Tootfinder

Supreme Court turns away RNC challenge to Pennsylvania ballot ruling
https://thehill.com/regulation/court-battles/5338008-supreme-court-rnc-pennsylvania-challenge/

Supreme Court turns away RNC challenge to Pennsylvania ballot ruling (Zach Schonfeld/The Hill)
https://thehill.com/regulation/court-battles/5338008-supreme-court-rnc-pennsylvania-challenge/
http://www.memeorandum.com/250606/p143#a250606p143

This https://arxiv.org/abs/2506.02197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the…

Trump foe Judge Boasberg rules deported migrants can challenge removals, in blow to admin (Fox News)
https://www.foxnews.com/politics/trump-foe-judge-boasberg-rules-deported-migrants-can-challenge-removals-blow-admin
http://www.memeorandum.com/250605/p114#a250605p114

This https://arxiv.org/abs/2503.21224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel family of multilevel Monte Carlo (MLMC) algorithms that integrate fixed-point iteration with MLMC techniques and a generic stochastic approximat…

The US supreme court on Friday permitted DOGE, the so-called ‘department of government efficiency’, broad access to personal information on millions of Americans in Social Security Administration data systems while a legal challenge plays out.
At the request of the Justice Department, the justices put on hold US district judge Ellen Hollander’s order that had largely blocked Doge’s access to “personally identifiable information”
in data such as medical and financial records while …

Ranking 2025 NFL Offensive Rookie of the Year candidates, plus best bets: Who can challenge Ashton Jeanty?

https://www.cbssports.com/nfl/news/rank…

Fight Club sells short the challenge of building an engaged audience for one's work.

Cross-attention and Self-attention for Audio-visual Speaker Diarization in MISP-Meeting Challenge
Zhaoyang Li, Haodong Zhou, Longjie Luo, Xiaoxiao Li, Yongxin Chen, Lin Li, Qingyang Hong
https://arxiv.org/abs/2506.02621

Cross-attention and Self-attention for Audio-visual Speaker Diarization in MISP-Meeting Challenge
This paper presents the system developed for Task 1 of the Multi-modal Information-based Speech Processing (MISP) 2025 Challenge. We introduce CASA-Net, an embedding fusion method designed for end-to-end audio-visual speaker diarization (AVSD) systems. CASA-Net incorporates a cross-attention (CA) module to effectively capture cross-modal interactions in audio-visual signals and employs a self-attention (SA) module to learn contextual relationships among audio-visual frames. To further enhance p…

This https://arxiv.org/abs/2506.01297 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MobCLIP: Learning General-purpose Geospatial Representation at Scale
Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence. Current embedding methods often lack versatility, limiting their utility across diverse tasks in both human and natural domains. We present MobCLIP, the first nationwide general-purpose location encoder, integrating an unprecedented diversity of data modalities through effective and scalable multimodal fusion. Adopting a novel CLIP-based architecture, our framework aligns 100M+…

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

This https://arxiv.org/abs/2502.13033 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Classical notions of computation and the Hasegawa-Thielecke theorem
In the spirit of the Curry-Howard correspondence between proofs and programs, we define and study a syntax and semantics for classical logic equipped with a computationally involutive negation, using a polarised effect calculus. A main challenge in designing a denotational semantics is to accommodate both call-by-value and call-by-name evaluation strategies, which leads to a failure of associativity of composition. Building on the work of the third author, we devise the notion of dialogue duplo…

This https://arxiv.org/abs/2501.19118 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Towards numerically exact computation of conductivity in the thermodynamic limit of interacting lattice models
Computing dynamical response functions in interacting lattice models is a long standing challenge in condensed matter physics. In view of recent results, the dc resistivity $ρ_\mathrm{dc}$ in the weak coupling regime of the Hubbard model is of great interest, yet it is not fully understood. The challenge lies in having to work with large lattices while avoiding analytical continuation. The weak-coupling $ρ_\mathrm{dc}$ results were so far computed at the level of the Boltzmann theory and at t…

This https://arxiv.org/abs/2405.10886 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Three-mode tunable coupler for superconducting two-qubit gates
Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction control. We experimentally demonstrate the native CZ gate with the pulse duration of 60 ns achieving the two-qubit gate fidelity above 98%,…

This https://arxiv.org/abs/2505.22769 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking
Mobile gaze tracking faces a fundamental challenge: maintaining accuracy as users naturally change their postures and device orientations. Traditional calibration approaches, like one-off, fail to adapt to these dynamic conditions, leading to degraded performance over time. We present MAC-Gaze, a Motion-Aware continual Calibration approach that leverages smartphone Inertial measurement unit (IMU) sensors and continual learning techniques to automatically detect changes in user motion states and…

OpenCCA: An Open Framework to Enable Arm CCA Research
Andrin Bertschi, Shweta Shinde
https://arxiv.org/abs/2506.05129 https://arxiv.o…

OpenCCA: An Open Framework to Enable Arm CCA Research
Confidential computing has gained traction across major architectures with Intel TDX, AMD SEV-SNP, and Arm CCA. Unlike TDX and SEV-SNP, a key challenge in researching Arm CCA is the absence of hardware support, forcing researchers to develop ad-hoc performance prototypes on non-CCA Arm boards. This approach leads to duplicated efforts, inconsistent performance comparisons, and high barriers to entry. To address this, we present OpenCCA, an open research platform that enables the execution of CC…

