capture carbon in climate change fight
(... leave old forests alone, leave young forests alone!)
https://phys.org/news/2025-07-young-forests-capture-carbon-climate.html

Young forests could help to capture carbon in climate change fight
Young forests regrowing from land where mature woodlands have been cut down have a key role to play in removing billions of tons of atmospheric carbon dioxide (CO2) and combating climate change, a new study reveals.

Stochastic forest transition model dynamics and parameter estimation via deep learning
Satoshi Kumabe, Tianyu Song, Ton Viet Ta
https://arxiv.org/abs/2507.21486 https://

Stochastic forest transition model dynamics and parameter estimation via deep learning
Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learn…

Optimal Estimation of Two-Way Effects under Limited Mobility
Xu Cheng, Sheng Chao Ho, Frank Schorfheide
https://arxiv.org/abs/2506.21987 https://

Optimal Estimation of Two-Way Effects under Limited Mobility
We propose an empirical Bayes estimator for two-way effects in linked data sets based on a novel prior that leverages patterns of assortative matching observed in the data. To capture limited mobility we model the bipartite graph associated with the matched data in an asymptotic framework where its Laplacian matrix has small eigenvalues that converge to zero. The prior hyperparameters that control the shrinkage are determined by minimizing an unbiased risk estimate. We show the proposed empiric…

SkinningGS: Editable Dynamic Human Scene Reconstruction Using Gaussian Splatting Based on a Skinning Model
Da Li, Donggang Jia, Markus Hadwiger, Ivan Viola
https://arxiv.org/abs/2506.21632

SkinningGS: Editable Dynamic Human Scene Reconstruction Using Gaussian Splatting Based on a Skinning Model
Reconstructing an interactive human avatar and the background from a monocular video of a dynamic human scene is highly challenging. In this work we adopt a strategy of point cloud decoupling and joint optimization to achieve the decoupled reconstruction of backgrounds and human bodies while preserving the interactivity of human motion. We introduce a position texture to subdivide the Skinned Multi-Person Linear (SMPL) body model's surface and grow the human point cloud. To capture fine details…

GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
Ethan Frakes, Yinghui Wu, Roger H. French, Mengjie Li
https://arxiv.org/abs/2507.22878

GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
Detecting, analyzing, and predicting power outages is crucial for grid risk assessment and disaster mitigation. Numerous outages occur each year, exacerbated by extreme weather events such as hurricanes. Existing outage data are typically reported at the county level, limiting their spatial resolution and making it difficult to capture localized patterns. However, it offers excellent temporal granularity. In contrast, nighttime light satellite image data provides significantly higher spatial re…

On the Definition of Intelligence
Kei-Sing Ng
https://arxiv.org/abs/2507.22423 https://arxiv.org/pdf/2507.22423

On the Definition of Intelligence
To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently general to encompass diverse paradigms of intelligent behavior, including reinforcement learning, generative models, classification, analogical reasoning, and goal-directed decision-making. We propose a general criterion based on sample fidelity: intelligence is the ability, given sample(s) from a category, to generate sample(s) from the same category. …

Gravitational form factors of the nucleon in the Skyrme model based on scale-invariant chiral perturbation theory
Mitsuru Tanaka, Daisuke Fujii, Mamiya Kawaguchi
https://arxiv.org/abs/2507.21220

Gravitational form factors of the nucleon in the Skyrme model based on scale-invariant chiral perturbation theory
We investigate the role of the QCD scale anomaly in the gravitational form factors of the nucleon -- particularly the $D(t)$ form factor -- as well as the associated stress distribution and internal forces, using a Skyrme model based on the scale-invariant chiral perturbation theory. A distinctive feature of this model is the inclusion of both the pion and the scalar meson, which respectively capture the effects of the current quark mass and gluonic quantum contributions to the scale anomaly. B…

Assessing Value of Renewable-based VPP Versus Electrical Storage: Multi-market Participation Under Different Scheduling Regimes and Uncertainties
Hadi Nemati, Ignacio Egido, Pedro S\'anchez-Mart\'in, \'Alvaro Ortega
https://arxiv.org/abs/2507.22496

