Tootfinder

{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats…

Wrap a function to capture errors — safely
Creates a modified version of .f that always succeeds. It returns a list with components result and error. If the function succeeds, result contains the returned value and error is NULL. If an error occurred, error is an error object and result is either NULL or otherwise.

"At the end of the day - who cares what you choose. Just choose something. Stop looking at it as 'blogging' or trying to 'capture an audience'."
Great piece by @… from a few years back on the importance of brain dumps and blogging:

Elliptic Curves and the Hopf Fibration
Nadir Hajouji, Steve Trettel
https://arxiv.org/abs/2505.09627 https://arxiv.org/pdf/2505.09627…

Elliptic Curves and the Hopf Fibration
By combining tools from different areas of mathematics, we obtain 3D visualizations of elliptic curves over different fields that faithfully capture the underlying algebra and geometry.

There is little about Scott Jennings,
or the words that come out of his mouth,
that corresponds to reality.
A decade into Trump’s capture of the Republican Party, Jennings is the premier crossover enabler.
He is perhaps CNN’s most recognizable face alongside its primetime anchors,
and he is feted on jokey, tough-talk shows like Bill Maher. 
But Jennings is a representation of our own malady,
a projection of a country whose political culture is now…

Scott Jennings in the Hyperreal and the Myth of the Escape Hatch
Jennings is like a simulation of a Trump enabler.

Weekend #Plankton Factoid 🦠🦐
Mating by copepods is a complicated process since they are always at risk of predation by other plankton or fishes. Male #copepods carefully mechanically and chemically track a receptive female, capture them, and deposit an adhesive spermatophore near the genital open…

Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity
Loann Peurey, Marvin Lavechin, Tarek Kunze, Manel Khentout, Lucas Gautheron, Emmanuel Dupoux, Alejandrina Cristia
https://arxiv.org/abs/2506.11075

Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity
Audio-recordings collected with a child-worn device are a fundamental tool in child language research. Long-form recordings collected over whole days promise to capture children's input and production with minimal observer bias, and therefore high validity. The sheer volume of resulting data necessitates automated analysis to extract relevant metrics for researchers and clinicians. This paper summarizes collective knowledge on this technique, providing entry points to existing resources. We als…

Wow, Canterbury is getting absolutely hammered with lighting right now!

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Akshar Tumu, Henrik I. Christensen, Marcell Vazquez-Chanlatte, Chikao Tsuchiya, Dhaval Bhanderi
https://arxiv.org/abs/2506.10317

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road…

Momentum Multi-Marginal Schr\"odinger Bridge Matching
Panagiotis Theodoropoulos, Augustinos D. Saravanos, Evangelos A. Theodorou, Guan-Horng Liu
https://arxiv.org/abs/2506.10168

Momentum Multi-Marginal Schrödinger Bridge Matching
Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology, and economics. Despite advancements in Bridge and Flow matching frameworks, current methodologies rely on pairwise interpolation between adjacent snapshots. This hinders their ability to capture long-range temporal dependencies and potentially affects the coherence of the inferred trajectories. To address these issues…

IMALL with a Mixed-State Modality: A Logical Approach to Quantum Computation
Kinnari Dave, Alejandro D\'iaz-Caro, Vladimir Zamdzhiev
https://arxiv.org/abs/2506.09545

IMALL with a Mixed-State Modality: A Logical Approach to Quantum Computation
We introduce a proof language for Intuitionistic Multiplicative Additive Linear Logic (IMALL), extended with a modality B to capture mixed-state quantum computation. The language supports algebraic constructs such as linear combinations, and embeds pure quantum computations within a mixed-state framework via B, interpreted categorically as a functor from a category of Hilbert Spaces to a category of finite-dimensional C*-algebras. Measurement arises as a definable term, not as a constant, and t…

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Chengxu Zuo, Jiawei Huang, Xiao Jiang, Yuan Yao, Xiangren Shi, Rui Cao, Xinyu Yi, Feng Xu, Shihui Guo, Yipeng Qin
https://arxiv.org/abs/2506.10580

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive absolute static assumption in IMU calibration, i.e., the coordinate drift RG'G and measurement offset RBS remain constant during the entire motion, thereby significantly expanding their application scenarios. Specifically, we achieve real-time estimation of RG'G and RBS under two relaxed assumptions: i) the matrices change negligibly in a short tim…

Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, \"Omer Faruk Akg\"ul, Enes Burak Bilgin, Ollie Liu, Deqing Fu, Willie Neiswanger
https://arxiv.org/abs/2506.09967

Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified…

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
Logan M. Brabson, Andrew J. Medford, David S. Sholl
https://arxiv.org/abs/2506.09256

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
Direct air capture (DAC) with solid sorbents such as metal-organic frameworks (MOFs) is a promising approach for negative carbon emissions. Computational materials screening can help identify promising materials from the vast chemical space of potential sorbents. Experiments have shown that MOF framework flexibility and deformation induced by adsorbate molecules can drastically affect adsorption properties such as capacity and selectivity. Force field (FF) models are commonly used as surrogates…

A Survey of Link Prediction in N-ary Knowledge Graphs
Jiyao Wei, Saiping Guan, Da Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
https://arxiv.org/abs/2506.08970

A Survey of Link Prediction in N-ary Knowledge Graphs
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significan…