This https://arxiv.org/abs/2505.02945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Cognitive Foundations of Economic Exchange: A Modular Framework Grounded in Behavioral Evidence
A key challenge in multi-agent AI is modeling social cooperation under realistic behavioral constraints. Many foundational concepts in economics and ethics such as "trust" or "morality" are often defined informally, without operational criteria or cognitive grounding, which limits their testability and implementation in artificial agents. Drawing on converging empirical evidence from primate behavior, infant cognition, and economic anthropology, we propose a conceptual framework composed of thr…

This https://arxiv.org/abs/2505.17836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
This paper addresses the problem of robust estimation in gossip algorithms over arbitrary communication graphs. Gossip algorithms are fully decentralized, relying only on local neighbor-to-neighbor communication, making them well-suited for situations where communication is constrained. A fundamental challenge in existing mean-based gossip algorithms is their vulnerability to malicious or corrupted nodes. In this paper, we show that an outlier-robust mean can be computed by globally estimating …

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…

This https://arxiv.org/abs/2502.18470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Spatial Reasoning Questions
Spatial reasoning remains a challenge for Large Language Models (LLMs), which struggle with spatial data retrieval and reasoning. We propose Spatial Retrieval-Augmented Generation (Spatial-RAG), a framework that extends RAG to spatial tasks by integrating sparse spatial retrieval (spatial databases) and dense semantic retrieval (LLM-based similarity). A multi-objective ranking strategy balances spatial constraints and semantic relevance, while an LLM-guided generator ensures coherent responses.…

This https://arxiv.org/abs/2504.18791 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Nonconvex Linear System Identification with Minimal State Representation
Low-order linear System IDentification (SysID) addresses the challenge of estimating the parameters of a linear dynamical system from finite samples of observations and control inputs with minimal state representation. Traditional approaches often utilize Hankel-rank minimization, which relies on convex relaxations that can require numerous, costly singular value decompositions (SVDs) to optimize. In this work, we propose two nonconvex reformulations to tackle low-order SysID (i) Burer-Monterio…

Probability of Collision with Tethered Spacecraft
Yema Paul, Emmanuel Delande, Francois Vinet, Francois Laporte, Manuel Sanjurjo-Rivo, Aldo Tonnini, Joan-Pau Sanchez
https://arxiv.org/abs/2506.04969

Probability of Collision with Tethered Spacecraft
This Engineering Note addresses the challenge of estimating the probability of collision for tethered spacecraft during close encounters with other space objects. Standard probability of collision methods, based on spherical hard-body assumptions, tend to be overly conservative when applied to long tether systems. We introduce a method that accounts for the tether's spatial extent and configuration uncertainty by maximizing the probability of collision over all physically admissible tether shap…

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series
Sara Rutten, Bryan Sumalinab, Oswaldo Gressani, Thomas Neyens, Elisa Duarte, Niel Hens, Christel Faes
https://arxiv.org/abs/2506.04814

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series
Distributed lag non-linear models (DLNM) have gained popularity for modeling nonlinear lagged relationships between exposures and outcomes. When applied to spatially referenced data, these models must account for spatial dependence, a challenge that has yet to be thoroughly explored within the penalized DLNM framework. This gap is mainly due to the complex model structure and high computational demands, particularly when dealing with large spatio-temporal datasets. To address this, we propose a…

Indoor Sharing in the Mid-Band: A Performance Study of Neutral-Host, Cellular Macro, and Wi-Fi
Joshua Roy Palathinkal, Muhammad Iqbal Rochman, Vanlin Sathya, Mehmet Yavuz, Monisha Ghosh
https://arxiv.org/abs/2506.04974

Indoor Sharing in the Mid-Band: A Performance Study of Neutral-Host, Cellular Macro, and Wi-Fi
Indoor environments present a significant challenge for wireless connectivity, as immense data demand strains traditional solutions. Public Mobile Network Operators (MNOs), utilizing outdoor macro base stations (BSs), suffer from poor signal penetration. Indoor Wi-Fi networks, on the other hand, may face reliability issues due to spectrum contention. Shared spectrum models, particularly the Citizens Broadband Radio Service (CBRS) utilized by private 4G/5G networks, have emerged as a promising a…

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Bruno Deprez, Bart Baesens, Tim Verdonck, Wouter Verbeke
https://arxiv.org/abs/2506.04292

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Money laundering poses a significant challenge as it is estimated to account for 2%-5% of the global GDP. This has compelled regulators to impose stringent controls on financial institutions. One prominent laundering method for evading these controls, called smurfing, involves breaking up large transactions into smaller amounts. Given the complexity of smurfing schemes, which involve multiple transactions distributed among diverse parties, network analytics has become an important anti-money la…

Trust the process: mapping data-driven reconstructions to informed models using stochastic processes
Stefano Rinaldi, Alexandre Toubiana, Jonathan R. Gair
https://arxiv.org/abs/2506.05153

Trust the process: mapping data-driven reconstructions to informed models using stochastic processes
Gravitational-wave astronomy has entered a regime where it can extract information about the population properties of the observed binary black holes. The steep increase in the number of detections will offer deeper insights, but it will also significantly raise the computational cost of testing multiple models. To address this challenge, we propose a procedure that first performs a non-parametric (data-driven) reconstruction of the underlying distribution, and then remaps these results onto a …

IT specialists stop calling them MAC instead of Mac challenge: level impossible.