Assessing Value of Renewable-based VPP Versus Electrical Storage: Multi-market Participation Under Different Scheduling Regimes and Uncertainties
This paper compares the participation of Renewable-only Virtual Power Plants (RVPPs) and grid-scale Electrical Storage Systems (ESSs) in energy and reserve markets, evaluating their technical performance, market strategies, and economic outcomes. To ensure a fair comparison, scheduling is analyzed over representative sample days that capture seasonal operating regimes, and the associated uncertainties are explicitly modeled. Two-stage robust optimization frameworks are employed: the RVPP model …

Diffusion Models for Influence Maximization on Temporal Networks: A Guide to Make the Best Choice
Aaqib Zahoor, Iqra Altaf Gillani, Janibul Bashir
https://arxiv.org/abs/2507.22589

Diffusion Models for Influence Maximization on Temporal Networks: A Guide to Make the Best Choice
The increasing prominence of temporal networks in online social platforms and dynamic communication systems has made influence maximization a critical research area. Various diffusion models have been proposed to capture the spread of information, yet selecting the most suitable model for a given scenario remains challenging. This article provides a structured guide to making the best choice among diffusion models for influence maximization on temporal networks. We categorize existing models ba…

Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Aditya Retnanto (Asian Development Bank, Philippines), Son Le (Asian Development Bank, Philippines), Sebastian Mueller (Asian Development Bank, Philippines), Armin Leitner (GeoVille Information Systems and Data Processing GmbH, Austria), Konrad Schindler (ETH Z\"urich, Switzerland), Yohan Iddawela (Asian Development Bank, Philippines), Michael Riffler (GeoVille Information Systems and D…

Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond naïve upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages…

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
Arun Govind Neelan, Ferdin Sagai Don Bosco, Naveen Sagar Jarugumalli, Suresh Balaji Vedarethinam
https://arxiv.org/abs/2506.22413

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
In the realm of computational fluid dynamics, traditional numerical methods, which heavily rely on discretization, typically necessitate the formulation of partial differential equations (PDEs) in conservative form to accurately capture shocks and other discontinuities in compressible flows. Conversely, utilizing non-conservative forms often introduces significant errors near these discontinuities or results in smeared shocks. This dependency poses a considerable limitation, particularly as man…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

🪤 A California dairy tried to capture its methane, and it worked
#dairy

A California dairy tried to capture its methane, and it worked
A giant, balloon-like tarp stretches over a lagoon of manure on a Central Valley dairy farm, concealing a quiet but remarkable transformation. Methane, a potent climate-warming gas, is being captured and cleaned instead of released into the atmosphere.

Active Monitoring with RTLola: A Specification-Guided Scheduling Approach
Jan Baumeister, Bernd Finkbeiner, Frederik Scheerer
https://arxiv.org/abs/2507.20615 https://

Active Monitoring with RTLola: A Specification-Guided Scheduling Approach
Stream-based monitoring is a well-established runtime verification approach which relates input streams, representing sensor readings from the monitored system, with output streams that capture filtered or aggregated results. In such approaches, the monitor is a passive external component that continuously receives sensor data from the system under observation. This setup assumes that the system dictates what data is sent and when, regardless of the monitor's current needs. However, in many app…

Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval
Hani Alomari, Anushka Sivakumar, Andrew Zhang, Chris Thomas
https://arxiv.org/abs/2506.21538

Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval
Cross-modal image-text retrieval is challenging because of the diverse possible associations between content from different modalities. Traditional methods learn a single-vector embedding to represent semantics of each sample, but struggle to capture nuanced and diverse relationships that can exist across modalities. Set-based approaches, which represent each sample with multiple embeddings, offer a promising alternative, as they can capture richer and more diverse relationships. In this paper,…

This https://arxiv.org/abs/2505.18893 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects
Conventional AI evaluation approaches concentrated within the AI stack exhibit systemic limitations for exploring, navigating and resolving the human and societal factors that play out in real world deployment such as in education, finance, healthcare, and employment sectors. AI capability evaluations can capture detail about first-order effects, such as whether immediate system outputs are accurate, or contain toxic, biased or stereotypical content, but AI's second-order effects, i.e. any long…