Unlocking the mystery of Sr synthesis in the early Galaxy through analysis of barium isotopes in very metal-poor stars
T. M. Sitnova, L. Lombardo, L. I. Mashonkina, F. Rizzuti, G. Cescutti, C. J. Hansen, P. Bonifacio, E. Caffau, A. Koch-Hansen, G. Meynet, R. Fernandes de Melo
https://arxiv.org/abs/2506.10193

Unlocking the mystery of Sr synthesis in the early Galaxy through analysis of barium isotopes in very metal-poor stars
We determine the contributions of the rapid (r) and slow (s) neutron capture processes to the Ba isotope mixture, along with Ba, Eu, and Sr NLTE abundances, in a sample of very metal-poor stars. The selected stars formed before the contribution from the main s-process in low- and intermediate-mass stars became significant. Some of our sample stars are enhanced in Sr, with [Sr/Ba] reaching up to 0.7. These stars gained their high Sr abundance from a poorly understood process, which may appear to…

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Tahiya Chowdhury, Veronica Romero
https://arxiv.org/abs/2506.11072

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture b…

Statistical mechanics of fluids with hidden degrees of freedom
Masanari Shimada, Tetsuya J. Kobayashi
https://arxiv.org/abs/2506.10253 https://

Statistical mechanics of fluids with hidden degrees of freedom
Although coarse-grained models have been widely used to explain exotic phenomena in complex fluids, such as droplet formation in living cells, these conventional approaches often fail to capture the intricate microscopic degrees of freedom that such fluids inherently possess. In this study, we propose a model that incorporates distinct microscopic degrees of freedom and their interactions, without directly relying on conventional coarse-grained descriptions. By introducing two key assumptions, …

Structured Graph Representations for Visual Narrative Reasoning: A Hierarchical Framework for Comics
Yi-Chun Chen
https://arxiv.org/abs/2506.10008 https://…

Structured Graph Representations for Visual Narrative Reasoning: A Hierarchical Framework for Comics
This paper presents a hierarchical knowledge graph framework for the structured understanding of visual narratives, focusing on multimodal media such as comics. The proposed method decomposes narrative content into multiple levels, from macro-level story arcs to fine-grained event segments. It represents them through integrated knowledge graphs that capture semantic, spatial, and temporal relationships. At the panel level, we construct multimodal graphs that link visual elements such as charact…

Identity and Access Management for the Computing Continuum
Chalima Dimitra Nassar Kyriakidou, Athanasia Maria Papathanasiou, Vasilios A. Siris, Nikos Fotiou, George C. Polyzos, Eduardo C\'anovas Mart\'inez, Antonio Skarmeta
https://arxiv.org/abs/2506.09559

Identity and Access Management for the Computing Continuum
The computing continuum introduces new challenges for access control due to its dynamic, distributed, and heterogeneous nature. In this paper, we propose a Zero-Trust (ZT) access control solution that leverages decentralized identification and authentication mechanisms based on Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Additionally, we employ Relationship-Based Access Control (ReBAC) to define policies that capture the evolving trust relationships inherent in the contin…

This https://arxiv.org/abs/2505.00968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Tree-Sliced Wasserstein Distance with Nonlinear Projection
Tree-Sliced methods have recently emerged as an alternative to the traditional Sliced Wasserstein (SW) distance, replacing one-dimensional lines with tree-based metric spaces and incorporating a splitting mechanism for projecting measures. This approach enhances the ability to capture the topological structures of integration domains in Sliced Optimal Transport while maintaining low computational costs. Building on this foundation, we propose a novel nonlinear projectional framework for the Tre…

This https://arxiv.org/abs/2506.06155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

A Novel Large-scale Crop Dataset and Dual-stream Transformer Method for Fine-grained Hierarchical Crop Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series
Fine-grained crop classification is crucial for precision agriculture and food security monitoring. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these …

"Carbon Capture ‘Not Going to Happen,’ Top Fossil Fuel Advocate Predicts"
#CarbonCapture #Emissions #Climate

Carbon Capture ‘Not Going to Happen,’ Top Fossil Fuel Advocate Predicts
In audio obtained by DeSmog, Bjorn Lomborg told a Fraser Institute event in Vancouver that the technology is way too expensive to be viable.

Causality-aware Safety Testing for Autonomous Driving Systems
Wenbing Tang, Mingfei Cheng, Renzhi Wang, Yuan Zhou, Chengwei Liu, Yang Liu, Zuohua Ding
https://arxiv.org/abs/2506.08688

Causality-aware Safety Testing for Autonomous Driving Systems
Simulation-based testing is essential for evaluating the safety of Autonomous Driving Systems (ADSs). Comprehensive evaluation requires testing across diverse scenarios that can trigger various types of violations under different conditions. While existing methods typically focus on individual diversity metrics, such as input scenarios, ADS-generated motion commands, and system violations, they often fail to capture the complex interrelationships among these elements. This oversight leads to ga…

SAG-AFTRA ends the 300 day video game actors strike after reaching a tentative deal for a new Interactive Media Agreement with the major video game companies (Erik Hayden/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

SAG-AFTRA Suspends Strike Against Video Game Companies, Paving Way for Return to Work
The union had taken a stand on artificial intelligence, among other concerns, as it looked to advocate for voice and performance capture workers.

CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation
Zhao Chen, Yu Yu
https://arxiv.org/abs/2506.09688 https://

CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation
Accurate theoretical prediction for halo mass function across a broad cosmological space is crucial for the forthcoming China Space Station Telescope (CSST) observations, which will capture cosmological information from multiple probes, e.g., cluster abundance, and weak lensing. In this work, we quantify the percent-level impact of different mass binning schemes when measuring the differential halo mass function from simulations, and demonstrate that the cumulative form of the halo mass functio…

Age of Information in Unreliable Tandem Queues
Muthukrishnan Senthilkumar, Aresh Dadlani, Hina Tabassum
https://arxiv.org/abs/2506.09245 https://

Age of Information in Unreliable Tandem Queues
Stringent demands for timely information delivery, driven by the widespread adoption of real-time applications and the Internet of Things, have established the age of information (AoI) as a critical metric for quantifying data freshness. Existing AoI models often assume multi-hop communication networks with fully reliable nodes, which may not accurately capture scenarios involving node transmission failures. This paper presents an analytical framework for two configurations of tandem queue syst…

Power Diagram Enhanced Adaptive Isosurface Extraction from Signed Distance Fields
Pengfei Wang, Ziyang Zhang, Wensong Wang, Shuangmin Chen, Lin Lu, Shiqing Xin, Changhe Tu
https://arxiv.org/abs/2506.09579

Power Diagram Enhanced Adaptive Isosurface Extraction from Signed Distance Fields
Extracting high-fidelity mesh surfaces from Signed Distance Fields has become a fundamental operation in geometry processing. Despite significant progress over the past decades, key challenges remain namely, how to automatically capture the intricate geometric and topological structures encoded in the zero level set of SDFs. In this paper, we present a novel isosurface extraction algorithm that introduces two key innovations: 1. An incrementally constructed power diagram through the addition of…

Extensive Database of Spatial Ballistic Captures with Application to Lunar Trailblazer
Lorenzo Ano\`e, Roberto Armellin, Gregory Lantoine, Claudio Bombardelli
https://arxiv.org/abs/2506.09584

Extensive Database of Spatial Ballistic Captures with Application to Lunar Trailblazer
For low-energy missions to the Moon and beyond, Ballistic Capture has proven to be a valuable technique for enabling orbital insertion while alleviating propulsion system requirements. This approach offers two key advantages. First, it extends the insertion window, allowing multiple maneuver opportunities to mitigate potential failures at the nominal insertion point. Second, it enables the required insertion maneuver to be distributed across multiple revolutions, reducing propulsion system cons…

Did a smidge of cable management because the fiber runs to the QNAP needed a bit more slack. Things I love: being able to disconnect the connections to the iSCSI storage and have absolutely no loss of connectivity.
#homelab

A weighted quantum ensemble of homogeneous quantum classifiers
Emiliano Tolotti, Enrico Blanzieri, Davide Pastorello
https://arxiv.org/abs/2506.07810 https…

A weighted quantum ensemble of homogeneous quantum classifiers
Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifie…

Rule-Assisted Attribute Embedding
Sibo Zhao, Michael Bewong, Selasi Kwashie, Junwei Hu, Zaiwen Feng
https://arxiv.org/abs/2506.08314 https://

Rule-Assisted Attribute Embedding
Recommendation systems often overlook the rich attribute information embedded in property graphs, limiting their effectiveness. Existing graph convolutional network (GCN) models either ignore attributes or rely on simplistic triples, failing to capture deeper semantic structures. We propose RAE (Rule- Assisted Approach for Attribute Embedding), a novel method that improves recommendations by mining semantic rules from property graphs to guide attribute embedding. RAE per…

Enhancing semi-resolved CFD-DEM for dilute to dense particle-fluid systems: A point cloud based, two-step mapping strategy via coarse graining
Yuxiang Liu, Lu Jing, Xudong Fu, Huabin Shi
https://arxiv.org/abs/2506.09517

Enhancing semi-resolved CFD-DEM for dilute to dense particle-fluid systems: A point cloud based, two-step mapping strategy via coarse graining
Computational fluid dynamics and discrete element method (CFD-DEM) coupling is an efficient and powerful tool to simulate particle-fluid systems. However, current volume-averaged CFD-DEM relying on direct grid-based mapping between the fluid and particle phases can exhibit a strong dependence on the fluid grid resolution, becoming unstable as particles move across fluid grids, and can fail to capture pore fluid pressure effects in very dense granular systems. Here we propose a two-step mapping …

Designing lensless imaging systems to maximize information capture
Leyla A. Kabuli, Henry Pinkard, Eric Markley, Clara S. Hung, Laura Waller
https://arxiv.org/abs/2506.08513

Designing lensless imaging systems to maximize information capture
Mask-based lensless imaging uses an optical encoder (e.g. a phase or amplitude mask) to capture measurements, then a computational decoding algorithm to reconstruct images. In this work, we evaluate and design encoders based on the information content of their measurements using mutual information estimation. With this approach, we formalize the object-dependent nature of lensless imaging and study the interdependence between object sparsity, encoder multiplexing, and noise. Our analysis reveal…

Angular $k$-uniformity and the Hyperinvariance of Holographic Codes
Wanli Cheng
https://arxiv.org/abs/2506.06577 https://arxiv.org/pd…

Angular $k$-uniformity and the Hyperinvariance of Holographic Codes
Holographic quantum error-correcting codes, often realized through tensor network architectures, have emerged as compelling toy models for exploring bulk-boundary duality in AdS-CFT. By encoding bulk information into highly entangled boundary degrees of freedom, they capture key features of holography such as subregion duality, operator reconstruction, and complementary recovery. Among them, hyperinvariant tensor networks-characterized by the inclusion of edge tensors and the enforcement of mul…