This https://arxiv.org/abs/2503.02407 has been replaced.
initial toot: https://mastoxiv.page/@a…

Wyckoff Transformer: Generation of Symmetric Crystals
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making the consistent generation of stable and symmetrically valid crystal structures a significant challenge. We introduce WyFormer, a generative …

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong, Rampi Ramprasad
https://arxiv.org/abs/2506.04233

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design …

This https://arxiv.org/abs/2502.17327 has been replaced.
link: https://scholar.google.com/scholar?q=a

AnyTop: Character Animation Diffusion with Any Topology
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular nature of the data. In this work, we introduce AnyTop, a diffusion model that generates motions for diverse characters with distinct motion dynamics, using only their skeletal structure as input. Our work features a transformer-based denoising network, tailored for arbitrary skeleton learning, integrating topology info…

Selective Excitation of Coupled Resonators via Complex Frequency Driving: Enhanced Efficiency and Crosstalk Suppression
Deepanshu Trivedi, Laraib Niaz, Andrea Al\`u, Alex Krasnok
https://arxiv.org/abs/2506.02207

Selective Excitation of Coupled Resonators via Complex Frequency Driving: Enhanced Efficiency and Crosstalk Suppression
Controlling individual elements of coupled resonator systems poses a significant challenge, as conventional real-frequency pulses suffer from inefficiency and crosstalk, limiting fidelity and scalability. To address this challenge, we propose and explore the use of complex frequency excitations, tailoring the driving signal waveform to match the target complex reflection zeros. We demonstrate that complex frequency driving can achieve near-unity selected energy storage efficiency (100%) in a si…

18F announces a legal challenge to the dismantlement of the department by DOGE
https://18f.org/announcement-of-legal-challenge/

Lessons Learned from the URGENT 2024 Speech Enhancement Challenge
Wangyou Zhang, Kohei Saijo, Samuele Cornell, Robin Scheibler, Chenda Li, Zhaoheng Ni, Anurag Kumar, Marvin Sach, Wei Wang, Yihui Fu, Shinji Watanabe, Tim Fingscheidt, Yanmin Qian
https://arxiv.org/abs/2506.01611

Lessons Learned from the URGENT 2024 Speech Enhancement Challenge
The URGENT 2024 Challenge aims to foster speech enhancement (SE) techniques with great universality, robustness, and generalizability, featuring a broader task definition, large-scale multi-domain data, and comprehensive evaluation metrics. Nourished by the challenge outcomes, this paper presents an in-depth analysis of two key, yet understudied, issues in SE system development: data cleaning and evaluation metrics. We highlight several overlooked problems in traditional SE pipelines: (1) misma…

This https://arxiv.org/abs/2505.24492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Object Centric Concept Bottlenecks
Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandable concepts from a global encoding (e.g., image encoding) and then applying a linear classifier on the resulting concept activations, enabling transparent decision-making. However, their reliance on holistic image encodings limits their expressiveness in object-centric real-world settings and thus hinders their abilit…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Marcos V. Conde, Radu Timofte, Zihao Lu, Xiangyu Kongand Xiaoxia Xingand Fan Wangand Suejin Hanand MinKyu Parkand Tianyu Zhangand Xin Luoand Yeda Chenand Dong Liuand Li Pangand Yuhang Yangand Hongzhong Wangand Xiangyong Caoand Ruixuan Jiangand Senyan Xuand Siyuan Jiangand Xueyang Fuand Zheng-Jun Zhaand Tianyu Haoand Yuhong Heand Ruoqi Liand Yueqi Yangand Xiang Yuand Guanlan Hongand Minmin Yiand Yuanjia Chenand Liwen Zha…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the…

This https://arxiv.org/abs/2506.00433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free
High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present Latent Wavelet Diffusion (LWD), a lightweight framework that enables any latent diffusion model to scale to ultra-high-resolution image generation (2K to 4K) for free. LWD introduces three key components: (1) a scale-consistent variational autoencoder objective that enhances the spectral fidelity of la…

This https://arxiv.org/abs/2503.02077 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality ($\text{M}^3\text{HF}$), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process. By involving humans with diverse expertise levels to provide iterat…

Extremely large oblate deformation of the first excited state in $^{12}$C: a new challenge to modern nuclear theory
C. Ngwetsheni, J. N. Orce, P. Navr\'atil, P. E. Garrett, T. Faestermann, A. Bergmaier, M. Frosini, V. Bildstein, B. A. Brown, C. Burbadge, T. Duguet, K. Hady\'nska-Kl\c{e}k, M. Mahgoub, C. V. Mehl, A. Pastore, A. Radich, S. Triambak

Extremely large oblate deformation of the first excited state in $^{12}$C: a new challenge to modern nuclear theory
A Coulomb-excitation study of the high-lying first excited state at 4.439 MeV in the nucleus $^{12}$C has been carried out using the $^{208}$Pb($^{12}$C,$^{12}$C$^*$)$^{208}$Pb$^*$ reaction at 56 MeV and the {\sc Q3D} magnetic spectrograph at the Maier-Leibnitz Laboratorium in Munich. High-statistics achieved with an average beam intensity of approximately 10$^{11}$ ions/s together with state-of-the-art {\it ab initio} calculations of the nuclear dipole polarizability permitted the accurate det…