Human Mobility in Epidemic Modeling
Xin Lu, Jiawei Feng, Shengjie Lai, Petter Holme, Shuo Liu, Zhanwei Du, Xiaoqian Yuan, Siqing Wang, Yunxuan Li, Xiaoyu Zhang, Yuan Bai, Xiaojun Duan, Wenjun Mei, Hongjie Yu, Suoyi Tan, Fredrik Liljeros
https://arxiv.org/abs/2507.22799

Human Mobility in Epidemic Modeling
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in da…

PhotonSplat: 3D Scene Reconstruction and Colorization from SPAD Sensors
Sai Sri Teja, Sreevidya Chintalapati, Vinayak Gupta, Mukund Varma T, Haejoon Lee, Aswin Sankaranarayanan, Kaushik Mitra
https://arxiv.org/abs/2506.21680

PhotonSplat: 3D Scene Reconstruction and Colorization from SPAD Sensors
Advances in 3D reconstruction using neural rendering have enabled high-quality 3D capture. However, they often fail when the input imagery is corrupted by motion blur, due to fast motion of the camera or the objects in the scene. This work advances neural rendering techniques in such scenarios by using single-photon avalanche diode (SPAD) arrays, an emerging sensing technology capable of sensing images at extremely high speeds. However, the use of SPADs presents its own set of unique challenges…

Real-Space Chemistry on Quantum Computers: A Fault-Tolerant Algorithm with Adaptive Grids and Transcorrelated Extension
C\'esar Feniou, Christopher Cherfan, Julien Zylberman, Baptiste Claudon, Jean-Philip Piquemal, Emmanuel Giner
https://arxiv.org/abs/2507.20583

Real-Space Chemistry on Quantum Computers: A Fault-Tolerant Algorithm with Adaptive Grids and Transcorrelated Extension
First-quantized, real-space formulations of quantum chemistry on quantum computers are appealing: qubit count scales logarithmically with spatial resolution, and Coulomb operators achieve quadratic instead of quartic computational scaling of two-electron interactions. However, existing schemes employ uniform discretizations, so the resolution required to capture electron-nuclear cusps in high-density regions oversamples low-density regions, wasting computational resources. We address this by de…

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Supawich Sitdhipol, Waritwong Sukprasongdee, Ekapol Chuangsuwanich, Rina Tse
https://arxiv.org/abs/2507.19947

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Fusing information from human observations can help robots overcome sensing limitations in collaborative tasks. However, an uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs. This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features and their relationships with spatial relation semantics. The model is trained as a probability estimator to capture a…

Nonlinear Treatment Effects in Shift-Share Designs
Luigi Garzon, Vitor Possebom
https://arxiv.org/abs/2507.21915 https://arxiv.org/pdf/2507.21915

Nonlinear Treatment Effects in Shift-Share Designs
We analyze heterogenous, nonlinear treatment effects in shift-share designs with exogenous shares. We employ a triangular model and correct for treatment endogeneity using a control function. Our tools identify four target parameters. Two of them capture the observable heterogeneity of treatment effects, while one summarizes this heterogeneity in a single measure. The last parameter analyzes counterfactual, policy-relevant treatment assignment mechanisms. We propose flexible parametric estimato…

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Yueh-Po Peng, Vincent K. M. Cheung, Li Su
https://arxiv.org/abs/2507.22378

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a tr…

Single-shot HDR using conventional image sensor shutter functions and optical randomization
Xiang Dai, Kyrollos Yanny, Kristina Monakhova, Nicholas Antipa
https://arxiv.org/abs/2506.22426

Single-shot HDR using conventional image sensor shutter functions and optical randomization
High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data into a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed image detail. These approaches struggle wi…

Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
Bereket A. Yilma, Luis A. Leiva
https://arxiv.org/abs/2507.21120 https://

Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression. Recently, Visual Art Recommender Systems (VA RecSys) have emerged to support AT, demonstrating their potential by personalizing therapeutic artwork recommendations. Nonetheless, current VA RecSys rely on visual stimuli for user modeling, limiting their ability to capture the full spectrum of emotional responses during preference elicitation. Previous studies have shown tha…