This https://arxiv.org/abs/2505.02450 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder
Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the intrinsic dynamics. While existing methods capture key dynamics by encoding a low-dimensional latent space, they overlook the inherent multiscale structure of complex systems, making it difficult to accurately predict complex spatiotemporal evolution. Therefore, we propose a Multiscale Diffusion Prediction Network (M…

Came back from a 2 week vacation with 207 captured #orgmode headings with notes, ideas, bookmarks, ...
#PIM #Emacs

A Sound and Complete Characterization of Fair Asynchronous Session Subtyping
Mario Bravetti, Luca Padovani, Gianluigi Zavattaro
https://arxiv.org/abs/2506.06078

A Sound and Complete Characterization of Fair Asynchronous Session Subtyping
Session types are abstractions of communication protocols enabling the static analysis of message-passing processes. Refinement notions for session types are key to support safe forms of process substitution while preserving their compatibility with the rest of the system. Recently, a fair refinement relation for asynchronous session types has been defined allowing the anticipation of message outputs with respect to an unbounded number of message inputs. This refinement is useful to capture com…

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.

SPC to 3D: Novel View Synthesis from Binary SPC via I2I translation
Sumit Sharma, Gopi Raju Matta, Kaushik Mitra
https://arxiv.org/abs/2506.06890 https://

SPC to 3D: Novel View Synthesis from Binary SPC via I2I translation
Single Photon Avalanche Diodes (SPADs) represent a cutting-edge imaging technology, capable of detecting individual photons with remarkable timing precision. Building on this sensitivity, Single Photon Cameras (SPCs) enable image capture at exceptionally high speeds under both low and high illumination. Enabling 3D reconstruction and radiance field recovery from such SPC data holds significant promise. However, the binary nature of SPC images leads to severe information loss, particularly in te…

The Turn to Practice in Design Ethics: Characteristics and Future Research Directions for HCI Research
Gizem \"Oz, Christian Dindler, Sharon Lindberg
https://arxiv.org/abs/2506.06055

The Turn to Practice in Design Ethics: Characteristics and Future Research Directions for HCI Research
As emerging technologies continue to shape society, there is a growing emphasis on the need to engage with design ethics as it unfolds in practice to better capture the complexities of ethical considerations embedded in day-to-day work. Positioned within the broader "turn to practice" in HCI, the review characterizes this body of work in terms of its motivations, conceptual frameworks, methodologies, and contributions across a range of design disciplines and academic databases. The findings rev…

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Zheng Zhao, Clara Vania, Subhradeep Kayal, Naila Khan, Shay B. Cohen, Emine Yilmaz
https://arxiv.org/abs/2506.09902

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization--adapting to individual user preferences while completing tasks--remains challenging. Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented assistance. To address this, we introduce PersonaLens, a comprehensive benchmark for evalu…

This https://arxiv.org/abs/2504.19324 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Black hole absorption cross sections: Spin and Regge poles
We investigate the absorption of massless scalar, electromagnetic, and gravitational fields propagating in the Schwarzschild black hole geometry. Using complex angular momentum techniques, we first derive a representation of the absorption cross section that separates it into smooth background integrals and a discrete Regge pole series. This decomposition reveals the physical mechanisms underlying black hole absorption, including classical capture, surface wave interference near the photon sphe…

The Riemann problem for three-phase foam flow in porous media
Luis Fernando Lozano, Grigori Chapiro, Dan Marchesin
https://arxiv.org/abs/2506.08152 https:/…

The Riemann problem for three-phase foam flow in porous media
Gas injection in the context of the three-phase flow in porous media appears in applications such as Enhanced Oil Recovery, aquifer remediation, and carbon capture, utilization, and storage (CCUS). In general, this technique suffers from a difficulty related to excessive gas mobility, which can be circumvented by using foam. This study addresses the non-linear system of differential equations describing the three-phase foam flow based on Corey relative permeability functions. A major obstacle i…

Propagation and Dynamics of Oscillating Tetraquark Systems
Sh. Janjan, G. R. Boroun
https://arxiv.org/abs/2506.07396 https://arxiv.or…

Propagation and Dynamics of Oscillating Tetraquark Systems
We have presented a theoretical investigation into the behavior of tetraquark states. These states are modeled as a non-relativistic four-body quantum system governed by harmonic interactions. Our goal is to capture the fundamental dynamics of quark-antiquark interactions in the tetraquark configuration. Within this framework, we have derived an analytical expression for the quantum propagator. This expression was then used to compute the time evolution of the wave function and the associated p…

Measurement of radionuclide production probabilities in negative muon nuclear capture and validation of Monte Carlo simulation model
Y. Yamaguchi, M. Niikura, R. Mizuno, M. Tampo, M. Harada, N. Kawamura, I. Umegaki, S. Takeshita, K. Haga
https://arxiv.org/abs/2506.08301

Measurement of radionuclide production probabilities in negative muon nuclear capture and validation of Monte Carlo simulation model
As part of the development of a sample radioactivity calculation program, we have measured radionuclide production probabilities in negative muon nuclear capture to update experimental data and to validate a calculation dataset obtained by a Monte Carlo simulation code. The probabilities have been obtained by an activation experiment on $^{27}$Al, $^\mathrm{nat}$Si, $^{59}$Co, and $^\mathrm{nat}$Ta targets. The obtained probabilities expand the validation scope to the radionuclide production pr…

Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
Tarek Kunze, Marianne M\'etais, Hadrien Titeux, Lucas Elbert, Joseph Coffey, Emmanuel Dupoux, Alejandrina Cristia, Marvin Lavechin
https://arxiv.org/abs/2506.11074

Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
Recordings gathered with child-worn devices promised to revolutionize both fundamental and applied speech sciences by allowing the effortless capture of children's naturalistic speech environment and language production. This promise hinges on speech technologies that can transform the sheer mounds of data thus collected into usable information. This paper demonstrates several obstacles blocking progress by summarizing three years' worth of experiments aimed at improving one fundamental task: V…

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 …

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

Smallest Suffixient Sets as a Repetitiveness Measure
Gonzalo Navarro, Giuseppe Romana, Cristian Urbina
https://arxiv.org/abs/2506.05638 https://

Smallest Suffixient Sets as a Repetitiveness Measure
Suffixient sets are a novel combinatorial object that capture the essential information of repetitive strings in a way that, provided with a random-access mechanism, supports various forms of pattern matching. In this paper we study the size $χ$ of the smallest suffixient set as a repetitiveness measure: we place it between known measures and study its sensitivity to various string operations.

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://

On Level Crossings and Fade Durations in von Mises-Fisher Scattering Channels
Kenan Turbic, Slawomir Stanczak
https://arxiv.org/abs/2506.05898 https://

On Level Crossings and Fade Durations in von Mises-Fisher Scattering Channels
This paper investigates the second-order statistics of multipath fading channels with von Mises-Fisher (vMF) distributed scatters. Simple closed-form expressions for the mean Doppler shift and Doppler spread are derived as the key spectral moments that capture the impact of mobility and scattering characteristics on level crossings and fade durations. These expressions are then used to analyze the influence of vMF parameters on the Level-Crossing Rate (LCR) The results show that isotropic scatt…

Yule-Walker Estimation for Functional Time Series in Hilbert Space
Ying Niu, Yuwei Zhao, Zhao Chen, Christina Dan Wang
https://arxiv.org/abs/2506.05922 htt…

Yule-Walker Estimation for Functional Time Series in Hilbert Space
Recent advances in data collection technologies have led to the widespread availability of functional data observed over time, often exhibiting strong temporal dependence. However, existing methodologies typically assume independence across functions or impose restrictive low-order dependence structures, limiting their ability to capture the full dynamics of functional time series. To address this gap, we investigate higher-order functional autoregressive (FAR) models in Hilbert spaces, focusin…

Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting
Xuepeng Chen, Jing-Jia Luo, Qingqing Li, Fan Meng
https://arxiv.org/abs/2506.06638

Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting
Rapid intensification (RI) of tropical cyclones (TCs) poses a great challenge due to their highly nonlinear dynamics and inherent uncertainties. Conventional statistical dynamics and artificial intelligence prediction models typically rely on static parameterization schemes, which limits their ability to capture the non-stationary error structure in the intensity evolution. To address this issue, we propose a physically-inspired covariate adaptive conformal prediction framework that dynamically…

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Zaiqiang Wu, Yechen Li, Jingyuan Liu, Yuki Shibata, Takayuki Hori, I-Chao Shen, Takeo Igarashi
https://arxiv.org/abs/2506.10468

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Existing image-based virtual try-on methods are often limited to the front view and lack real-time performance. While per-garment virtual try-on methods have tackled these issues by capturing per-garment datasets and training per-garment neural networks, they still encounter practical limitations: (1) the robotic mannequin used to capture per-garment datasets is prohibitively expensive for widespread adoption and fails to accurately replicate natural human body deformation; (2) the synthesized …

Ensemencer les océans avec du fer pour stimuler la capture de CO2 est une idée intéressante, mais les inconnues sont encore trop nombreuses pour se lancer dans cette voie Š l'échelle industrielle.
Explications.
https://theconversation.co…

If you're trying to do the trick that was shared for #nintendoswitch2 where you can use a USB HDMI capture dongle HDMI to USB-C cable to use your iPhone as a camera - don't use the True Visage app mentioned in the video
That will rely on screen sharing mirroring and you'll potentially get an aspect ratio mismatch and see the dynamic island floating in your view
I'd personally suggest the free "Blackmagic Camera" app instead, and then go to:
Settings->Monitor->HDMI Out
Switch from Mirror Display to Video Feed and it will cleanly output your camera. Works for a Lightning to HDMI out adapter for older phones, too
Then just tap on Lense and FPS and pick which combo you like.
Also, if you're not getting any output signal make sure the capture dongle you're using supports 1080p out - not sure the Switch 2 supports anything higher as camera input

This https://arxiv.org/abs/2506.04354 has been replaced.
initial toot: https://mastoxiv.page/@ar…

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields
Amit Kumar Jha
https://arxiv.org/abs/2506.06317

A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields
This study is motivated by empirical observations of periodic fluctuations in interest rates, notably long-term economic cycles spanning decades, which the conventional Hull-White short-rate model fails to adequately capture. To address this limitation, we propose an extension that incorporates a sinusoidal, time-varying mean reversion speed, allowing the model to reflect cyclic interest rate dynamics more effectively. The model is calibrated using a comprehensive dataset of daily U.S. Treasu…