Steelers' long wait for Aaron Rodgers is over, but offense far from finished product https://www.nfl.com/news/steelers-long-wait-for-aaron-rodgers-is-over-but-offense-far-from-finished-product

Electron and gas temperature-driven chemistry during microdischarges formed in water vapour bubbles
Florens Grimm, Jan-Luca Gembus, Jana Sch\"one, Peter Awakowicz, Lars Sch\"ucke, Andrew R. Gibson
https://arxiv.org/abs/2506.05124

Electron and gas temperature-driven chemistry during microdischarges formed in water vapour bubbles
Microdischarges formed in bubbles immersed in liquids are of interest for materials synthesis and chemical conversion applications in the frame of plasma-driven electrochemistry. A key challenge associated with controlling such processes is the limited understanding of the gas-phase chemical kinetics in these microdischarges. Due to their large electron densities, and high gas temperatures, both electron and gas temperature driven chemistry are likely to be important. Here, a 0-D modelling appr…

Reduction of Outflow Boundary Influence on Aerodynamic Performance using Neural Networks
Mario Christopher Bedrunka, Dirk Reith, Holger Foysi, {\L}ukasz {\L}aniewski-Wo{\l}{\l}k, Travis Mitchell
https://arxiv.org/abs/2506.05293

Reduction of Outflow Boundary Influence on Aerodynamic Performance using Neural Networks
The accurate treatment of outflow boundary conditions remains a critical challenge in computational fluid dynamics when predicting aerodynamic forces and/or acoustic emissions. This is particularly evident when employing the lattice Boltzmann method (LBM) as the numerical solution technique, which often suffers from inaccuracies induced by artificial reflections from outflow boundaries. This paper investigates the use of neural networks (NN) to mitigate these adverse boundary effects and enable…

At the edge of Donsker's Theorem: Asymptotics of multiscale scan statistics
Johann K\"ohne, Fabian Mies
https://arxiv.org/abs/2506.05112 https://

At the edge of Donsker's Theorem: Asymptotics of multiscale scan statistics
For nonparametric inference about a function, multiscale testing procedures resolve the need for bandwidth selection and achieve asymptotically optimal detection performance against a broad range of alternatives. However, critical values strongly depend on the noise distribution, and we argue that existing methods are either statistically infeasible, or asymptotically sub-optimal. To address this methodological challenge, we show how to develop a feasible multiscale test via weak convergence ar…

Judge tosses Democratic Party challenge to Trump order's impact on FEC (Politico)
https://www.politico.com/news/2025/06/03/judge-tosses-democratic-party-challenge-to-trump-orders-impact-on-fec-00384577
http://www.memeorandum.com/250604/p1#a250604p1

This https://arxiv.org/abs/2505.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize ho…

This https://arxiv.org/abs/2506.03896 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FLIP: Flowability-Informed Powder Weighing
Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantifie…

Logical Inferentialism & Attacks on Classical Logic
Khashayar Irani
https://arxiv.org/abs/2506.04295 https://arxiv.org/pdf/2506.0…

Logical Inferentialism & Attacks on Classical Logic
This paper undertakes a foundational inquiry into logical inferentialism with particular emphasis on the normative standards it establishes and the implications these pose for classical logic. The central question addressed herein is: 'What is Logical Inferentialism & How do its Standards challenge Classical Logic?' In response, the study begins with a survey of the three principal proof systems that is, David Hilbert's axiomatic systems and Gerhard Gentzen's natural deduction and his sequent c…

Hey everyone!
What do you think about Greta Tintin Eleonora Ernman Thunberg heading to Gaza with the Freedom Flotilla to deliver humanitarian aid and challenge the blockade?
I’m honestly mixed on it, and I would love to hear your thoughts.
#GretaThunberg #GretaTintinEleonoraErnmanThunberg

This https://arxiv.org/abs/2505.22769 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking
Mobile gaze tracking faces a fundamental challenge: maintaining accuracy as users naturally change their postures and device orientations. Traditional calibration approaches, like one-off, fail to adapt to these dynamic conditions, leading to degraded performance over time. We present MAC-Gaze, a Motion-Aware continual Calibration approach that leverages smartphone Inertial measurement unit (IMU) sensors and continual learning techniques to automatically detect changes in user motion states and…

“This considers how two modern cameras handle a difficult color challenge, illustrated by photos of a perfect rose and a piano.” https://www.tbray.org/ongoing/When/202x/2025/05/31/Colors

Perfectly Different Colors

This https://arxiv.org/abs/2502.11475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly complex and long-reasoning nature of the task. Even state-of-the-art models often fail in code generation due to small errors, which can drastically affect the overall functionality of code. Our study identifies that current models tend to produce errors concentrated at specific error-prone points, which significantl…

This https://arxiv.org/abs/2406.18370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Learning pure quantum states (almost) without regret
We initiate the study of sample-optimal quantum state tomography with minimal disturbance to the samples. Can we efficiently learn a precise description of a quantum state through sequential measurements of samples while at the same time making sure that the post-measurement state of the samples is only minimally perturbed? Defining regret as the cumulative disturbance of all samples, the challenge is to find a balance between the most informative sequence of measurements on the one hand and me…

This https://arxiv.org/abs/2503.05740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults
Cognitive health in older adults presents a growing challenge. Although conversational interventions show feasibility in improving cognitive wellness, human caregiver resources remain overloaded. AI-based chatbots have shown promise, yet existing work is often limited to implicit strategies or heavily depends on training and label resources. In response, we propose a strategy-guided AI chatbot named ChatWise that follows a dual-level conversation reasoning framework. It integrates macro-level s…

This https://arxiv.org/abs/2506.03074 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

GL-LowPopArt: A Nearly Instance-Wise Minimax Estimator for Generalized Low-Rank Trace Regression
We present `GL-LowPopArt`, a novel Catoni-style estimator for generalized low-rank trace regression. Building on `LowPopArt` (Jang et al., 2024), it employs a two-stage approach: nuclear norm regularization followed by matrix Catoni estimation. We establish state-of-the-art estimation error bounds, surpassing existing guarantees (Fan et al., 2019; Kang et al., 2022), and reveal a novel experimental design objective, $\mathrm{GL}(π)$. The key technical challenge is controlling bias from the non…

This https://arxiv.org/abs/2409.05546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Generative Recommender with End-to-End Learnable Item Tokenization
Generative recommendation systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potential, a major challenge is the effective construction of item identifiers that align well with recommender systems. Current approaches often treat item tokenization and generative recommendation training as separate processes, which can lead to suboptimal performance. To overcome this issue, we introduce ETEGRec, a nov…

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Philipp H\"artel, Michael von Bonin
https://arxiv.org/abs/2506.04843

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Electric vehicle (EV) fleets are expected to become an increasingly important source of flexibility for power system operations. However, accurately capturing the flexibility potential of numerous and heterogeneous EVs remains a significant challenge. We propose a bilevel optimization formulation to enhance flexibility aggregations of electric vehicle fleets. The outer level minimizes scheduling deviations between the aggregated and reference EV units, while the inner level maximizes the aggreg…

This https://arxiv.org/abs/2406.10554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Causal Inference with Outcomes Truncated by Death and Missing Not at Random
In clinical trials, principal stratification analysis is commonly employed to address the issue of truncation by death, where a subject dies before the outcome can be measured. However, in practice, many survivor outcomes may remain uncollected or be missing not at random, posing a challenge to standard principal stratification analyses. In this paper, we explore the identification, estimation, and bounds of the average treatment effect within a subpopulation of individuals who would potentiall…

Towards provable probabilistic safety for scalable embodied AI systems
Linxuan He, Qing-Shan Jia, Ang Li, Hongyan Sang, Ling Wang, Jiwen Lu, Tao Zhang, Jie Zhou, Yi Zhang, Yisen Wang, Peng Wei, Zhongyuan Wang, Henry X. Liu, Shuo Feng
https://arxiv.org/abs/2506.05171

Towards provable probabilistic safety for scalable embodied AI systems
Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety in complex operating environments remains a major challenge, which severely hinders their large-scale deployment in safety-critical domains, such as autonomous vehicles, medical devices, and robotics. While achieving provable deterministic safety--verifying system safety across all possible scenarios--remains theoretic…

This https://arxiv.org/abs/2410.16428 has been replaced.
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Neural Scoring: A Refreshed End-to-End Approach for Speaker Recognition in Complex Conditions
Modern speaker verification systems primarily rely on speaker embeddings and cosine similarity. While effective, these methods struggle with multi-talker speech due to the unidentifiability of embedding vectors. We propose Neural Scoring (NS), a novel end-to-end framework that directly estimates verification posterior probabilities without relying on test-side embeddings, making it more powerful and robust to complex conditions, e.g., with multiple talkers. To address the challenge of training …

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SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logica…

A Fast, Accurate and Oscillation-free Spectral Collocation Solver for High-dimensional Transport Problems
Nicola Cavallini, Gianmarco Manzini, Daniele Funaro, Andrea Favalli
https://arxiv.org/abs/2506.04732

A Fast, Accurate and Oscillation-free Spectral Collocation Solver for High-dimensional Transport Problems
Transport phenomena-describing the movement of particles, energy, or other physical quantities-are fundamental in various scientific disciplines, including nuclear physics, plasma physics, astrophysics, engineering, and the natural sciences. However, solving the associated seven-dimensional transport equations poses a significant computational challenge due to the curse of dimensionality. We introduce the Tensor Train Superconsistent Spectral (T${^2}$S${^2}$) solver to address this challeng…

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Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, traversing rough terrain, fine-grained control during locomotion remains a significant challenge. In particular, stabilizing a filled end-effector (EE) while walking is far from solved, due to a fundamental mismatch in task dynamics: locomotion demands slow-timescale, robust control, whereas EE stabilization requires rap…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Mohammed Almutairi
https://arxiv.org/abs/2506.05265 http…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Effective teamwork is essential across diverse domains. During the team formation stage, a key challenge is forming teams that effectively balance user preferences with task objectives to enhance overall team satisfaction. In the team performing stage, maintaining cohesion and engagement is critical for sustaining high team performance. However, existing computational tools and algorithms for team optimization often rely on static data inputs, narrow algorithmic objectives, or solutions tailore…

This https://arxiv.org/abs/2505.12910 has been replaced.
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SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs
Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. …