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
Shirui Zhou, Shiteng Zheng, Junfang Tian, Rui Jiang, and H. M. Zhang
https://arxiv.org/abs/2506.05909

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions fac…

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
Gabriele Codega, Anna Ivagnes, Nicola Demo, Gianluigi Rozza
https://arxiv.org/abs/2506.09830

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
In the present work, we introduce a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models (ROMs). In particular, we focus on ROMs built using Proper Orthogonal Decomposition (POD) in an under-resolved and marginally-resolved regime, i.e. when the number of modes employed is not enough to capture the system dynamics. We propose a method to re-introduce the contribution of neglected modes through a quadratic correction term, given by the action of a quadratic operator…

This https://arxiv.org/abs/2409.17458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking
The rapid progress of Large Language Models (LLMs) has opened up new opportunities across various domains and applications; yet it also presents challenges related to potential misuse. To mitigate such risks, red teaming has been employed as a proactive security measure to probe language models for harmful outputs via jailbreak attacks. However, current jailbreak attack approaches are single-turn with explicit malicious queries that do not fully capture the complexity of real-world interactions…

Thermoelectric energy conversion in molecular junctions out of equilibrium
R. Tuovinen, Y. Pavlyukh
https://arxiv.org/abs/2506.06129 https://

Thermoelectric energy conversion in molecular junctions out of equilibrium
Understanding time-resolved quantum transport is crucial for developing next-generation quantum technologies, particularly in nano- and molecular junctions subjected to time-dependent perturbations. Traditional steady-state approaches to quantum transport are not designed to capture the transient dynamics necessary for controlling electronic behavior at ultrafast time scales. In this work, we present a non-equilibrium Green's function formalism, within the recently-developed iterated generalize…

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06021

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit mod…

I've been thinking a lot lately about how porous our identities are: how we are shaped by every experience, person, bit of information.
How this shaping means losing parts of ourselves...or at least who we would have been without external influences.
It brought to mind a piece I wrote about the drive to retrieve, catalogue & preserve a sense of self amidst this loss of #identity

Recollecting Hailey
I’m trying to capture myself. I can feel myself disintegrating. I feel my atoms dispersed. I am disappearing.

BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading
Jonathan Schmidt, Simon Giebenhain, Matthias Niessner
https://arxiv.org/abs/2506.06271

BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading
We introduce BecomingLit, a novel method for reconstructing relightable, high-resolution head avatars that can be rendered from novel viewpoints at interactive rates. Therefore, we propose a new low-cost light stage capture setup, tailored specifically towards capturing faces. Using this setup, we collect a novel dataset consisting of diverse multi-view sequences of numerous subjects under varying illumination conditions and facial expressions. By leveraging our new dataset, we introduce a new …

Sequence Modeling for N-Agent Ad Hoc Teamwork
Caroline Wang, Di Yang Shi, Elad Liebman, Ishan Durugkar, Arrasy Rahman, Peter Stone
https://arxiv.org/abs/2506.05527

Sequence Modeling for N-Agent Ad Hoc Teamwork
N-agent ad hoc teamwork (NAHT) is a newly introduced challenge in multi-agent reinforcement learning, where controlled subteams of varying sizes must dynamically collaborate with varying numbers and types of unknown teammates without pre-coordination. The existing learning algorithm (POAM) considers only independent learning for its flexibility in dealing with a changing number of agents. However, independent learning fails to fully capture the inter-agent dynamics essential for effective colla…

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
Liyuan Liang, Xinyi Chen, Gan Luo, Kun Yuan
https://arxiv.org/abs/2506.04600

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed for doubly-stochastic and column-stochastic mixing matrices, the row-stochastic scenario remains unexplored. This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-sto…

Paraconsistent Relations as a Variant of Kleene Algebras
Juliana Cunha, Alexandre Madeira, Lu\'is S. Barbosa
https://arxiv.org/abs/2506.05840 https://

Paraconsistent Relations as a Variant of Kleene Algebras
Kleene algebras (KA) and Kleene algebras with tests (KAT) provide an algebraic framework to capture the behavior of conventional programming constructs. This paper explores a broader understanding of these structures, in order to enable the expression of programs and tests yielding vague or inconsistent outcomes. Within this context, we introduce the concept of a paraconsistent Kleene Algebra with tests (PKAT), capable of capturing vague and contradictory computations. Finally, to establish the…

Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment
Amritha Premkumar, Prajit T Rajendran, Vignesh V Menon
https://arxiv.org/abs/2506.09795

Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment
This paper presents a novel approach for reduced-reference video quality assessment (VQA), developed as part of the recent VQA Grand Challenge. Our method leverages low-level complexity and structural information from reference and test videos to predict perceptual quality scores. Specifically, we extract spatio-temporal features using Video Complexity Analyzer (VCA) and compute SSIM values from the test video to capture both texture and structural characteristics. These features are aggregated…

"A California dairy tried to capture its methane, and it worked"
#US #USA #America #Methane ´

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.

Nonlinear input-output analysis of transitional shear flows using small-signal finite-gain $\mathcal{L}_p$ stability
Zhengyang Wei, Chang Liu
https://arxiv.org/abs/2506.08129

Nonlinear input-output analysis of transitional shear flows using small-signal finite-gain $\mathcal{L}_p$ stability
Input-output analysis has been widely used to predict the transition to turbulence in wall-bounded shear flows, but it typically does not capture the full nonlinear effects involved. This work employs the Small-Signal Finite-Gain (SSFG) Lp stability theorem to assess nonlinear input-output amplification and demonstrates its applicability using a nine-mode shear flow model with a random body force. Our analysis employs Linear Matrix Inequalities (LMI) and Sum-of-Squares (SOS) as the primary tool…

This https://arxiv.org/abs/2501.07121 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies
Grid-scale battery energy storage systems (BESSs) can provide flexibility to the power system and capture shortterm price volatility by shifting energy in time through controlled charging and discharging. The highly volatile European continuous intraday (CID) market allows trading until just a few minutes before physical delivery, offering significant earning potential. However, its high trading frequency poses substantial modeling challenges. Accurate modeling of BESSs trading in the CID marke…

Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendk\^uuni C. Ou\'edraogo, Yinghua Li, Xueqi Dang, Xin Zhou, Anil Koyuncu, Jacques Klein, David Lo, Tegawend\'e F. Bissyand\'e
https://arxiv.org/abs/2506.06767

Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Large Language Models (LLMs) are increasingly employed to automatically refactor unit tests, aiming to enhance readability, naming, and structural clarity while preserving functional behavior. However, evaluating such refactorings remains challenging: traditional metrics like CodeBLEU are overly sensitive to renaming and structural edits, whereas embedding-based similarities capture semantics but ignore readability and modularity. We introduce CTSES, a composite metric that integrates CodeBLEU,…

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Mengke Li, Matthew Mumpower, Nicole Vassh, William Samuel Porter, Rebecca Surman
https://arxiv.org/abs/2506.06464

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which…

This https://arxiv.org/abs/2506.00759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly involving personally identifiable information (PII). Although previous studies have shown that this risk can be mitigated through methods such as privacy neurons, they all assume that both the (sensitive) training data and user queries are in English. We show that they cannot defend against the privacy leakage in cro…

This https://arxiv.org/abs/2410.21225 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Boosting HI-Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations
After reionization, neutral hydrogen (HI) traces the large-scale structure (LSS) of the Universe, enabling HI intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study HI clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of $k$-nearest neighbor ($k$NN) optical galaxies and the HI brightness tempe…

Neighborhood Overlap-Aware High-Order Graph Neural Network for Dynamic Graph Learning
Ling Wang
https://arxiv.org/abs/2506.06728 https://

Neighborhood Overlap-Aware High-Order Graph Neural Network for Dynamic Graph Learning
Dynamic graph learning (DGL) aims to learn informative and temporally-evolving node embeddings to support downstream tasks such as link prediction. A fundamental challenge in DGL lies in effectively modeling both the temporal dynamics and structural dependencies of evolving graph topologies. Recent advances in Dynamic Graph Neural Networks (DGNNs) have obtained remarkable success by leveraging message-passing mechanisms to capture pairwise node interactions. However, these approaches often over…

This https://arxiv.org/abs/2309.05677 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Optical trapping based on microring resonators with transverse slot structure
Over the past few decades, optical manipulation has emerged as a highly successful tool in various fields, such as biology, micro/nanorobotics, and physics. Among the different techniques, the transverse slot optical waveguide has shown remarkable potential in enhancing the field and significantly improving optical trapping capabilities. Additionally, microring resonators have demonstrated the ability to enhance the field at specific resonance wavelengths, enabling the manipulation and capture …

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha V, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai
https://arxiv.org/abs/2506.06099…

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively cu…

Obfuscation-Resilient Binary Code Similarity Analysis using Dominance Enhanced Semantic Graph
Yufeng Wang, Yuhong Feng, Yixuan Cao, Haoran Li, Haiyue Feng, Yifeng Wang
https://arxiv.org/abs/2506.06161

Obfuscation-Resilient Binary Code Similarity Analysis using Dominance Enhanced Semantic Graph
Binary code similarity analysis (BCSA) serves as a core technique for binary analysis tasks such as vulnerability detection. While current graph-based BCSA approaches capture substantial semantics and show strong performance, their performance suffers under code obfuscation due to the unstable control flow. To address this issue, we develop ORCAS, an Obfuscation-Resilient BCSA model based on Dominance Enhanced Semantic Graph (DESG). The DESG is an original binary code representation, capturing …

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

This https://arxiv.org/abs/2505.10033 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected…

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren, Danula Hettiachchi, Jeffrey Chan
https://arxiv.org/abs/2506.04551

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulati…

This https://arxiv.org/abs/2503.01276 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A hierarchical approach for multicontinuum homogenization in high contrast media
A recently developed upscaling technique, the multicontinuum homogenization method, has gained significant attention for its effectiveness in modeling complex multiscale systems. This method defines multiple continua based on distinct physical properties and solves a series of constrained cell problems to capture localized information for each continuum. However, solving all these cell problems on very fine grids at every macroscopic point is computationally expensive, which is a common limitat…

This https://arxiv.org/abs/2504.10200 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Stability analysis of discrete Boltzmann simulation for supersonic flows: Influencing factors, coupling mechanisms and optimization strategies
Supersonic flow simulations face challenges in trans-scale modeling, numerical stability, and complex field analysis due to inherent nonlinear, nonequilibrium, and multiscale characteristics. The discrete Boltzmann method (DBM) provides a multiscale kinetic modeling framework and analysis tool to capture complex discrete/nonequilibrium effects. While the numerical scheme plays a fundamental role in DBM simulations, a comprehensive stability analysis remains lacking. Similar to LBM, complexity m…

Applying Informer for Option Pricing: A Transformer-Based Approach
Feliks Ba\'nka, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2506.05565 https://

Applying Informer for Option Pricing: A Transformer-Based Approach
Accurate option pricing is essential for effective trading and risk management in financial markets, yet it remains challenging due to market volatility and the limitations of traditional models like Black-Scholes. In this paper, we investigate the application of the Informer neural network for option pricing, leveraging its ability to capture long-term dependencies and dynamically adjust to market fluctuations. This research contributes to the field of financial forecasting by introducing Info…

This https://arxiv.org/abs/2504.06212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

NNN: Next-Generation Neural Networks for Marketing Measurement
We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (MMMs) which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, potentially enables NNN to model complex interactions, capture long-term effects, and improve sales attributi…

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Yi Huang, Wajih UI Hassan, Yao Guo, Xiangqun Chen, Ding Li
https://arxiv.org/abs/2506.06226

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats (APTs), by exposing complex attack patterns. While recent systems combine graph neural networks (GNNs) with natural language processing (NLP) to capture structural and semantic features, their effectiveness is limited by class imbalance in real-world data. To address this, we introduce PROVSYN, an automated framework that synthesizes provenance graphs through a three-phase pipel…

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Yao Ni, Song Wen, Piotr Koniusz, Anoop Cherian
https://arxiv.org/abs/2506.06483

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, we propose two auxiliary consistency losses for diffusion fine-tuning. First, a prior consistency regul…

On the Wasserstein Geodesic Principal Component Analysis of probability measures
Nina Vesseron, Elsa Cazelles, Alice Le Brigant, Thierry Klein
https://arxiv.org/abs/2506.04480

On the Wasserstein Geodesic Principal Component Analysis of probability measures
This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesic curves in the space of probability measures that best capture the modes of variation of the underlying dataset. We first address the case of a collection of Gaussian distributions, and show how to lift the computations in the space of invertible linear maps. For the more general setting of absolutely continuous prob…

This https://arxiv.org/abs/2505.01651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rat…

This https://arxiv.org/abs/2503.17797 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Enhancing Fourier Neural Operators with Local Spatial Features
Partial Differential Equation (PDE) problems often exhibit strong local spatial structures, and effectively capturing these structures is critical for approximating their solutions. Recently, the Fourier Neural Operator (FNO) has emerged as an efficient approach for solving these PDE problems. By using parametrization in the frequency domain, FNOs can efficiently capture global patterns. However, this approach inherently overlooks the critical role of local spatial features, as frequency-domain…

RewardBench 2: Advancing Reward Model Evaluation
Saumya Malik, Valentina Pyatkin, Sander Land, Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Nathan Lambert
https://arxiv.org/abs/2506.01937

RewardBench 2: Advancing Reward Model Evaluation
Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from the development of benchmarks that test capabilities in specific skill areas to others that test agreement with human preferences. At the same time, progress in evaluation has not…

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Yuxuan Han, Junfeng Lyu, Kuan Sheng, Minghao Que, Qixuan Zhang, Lan Xu, Feng Xu
https://arxiv.org/abs/2506.03478

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Existing facial appearance capture methods can reconstruct plausible facial reflectance from smartphone-recorded videos. However, the reconstruction quality is still far behind the ones based on studio recordings. This paper fills the gap by developing a novel daily-used solution with a co-located smartphone and flashlight video capture setting in a dim room. To enhance the quality, our key observation is to solve facial reflectance maps within the data distribution of studio-scanned ones. Spec…

This https://arxiv.org/abs/2412.13147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performances and real-world applications. We attribute this gap primarily to current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, especially in complex reasoning tasks where both accuracy and consistency are essential. In this paper, we introduce G-Pass@$k$, a novel evaluat…

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Robert J. Joyce, Gideon Miller, Phil Roth, Richard Zak, Elliott Zaresky-Williams, Hyrum Anderson, Edward Raff, James Holt
https://arxiv.org/abs/2506.05074

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow scope - most include files targeting a single platform, have labels supporting just one type of malware classification task, and make no effort to capture the evasive files that make malware detection difficult in practice. We present EMBER2024, a new dataset that enables holisti…

This https://arxiv.org/abs/2502.17701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

This https://arxiv.org/abs/2411.02006 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile…

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, Andr\'as Luk\'acs
https://arxiv.org/abs/2506.02048

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Large Language Models (LLMs) still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag (CTF) challenge generator framework that we use to fine-tune a tool-augmented Llama-3.1-8B with Guided Reinforcement Prompt Optimisation (GRPO), allowing the agent to iteratively write and execute Python inside an isolated REPL. GRPO yields a +53% absolute jum…

CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale
Zhun Wang, Tianneng Shi, Jingxuan He, Matthew Cai, Jialin Zhang, Dawn Song
https://arxiv.org/abs/2506.02548

CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale
Large language model (LLM) agents are becoming increasingly skilled at handling cybersecurity tasks autonomously. Thoroughly assessing their cybersecurity capabilities is critical and urgent, given the high stakes in this domain. However, existing benchmarks fall short, often failing to capture real-world scenarios or being limited in scope. To address this gap, we introduce CyberGym, a large-scale and high-quality cybersecurity evaluation framework featuring 1,507 real-world vulnerabilities fo…