Towards the target and not beyond: 2d vs 3d visual aids in mr-based neurosurgical simulation
Pasquale Cascarano, Andrea Loretti, Matteo Martinoni, Luca Zanuttini, Alessio Di Pasquale, Gustavo Marfia
https://arxiv.org/abs/2506.05164

Towards the target and not beyond: 2d vs 3d visual aids in mr-based neurosurgical simulation
Neurosurgery increasingly uses Mixed Reality (MR) technologies for intraoperative assistance. The greatest challenge in this area is mentally reconstructing complex 3D anatomical structures from 2D slices with millimetric precision, which is required in procedures like External Ventricular Drain (EVD) placement. MR technologies have shown great potential in improving surgical performance, however, their limited availability in clinical settings underscores the need for training systems that fos…

This https://arxiv.org/abs/2506.00095 has been replaced.
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ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising…

Modelling the evolution of flow-induced anisotropy of concentrated suspensions
Pappu Acharya, Romain Mari
https://arxiv.org/abs/2506.05222 https://

Modelling the evolution of flow-induced anisotropy of concentrated suspensions
Suspensions, which exhibit complex behaviors such as shear thickening, thinning, and jamming, are prevalent in nature and industry. However, predicting the mechanical properties of concentrated suspensions, in both steady state and the transient regime, remains a significant challenge, impacting product quality and process efficiency. In this study, we focus on developing a robust theoretical framework to explain how flow history governs the anisotropy of mechanical responses in suspensions of …

Reconfigurable Ultrafast Thermal Metamaterial Pixel Arrays by Dual-Gate Graphene Transistors
Yibai Zhong, Xiu Liu, Zexiao Wang, Tianyi Huang, Jingyi Zou, Sen Lin, Xiao Luo, Zhuo Li, Rui Cheng, Xu Zhang, Sheng Shen
https://arxiv.org/abs/2506.04372

Reconfigurable Ultrafast Thermal Metamaterial Pixel Arrays by Dual-Gate Graphene Transistors
Thermal signatures represent ubiquitous infrared appearances of objects, carrying their unique spectral fingerprints. Despite extensive efforts to decipher and manipulate thermal-infrared signals, the ability to fully control them across spatial, temporal and spectral domains remains a significant challenge due to the slow speed, diffuse and broadband emitting nature of thermal emission in most materials. Here, we demonstrate a reconfigurable ultrafast thermal metamaterial pixel array that inte…

This https://arxiv.org/abs/2505.21179 has been replaced.
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Normalized Attention Guidance: Universal Negative Guidance for Diffusion Model
Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based…

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang, Yanfeng Wang, Weidi Xie
https://arxiv.org/abs/2506.03238

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing …

Tensor network method for real-space topology in quasicrystal Chern mosaics
Tiago V. C. Ant\~ao, Yitao Sun, Adolfo O. Fumega, Jose L. Lado
https://arxiv.org/abs/2506.05230

Tensor network method for real-space topology in quasicrystal Chern mosaics
Computing topological invariants in two-dimensional quasicrystals and super-moire matter is an exceptional open challenge, due to the absence of translational symmetry and the colossal number of sites inherent to these systems. Here, we establish a method to compute local topological invariants of exceptionally large systems using tensor networks, enabling the computation of invariants for Hamiltonians with hundreds of millions of sites, several orders of magnitude above the capabilities of con…

This https://arxiv.org/abs/2506.00377 has been replaced.
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A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources,…

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Jiang Hu, Jiahui Xie, Yangchun Zhang, Wang Zhou
https://arxiv.org/abs/2506.05116

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Factor models are essential tools for analyzing high-dimensional data, particularly in economics and finance. However, standard methods for determining the number of factors often overestimate the true number when data exhibit heavy-tailed randomness, misinterpreting noise-induced outliers as genuine factors. This paper addresses this challenge within the framework of Elliptical Factor Models (EFM), which accommodate both heavy tails and potential non-linear dependencies common in real-world da…

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Weiyuan Xu, Juntao Luo, Tao Huang, Kaixin Sui, Jie Geng, Qijun Ma, Isami Akasaka, Xiaoxue Shi, Jing Tang, Peng Cai
https://arxiv.org/abs/2506.03691

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Continuous Integration and Continuous Deployment (CI/CD) pipelines are pivotal to modern software engineering, yet diagnosing and resolving their failures remains a complex and labor-intensive challenge. In this paper, we present LogSage, the first end-to-end LLM-powered framework that performs root cause analysis and solution generation from failed CI/CD pipeline logs. During the root cause analysis stage, LogSage employs a specialized log preprocessing pipeline tailored for LLMs, which extrac…

Stabilization of Linear Switched Systems with Long Input Delay via Average or Averaging Predictor Feedbacks
Andreas Katsanikakis, Nikolaos Bekiaris-Liberis
https://arxiv.org/abs/2506.03908

Stabilization of Linear Switched Systems with Long Input Delay via Average or Averaging Predictor Feedbacks
We develop delay-compensating feedback laws for linear switched systems with time-dependent switching. Because the future values of the switching signal, which are needed for constructing an exact predictor-feedback law, may be unavailable at current time, the key design challenge is how to construct a proper predictor state. We resolve this challenge constructing two alternative, average predictor-based feedback laws. The first is viewed as a predictor-feedback law for a particular average sys…

Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
Kacper Sokol, James Fackler, Julia E Vogt
https://arxiv.org/abs/2506.05030

Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
Artificial intelligence promises to revolutionise medicine, yet its impact remains limited because of the pervasive translational gap. We posit that the prevailing technology-centric approaches underpin this challenge, rendering such systems fundamentally incompatible with clinical practice, specifically diagnostic reasoning and decision making. Instead, we propose a novel sociotechnical conceptualisation of data-driven support tools designed to complement doctors' cognitive and epistemic activ…

This https://arxiv.org/abs/2505.23212 has been replaced.
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Interspeech 2025 URGENT Speech Enhancement Challenge
There has been a growing effort to develop universal speech enhancement (SE) to handle inputs with various speech distortions and recording conditions. The URGENT Challenge series aims to foster such universal SE by embracing a broad range of distortion types, increasing data diversity, and incorporating extensive evaluation metrics. This work introduces the Interspeech 2025 URGENT Challenge, the second edition of the series, to explore several aspects that have received limited attention so fa…

This https://arxiv.org/abs/2506.02774 has been replaced.
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Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone
3D Gaussian Splatting (3DGS) is an emerging technique for photorealistic 3D scene rendering. However, rendering city-scale 3DGS scenes on mobile devices, e.g., your smartphones, remains a significant challenge due to the limited resources on mobile devices. A natural solution is to offload computation to the cloud; however, naively streaming rendered frames from the cloud to the client introduces high latency and requires bandwidth far beyond the capacity of current wireless networks. In this…

Non-invasive measurement of local stress inside soft materials with programmed shear waves
Zhaoyi Zhang, Guo-Yang Li, Yuxuan Jiang, Yang Zheng, Artur L. Gower, Michel Destrade, Yanping Cao
https://arxiv.org/abs/2506.03816

Non-invasive measurement of local stress inside soft materials with programmed shear waves
Mechanical stresses in soft materials across different length scales play a fundamental role in understanding the function of biological systems and in the use of artificial materials for engineering soft machines and biomedical devices. Yet it remains a great challenge to probe local mechanical stresses in situ in a non-invasive, non-destructive manner, in particular when the mechanical properties are unknown. To address this challenge, we propose an acoustoelastic imaging-based method to infe…

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
Nabarun Goswami, Tatsuya Harada
https://arxiv.org/abs/2506.00809

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
We propose a multi-stage framework for universal speech enhancement, designed for the Interspeech 2025 URGENT Challenge. Our system first employs a Sparse Compression Network to robustly separate sources and extract an initial clean speech estimate from noisy inputs. This is followed by an efficient generative model that refines speech quality by leveraging self-supervised features and optimizing a masked language modeling objective on acoustic tokens derived from a neural audio codec. In the f…

This https://arxiv.org/abs/2505.11282 has been replaced.
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MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
Mobile robots are reaching unprecedented speeds, with platforms like Unitree B2, and Fraunhofer O3dyn achieving maximum speeds between 5 and 10 m/s. However, effectively utilizing such speeds remains a challenge due to the limitations of RGB cameras, which suffer from motion blur and fail to provide real-time responsiveness. Event cameras, with their asynchronous operation, and low-latency sensing, offer a promising alternative for high-speed robotic perception. In this work, we introduce MTeve…

This https://arxiv.org/abs/2305.15549 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data
Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameters, which govern moisture dynamics but are rarely known precisely and must therefore be inferred from observational data. In large-scale agricultural fields, estimating the complete set of these parameters is often impractical due to the sparse and noisy nature of available measurements. To address this challenge, this…

This https://arxiv.org/abs/2502.11844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

BaxBench: Can LLMs Generate Correct and Secure Backends?
Automatic program generation has long been a fundamental challenge in computer science. Recent benchmarks have shown that large language models (LLMs) can effectively generate code at the function level, make code edits, and solve algorithmic coding tasks. However, to achieve full automation, LLMs should be able to generate production-quality, self-contained application modules. To evaluate the capabilities of LLMs in solving this challenge, we introduce BaxBench, a novel evaluation benchmark c…

Iowa Democrat Will Challenge Ernst After 'We All Are Going to Die' Remarks (Reid J. Epstein/New York Times)
https://www.nytimes.com/2025/06/02/us/politics/jd-scholten-joni-ernst-iowa-senate-race.html
http://www.memeorandum.com/250603/p32#a250603p32

Phi-Omni-ST: A multimodal language model for direct speech-to-speech translation
Yuxuan Hu, Haibin Wu, Ruchao Fan, Xiaofei Wang, Heng Lu, Yao Qian, Jinyu Li
https://arxiv.org/abs/2506.04392

Phi-Omni-ST: A multimodal language model for direct speech-to-speech translation
Speech-aware language models (LMs) have demonstrated capabilities in understanding spoken language while generating text-based responses. However, enabling them to produce speech output efficiently and effectively remains a challenge. In this paper, we present Phi-Omni-ST, a multimodal LM for direct speech-to-speech translation (ST), built on the open-source Phi-4 MM model. Phi-Omni-ST extends its predecessor by generating translated speech using an audio transformer head that predicts audio to…

This https://arxiv.org/abs/2505.19746 has been replaced.
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Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa, Pekka Marttinen, Hang Zhang, Min Liu, Yichao Zhou, Zuopeng Tan, Zhuoyuan Wang, Yi Wang, Hongchao Zhou, Shunbo Hu, Yi Zhang, Qian Tao, Lukas F\"orner, Thomas Wendler, Bailiang Jian, Benedikt Wiestler, Tim Hable, Jin Kim, Dan Ruan, Frederic Madesta, Thilo Sentker, Wiebke Heyer, Lianrui Zuo, Yuwei Dai, Jing Wu, Jerry L. Prince, Harrison Bai, Yong Du, Yihao Liu, Alessa He…

Beyond the LUMIR challenge: The pathway to foundational registration models
Medical image challenges have played a transformative role in advancing the field, catalyzing algorithmic innovation and establishing new performance standards across diverse clinical applications. Image registration, a foundational task in neuroimaging pipelines, has similarly benefited from the Learn2Reg initiative. Building on this foundation, we introduce the Large-scale Unsupervised Brain MRI Image Registration (LUMIR) challenge, a next-generation benchmark designed to assess and advance u…

FLIP: Flowability-Informed Powder Weighing
Nikola Radulov (Department of Computer Science University of Liverpool UK), Alex Wright (Department of Computer Science University of Liverpool UK), Thomas little (Department of Computer Science University of Liverpool UK), Andrew I. Cooper (Department of Chemistry university of Liverpool UK), Gabriella Pizzuto (Department of Computer Science University of Liverpool UK, Department of Chemistry university of Liverpool UK)

FLIP: Flowability-Informed Powder Weighing
Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantifie…

This https://arxiv.org/abs/2308.16061 has been replaced.
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Conti Inc.: Understanding the Internal Discussions of a large Ransomware-as-a-Service Operator with Machine Learning
Ransomware-as-a-service (RaaS) is increasing the scale and complexity of ransomware attacks. Understanding the internal operations behind RaaS has been a challenge due to the illegality of such activities. The recent chat leak of the Conti RaaS operator, one of the most infamous ransomware operators on the international scene, offers a key opportunity to better understand the inner workings of such organizations. This paper analyzes the main topic discussions in the Conti chat leak using machin…

This https://arxiv.org/abs/2506.00095 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising…

This https://arxiv.org/abs/2506.00541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras
Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchr…

This https://arxiv.org/abs/2505.18780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion
Humanoid locomotion faces a critical scalability challenge: traditional reinforcement learning (RL) methods require task-specific rewards and struggle to leverage growing datasets, even as more training terrains are introduced. We propose DreamPolicy, a unified framework that enables a single policy to master diverse terrains and generalize zero-shot to unseen scenarios by systematically integrating offline data and diffusion-driven motion synthesis. At its core, DreamPolicy introduces Humanoid…

This https://arxiv.org/abs/2505.23503 has been replaced.
link: https://scholar.google.com/scholar?q=a

Can Large Language Models Challenge CNNS in Medical Image Analysis?
This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the strengths of convolutional neural networks (CNNs) and different large language models (LLMs). This in-depth comparative analysis highlights key differences in diagnostic performance, execution efficiency, and environmental impacts. Model evaluation was based on accuracy, F1-score, average execution time, average energ…

This https://arxiv.org/abs/2504.20369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Perception-aware Sampling for Scatterplot Visualizations
Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeti…

Antonio Delgado, Hochul's No. 2, Will Challenge Her in Governor's Race (Jeffery C. Mays/New York Times)
https://www.nytimes.com/2025/06/02/nyregion/antonio-delgado-governor-hochul.html
http://www.memeorandum.com/250602/p120#a250602p120

This https://arxiv.org/abs/2505.13556 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures
Accurate and timely alerts for drivers or automated systems to unfolding collisions remains a challenge in road safety, particularly in highly interactive urban traffic. Existing approaches require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are useful only in the scenarios they are designed for. To address these limits, this study introduces the generalised surrogate safety measure (GSSM), a new approach that learns exclusively from naturalis…

Supreme Court will consider reviving Republican challenge to Illinois law on mail ballots (Mark Sherman/Associated Press)
https://apnews.com/article/supreme-court-trump-mail-ballots-b2e44031402ebbe18ed1291c7a92f46d
http://www.memeorandum.com/250602/p110#a250602p110

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Junting Chen, Haotian Liang, Lingxiao Du, Weiyun Wang, Mengkang Hu, Yao Mu, Wenhai Wang, Jifeng Dai, Ping Luo, Wenqi Shao, Lin Shao
https://arxiv.org/abs/2506.04217

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM) task remains a challenge due to the need for generalization to open-ended instructions and environments, as well as the systematic complexity to integrate high-level decision making with low-level robot control based on both global scene understanding and current agent state. To address this complexity, we propose a …

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

This https://arxiv.org/abs/2501.14147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction with data streamed from multiple robots and devices remains a challenge. To that end, we propose HAMMER, a server-based collaborative Gaussian Splatting method that leverages widely available ROS communication infrastructure to generate 3D, metric-semantic maps from asynchronous robot data-streams with no prior knowl…

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
Zuhao Teng, Qian Dong, Ze Zhang, Shuangyao Huang, Wenzhang Zhang, Jingchen Wang, Ji Li, Xi Chen
https://arxiv.org/abs/2506.03663

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed …