Tootfinder

Every single talk that I saw from @… is just fantastic and highly recommended. 🤩
https://rosa.codes/machines2025/

Adrienne Jones-McAllister told News 12 Long Island that she was undergoing an MRI on her knee
when she asked the technician to get her husband, Keith McAllister, to help her get off the table.
She said she called out to him.
The man, 61, entered the MRI room while a scan was underway
The machine’s strong magnetic force drew him in by the metallic chain around his neck, according to a release from the Nassau County Police Department.
He died Thursday afternoon

Man dies after heavy weight-training chain around neck pulls him into MRI machine
A man whose large metallic weight-training chain around his neck pulled him into an MRI machine in New York after he wandered into the room has died. Nassau County police on Long Island say the 61-year-old man died Thursday afternoon, a day after he entered a room while a Magnetic Resonance Imaging scan was underway at Nassau Open MRI. The police department said in a statement that the strong magnetic force drew him in by his metallic chain. Authorities did not release his name. A person who an…

Does using machine learning solve our problem of p-hacking and HARKing or do we have the same problems as with statistical tests and models?
https://digiresacademy.kit.com/posts/is-machine-learning-and-ai-solving-the-problem-of-p-hacking

Walter and Paul presenting a DIY project to convert an old sewing machine into a modern computerized embroidery machine. First time in TV today 😄
#turtlestitch10 #embroidery #arts

Gender-Neutral Machine Translation Strategies in Practice
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
https://arxiv.org/abs/2506.15676 https://

Gender-Neutral Machine Translation Strategies in Practice
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs naturally in notional gender languages such as English, maintaining that gender neutrality in grammatical gender languages is a challenge. Here we assess the sensitivity of 21 MT systems to the need for gender neutrality in response to gender ambiguity in three translation directions of varying difficulty. The specific gen…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Special Interest:
🎵 Concerning Peace (Machine Girl Remix)
#SpecialInterest
https://specialinterestno.bandcamp.com/track/concerning-peace-machine-girl-remix
https://open.spotify.com/track/3lmMCXxpjCEehCRPMwrSUx

🇺🇦 Auf radioeins läuft...
When Saints Go Machine:
🎵 Kelly
#NowPlaying #WhenSaintsGoMachine
https://when-saints-go-machine.bandcamp.com/album/kelly
https://open.spotify.com/track/4C39zJ6Fuujo6d8ZTiBztT

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdos...
Mullen, Harris, Rock, Thompson, Slavova, Talbert, Bumgardner

Causality in the human niche: lessons for machine learning
Richard D. Lange, Konrad P. Kording
https://arxiv.org/abs/2506.13803 https://

Causality in the human niche: lessons for machine learning
Humans interpret the world around them in terms of cause and effect and communicate their understanding of the world to each other in causal terms. These causal aspects of human cognition are thought to underlie humans' ability to generalize and learn efficiently in new domains, an area where current machine learning systems are weak. Building human-like causal competency into machine learning systems may facilitate the construction of effective and interpretable AI. Indeed, the machine learnin…

Instead of using butter with your garlic bread, you should switch to Linux. You can set up a virtual machine to try out various distros to see which one works best for your needs.

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Zhenyao Fang, Ting-Wei Hsu, Qimin Yan
https://arxiv.org/abs/2506.15652

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. I…

Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond
Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
https://arxiv.org/abs/2506.14950

Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond
Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest that satisfies some conditional moment equalities. Specifically, many techniques for causal inference, such as instrumental variable (IV) regression and proximal causal learning (PCL), are CMR problems. Most CMR estimators use a two-stage approach, where the first-stage estimation is directly plugged into the second st…

Apple seems to be working on adding CUDA support to open-source ML framework MLX, which may mean that code developed using MLX would work with CUDA (Malcolm Owen/AppleInsider)
https://appleinsider.com/articles/25/0

Apple Silicon machine learning code may become more easily portable to Nvidia hardware
A project is trying to cut the cost of making machine learning applications for Nvidia hardware, by developing on an Apple Silicon Mac and exporting it to CUDA.

PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning
Xiangman Li, Xiaodong Wu, Jianbing Ni, Mohamed Mahmoud, Maazen Alsabaan
https://arxiv.org/abs/2506.15112

PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning
Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and filter malicious models, aiming to prevent a limited number of attackers from corrupting the global model. However, restoring an already compromised global model remains a challenge. A direct approach is to remove malicious clients and retrain the model using only the benign clients. Yet, retraining is time-consuming, c…

An EPTAS for multiprocessor scheduling with rejection under a machine cost constraint
Mingyang Gong, Brendan Mumey
https://arxiv.org/abs/2507.12635 https:/…

An EPTAS for multiprocessor scheduling with rejection under a machine cost constraint
We study the multiprocessor scheduling with rejection problem under a machine cost constraint. In this problem, each job is either rejected with a rejection penalty or; accepted and scheduled on one of the machines for processing. The machine cost is proportional to the total processing time of the jobs scheduled on it. The problem aims to minimize the makespan of accepted jobs plus the total rejection penalty of rejected jobs while the total machine cost does not exceed a given upper bound. We…

All tools create a path of least resistance. When it comes to AI chatbots, that path is to trust the AI's outputs.
Unfortunately, all LLMs hallucinate. And as users get used to relying on the machine, their ability and willingness to spot these errors deteriorates.
Blaming the user for this is irresponsible. The problem is caused by the way these tools are designed - so it's up to us, as designers, to fix it.

Getting the Windows dev box set up for scopehal testing.
The most important step...

“Why do people have such dramatically different experiences using AI?”
https://shkspr.mobi/blog/2025/06/why-do-people-have-such-dramatically-different-experiences-using-ai/
In the example, you have Google engine…

Why do people have such dramatically different experiences using AI?
For some people, it seems, AI is an amazing machine which - while fallible - represents an incredible leap forward in productivity. For other people, it seems, AI is wrong more often than right and - although occasionally useful - requires constant supervision. Who is right? I recently pointed out a few common problems with LLMs. I was discussing this with someone relatively senior who works…

Understanding multi-fidelity training of machine-learned force-fields
John L. A. Gardner, Hannes Schulz, Jean Helie, Lixin Sun, Gregor N. C. Simm
https://arxiv.org/abs/2506.14963 …

Understanding multi-fidelity training of machine-learned force-fields
Effectively leveraging data from multiple quantum-chemical methods is essential for building machine-learned force fields (MLFFs) that are applicable to a wide range of chemical systems. This study systematically investigates two multi-fidelity training strategies, pre-training/fine-tuning and multi-headed training, to elucidate the mechanisms underpinning their success. We identify key factors driving the efficacy of pre-training followed by fine-tuning, but find that internal representations …

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Zhuolin Du, Yisheng Song
https://arxiv.org/abs/2507.13012

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving class…

Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo
https://arxiv.org/abs/2506.15235 …

Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. Howev…

The Enigma machine's really just phoning it in now.

Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Anastasia Kuznetsova, Inseon Jang, Wootaek Lim, Minje Kim
https://arxiv.org/abs/2507.12701

Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Neural audio codecs, leveraging quantization algorithms, have significantly impacted various speech/audio tasks. While high-fidelity reconstruction is paramount for human perception, audio coding for machines (ACoM) prioritizes efficient compression and downstream task performance, disregarding perceptual nuances. This work introduces an efficient ACoM method that can compress and quantize any chosen intermediate feature representation of an already trained speech/audio downstream model. Our ap…

@… Ah, interesting! The only non-diskless machine we had at the time was a 735/125, which was obviously much faster in all respects, so…

@… Ah, interesting! The only non-diskless machine we had at the time was a 735/125, which was obviously much faster in all respects, so…

An introduction to Neural Networks for Physicists
G. Caf\'e de Miranda, Gubio G. de Lima, Tiago de S. Farias
https://arxiv.org/abs/2505.13042 https://

An introduction to Neural Networks for Physicists
Machine learning techniques have emerged as powerful tools to tackle various challenges. The integration of machine learning methods with Physics has led to innovative approaches in understanding, controlling, and simulating physical phenomena. This article aims to provide a practical introduction to neural network and their basic concepts. It presents some perspectives on recent advances at the intersection of machine learning models with physical systems. We introduce practical material to gu…

"Ardisson, c'était une machine Š broyer les femmes, les pauvres, les minorités, parfois les trois Š la fois. Mais avant tout, et tout le temps, les femmes."
https://www.arretsurimages.net/chroniques/sur-le-gril/ardisson-der…

What's going on with this #ICLR paper?
The metareview says that the authors provided a sound rebuttal and update to the paper, but neither are available (rebuttals are shown on other papers).
https://openreview.…

GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range...
Learning on graphs is one of the most active research topics in machine learning (ML). Among the key challenges in this field, effectively learning long-range dependencies in graphs has been...

Machine learning based prediction of dynamical clustering in granular gases
Sai Preetham Sata, Ralf Stannarius, Benjamin Noack, Dmitry Puzyrev
https://arxiv.org/abs/2506.15657

Machine learning based prediction of dynamical clustering in granular gases
When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called clusters. This dynamical clustering, or gas-cluster transition, is caused by a complex interplay and balance between the energy influx and dissipation in particle collisions. Particle number density, container geometry, and excitation strength influence this tran…

Building State Machine Replication Using Practical Network Synchrony
Yiliang Wan, Nitin Shivaraman, Akshaye Shenoi, Xiang Liu, Tao Luo, Jialin Li
https://arxiv.org/abs/2507.12792 …

Building State Machine Replication Using Practical Network Synchrony
Distributed systems, such as state machine replication, are critical infrastructures for modern applications. Practical distributed protocols make minimum assumptions about the underlying network: They typically assume a partially synchronous or fully asynchronous network model. In this work, we argue that modern data center systems can be designed to provide strong synchrony properties in the common case, where servers move in synchronous lock-step rounds. We prove this hypothesis by engineeri…

Multiple machine-learning as a powerful tool for the star clusters analysis
Denilso Camargo
https://arxiv.org/abs/2506.13951 https://…

Multiple machine-learning as a powerful tool for the star clusters analysis
This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness in the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of the old binary cluster candidate - NGC 1605a and NGC 1605b - found by Camargo (2021) (hereafter C21). The binary cluster candidate is analyzed by employing a set of well established machine learning algorithms applied to the Gaia-EDR3 data. Membership probabilities and open clusters (OCs…

Alt National Park Servic "They’re cranking up the propaganda machine again: claiming Brad Lander “unlawfully interfered” and that ICE is just “enforcing Trump’s policy.” Reality: Lander stood up for civil rights. ICE’s conduct reflects a deeply troubling use of federal power." — Bluesky
https://

High computational density nanophotonic media for machine learning inference
Zhenyu Zhao, Yichen Pan, Jinlong Xiang, Yujia Zhang, An He, Yaotian Zhao, Youlve Chen, Yu He, Xinyuan Fang, Yikai Su, Min Gu, Xuhan Guo
https://arxiv.org/abs/2506.14269

High computational density nanophotonic media for machine learning inference
Efficient machine learning inference is essential for the rapid adoption of artificial intelligence across various domains.On-chip optical computing has emerged as a transformative solution for accelerating machine learning tasks, owing to its ultra-low power consumption. However, enhancing the computational density of on-chip optical systems remains a significant challenge, primarily due to the difficulties in miniaturizing and integrating key optical interference components.In this work, we h…

Microsoft has once again been named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning (DSML) Platforms.
https://azure.microsoft.com/en-us/blog

Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms | Microsoft Azure Blog
Find out how Microsoft Azure Machine Learning can help you optimize business processes, improve customer experiences, and drive innovation.

Search for Z/2 eigenfunctions on the sphere using machine learning
Andriy Haydys, Willem Adriaan Salm
https://arxiv.org/abs/2507.13122 https://

Search for Z/2 eigenfunctions on the sphere using machine learning
We use machine learning to search for examples of Z/2 eigenfunctions on the 2-sphere. For this we created a multivalued version of a feedforward deep neural network, and we implemented it using the JAX library. We found Z/2 eigenfunctions for three cases: In the first two cases we fixed the branch points at the vertices of a tetrahedron and at a cube respectively. In a third case, we allowed the AI to move the branch points around and, in the end, it positioned the branch points at the vertices…

Machine Mirages: Defining the Undefined
Hamidou Tembine
https://arxiv.org/abs/2506.13990 https://arxiv.org/pdf/2506.13990

Machine Mirages: Defining the Undefined
As multimodal machine intelligence systems started achieving average animal-level and average human-level fluency in many measurable tasks in processing images, language, and sound, they began to exhibit a new class of cognitive aberrations: machine mirages. These include delusion, illusion, confabulation, hallucination, misattribution error, semantic drift, semantic compression, exaggeration, causal inference failure, uncanny valley of perception, bluffing-patter-bullshitting, cognitive stereo…

Call for Papers: Religion and AI in Science Fiction and Horror
https://ift.tt/LWmMxYF
Book Title: A (Holy?) Ghost in the Machine: Religion and Artificial Intelligence in Science-Fiction…
via Input 4 RELCFP

Call for Papers: Religion and AI in Science Fiction and Horror
Book Title: A (Holy?) Ghost in the Machine: Religion and Artificial Intelligence in Science-Fiction and Horror Editor: Gregory Stevenson, York University The genres of Science-Fiction and Horror ha…

My favourite #summerbeverage with #caffeine: A lungo directly from the Espresso machine into a mug full of ice cubes made with an ice cube machine. Result: Ice-cold #coffee with crema. 😋 No sugar or milk …

Discrimination of neutron-$\gamma$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
S. Panda (a,b), P. K. Netrakanti (a), S. P. Behera (a,b), R. R. Sahu (a), K. Kumar (a,b), R. Sehgal (a), D. K. Mishra (a,b), V. Jha (a,b)
https://arxiv.org/abs/2506.13802

Discrimination of neutron-$γ$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
In this work, we present results for discrimination of neutron and $γ$ events using a plastic scintillator detector with pulse shape discrimination capabilities. Machine learning (ML) algorithms are used to improve the discriminatory power between neutron and $γ$ events at lower energy ranges which otherwise are not addressed by the conventional pulse shape discrimination techniques. The use of a multilayer perceptron with Bayesian inference (MLPBNN) and support vector machine (SVM) algorithm…

Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
Timothy Heightman, Edward Jiang, Ruth Mora-Soto, Maciej Lewenstein, Marcin P{\l}odzie\'n
https://arxiv.org/abs/2507.12117

Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical data processing. However, due to the exponential growth of the Hilbert space, QML faces practical limits in classical simulations with the state-vector representation of quantum system. On the other hand, phase-space methods offer an alternative by encoding quantum states as quasi-probability functions. Building on pri…

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
https://arxiv.org/abs/2506.15223

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLI…

fun #chinafake fact: many believe that http://www.eptoys.net is short for “ever profit toys”, but it actually stands for “eepy toys” @…

Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods
Jordan Krywonos, Yurii Kvasiuk, Matthew C. Johnson, Moritz M\"unchmeyer
https://arxiv.org/abs/2507.12530

Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods
For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or baryons, from observed luminous tracers. The dominant approach in cosmology has been to use the so-called halo model, which assumes radially symmetric profiles centered around luminous tracers such as galaxies. More recently, field-level machine learning methods have been proposed that can learn to estimate the unobserved field after being trained on simulations. Ho…

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Dana Lupsa, Sanda-Maria Avram
https://arxiv.org/abs/2506.15650 https://

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect clas…

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Laura Boggia, Carlos Cocha, Fotis Giasemis, Joachim Hansen, Patin Inkaew, Kaare Endrup Iversen, Pratik Jawahar, Henrique Pineiro Monteagudo, Micol Olocco, Sten Astrand, Martino Borsato, Leon Bozianu, Steven Schramm, the SMARTHEP Network

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large …

Machine Learning-Based Estimation of Monthly GDP
Yonggeun Jung
https://arxiv.org/abs/2506.14078 https://arxiv.org/pdf/2506.14078

Machine Learning-Based Estimation of Monthly GDP
This paper proposes a scalable framework to estimate monthly GDP using machine learning methods. We apply Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Extreme Gradient Boosting (XGBoost), and Elastic Net regression to map monthly indicators to quarterly GDP growth, and reconcile the outputs with actual aggregates. Using data from China, Germany, the UK, and the US, our method delivers robust performance across varied data environments. Benchmark comparisons with prior U…

The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
Michele Alberti (LSL), Fran\c{c}ois Bobot (LSL), Julien Girard-Satabin (LSL), Alban Grastien (LSL), Aymeric Varasse (LSL), Zakaria Chihani (LSL)
https://arxiv.org/abs/2506.12084

The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
The formal specification and verification of machine learning programs saw remarkable progress in less than a decade, leading to a profusion of tools. However, diversity may lead to fragmentation, resulting in tools that are difficult to compare, except for very specific benchmarks. Furthermore, this progress is heavily geared towards the specification and verification of a certain class of property, that is, local robustness properties. But while provers are becoming more and more efficient at…

The Hyperpersonalized AI Slop Silo Machine Is Here https://www.404media.co/the-ai-slop-niche-machine-is-here/

Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
Suman Raj, Swapnil Padhi, Ruchi Bhoot, Prince Modi, Yogesh Simmhan
https://arxiv.org/abs/2506.14857

Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logistics, and disaster management. In this paper, we examine the use of drones for assisting visually impaired people (VIPs) in navigating through outdoor urban environments. Specifically, we present a perception-based path planning system for local planning around the neighborhood of the VIP, integrated with a global plann…

See What I Mean? CUE: A Cognitive Model of Understanding Explanations
Tobias Labarta, Nhi Hoang, Katharina Weitz, Wojciech Samek, Sebastian Lapuschkin, Leander Weber
https://arxiv.org/abs/2506.14775

See What I Mean? CUE: A Cognitive Model of Understanding Explanations
As machine learning systems increasingly inform critical decisions, the need for human-understandable explanations grows. Current evaluations of Explainable AI (XAI) often prioritize technical fidelity over cognitive accessibility which critically affects users, in particular those with visual impairments. We propose CUE, a model for Cognitive Understanding of Explanations, linking explanation properties to cognitive sub-processes: legibility (perception), readability (comprehension), and inter…

#Springer Nature book on #machinelearning is full of made-up #citations

Machine-Readable Ads: Accessibility and Trust Patterns for AI Web Agents interacting with Online Advertisements
Joel Nitu, Heidrun M\"uhle, Andreas St\"ockl
https://arxiv.org/abs/2507.12844

Machine-Readable Ads: Accessibility and Trust Patterns for AI Web Agents interacting with Online Advertisements
Autonomous multimodal language models are rapidly evolving into web agents that can browse, click, and purchase items on behalf of users, posing a threat to display advertising designed for human eyes. Yet little is known about how these agents interact with ads or which design principles ensure reliable engagement. To address this, we ran a controlled experiment using a faithful clone of the news site TT.com, seeded with diverse ads: static banners, GIFs, carousels, videos, cookie dialogues, a…

Interesting… but why is this at the browser level instead of OS level? 🤔 #Chrome…

Fighting Unwanted Notifications with Machine Learning in Chrome
Notifications in Chrome are a useful feature to keep up with updates from your favorite sites. However, we know that some notifications may...

Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation
Madox C. McGrae-Menge, Jacob R. Pierce, Frederico Fiuza, E. Paulo Alves
https://arxiv.org/abs/2506.14048

Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation
Machine learning is offering powerful new tools for the development and discovery of reduced models of nonlinear, multiscale plasma dynamics from the data of first-principles kinetic simulations. However, ensuring the physical consistency of such models requires embedding fundamental symmetries of plasma dynamics. In this work, we explore a symmetry-embedding strategy based on data augmentation, where symmetry-preserving transformations (e.g., Lorentz and Galilean boosts) are applied to simulat…

Equalized Hyperspin Machine
Marcello Calvanese Strinati, Claudio Conti
https://arxiv.org/abs/2507.12940 https://arxiv.org/pdf/2507.12…

Equalized Hyperspin Machine
The reliable simulation of spin models is of critical importance to tackle complex optimization problems that are intractable on conventional computing machines. The recently introduced hyperspin machine, which is a network of linearly and nonlinearly coupled parametric oscillators, provides a versatile simulator of general classical vector spin models in arbitrary dimension, finding the minimum of the simulated spin Hamiltonian and implementing novel annealing algorithms. In the hyperspin mach…

A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions
Lang Qin, Yuejin Xie, Daili Hua, Xuhui Meng
https://arxiv.org/abs/2506.14638

A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions
Extreme weather events increasingly threaten the insurance and real estate industries, creating conflicts between profitability and homeowner burdens. To address this, we propose the SSC-Insurance Model, which integrates SMOTE, SVM, and C-D-C algorithms to evaluate weather impacts on policies and investments. Our model achieves 88.3% accuracy in Zhejiang and 79.6% in Ireland, identifying a critical threshold (43% weather increase) for insurance viability. Additionally, we develop the TOA-Preser…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
Yang, Yu, Wu, Yan, Li, Cheng, Zou, Fang, Yin, Liu, Han, Lu, Wang

Ironically eponymous.
Springer Nature Book on Machine Learning is Full of Made-up Citations
'Based on a tip from a reader, we checked 18 of the 46 citations in the book. Two-thirds of them either did not exist or had substantial errors. And three researchers cited in the book confirmed the works they supposedly authored were fake or the citation contained substantial errors.'

Springer Nature book on machine learning is full of made-up citations
Would you pay $169 for an introductory ebook on machine learning with citations that appear to be made up? If not, you might want to pass on purchasing Mastering Machine Learning: From Basics to Ad…

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang, Jie Fu, Wendy Hui Wang, Weiran Liu, Yuan Hong
https://arxiv.org/abs/2506.13009

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods. However, the lack of formal guarantees in these methods necessitates the need for robust evaluation frameworks to assess their privacy and effectiveness. In this wo…

Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder
Robert Czupryniak, Abhishek Chakraborty, Andrew N. Jordan, John C. Howell
https://arxiv.org/abs/2506.14906

Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder
We apply machine learning methods to demonstrate range superresolution in remote sensing radar detection. Specifically, we implement a denoising autoencoder to estimate the distance between two equal intensity scatterers in the subwavelength regime. The machine learning models are trained on waveforms subject to a bandlimit constraint such that ranges much smaller than the inverse bandlimit are optimized in their precision. The autoencoder achieves effective dimensionality reduction, with the b…

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
Marouane Il Idrissi, Agathe Fernandes Machado, Arthur Charpentier
https://arxiv.org/abs/2506.13900

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient al…

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
Guobin Zhao, Pengyu Zhao, Yongchul G. Chung
https://arxiv.org/abs/2506.14845

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily relies on high-quality, computation-ready structural data. However, recent studies have revealed significant error rates within existing MOF databases, posing a critical data problem that hinders efficient high-throughput computational screening. While rule-based algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen-Manz) have been developed to address this…

Enhancing Symbolic Machine Learning by Subsymbolic Representations
Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
https://arxiv.org/abs/2506.14569 …

Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach:…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing
Yujie Feng, Yin Yang, Xiaohong Fan, Zhengpeng Zhang, Lijing Bu, Jianping Zhang

Leveraging Quantum Layers in Classical Neural Networks
Silvie Ill\'esov\'a
https://arxiv.org/abs/2507.12505 https://arxiv.org…

Leveraging Quantum Layers in Classical Neural Networks
Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers within classical convolutional neural network architectures, aiming to leverage quantum entanglement and feature mapping to enhance learning capabilities. A detailed methodology for constructing and training such hybrid models is presented, using PyTorch and Qiskit Machine Learning frameworks. Experiments investigate t…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
Generalized Random Forests using Fixed-Point Trees
https://…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Rage Against the Machine:
🎵 Bulls on Parade
#RageAgainsttheMachine
https://fororchestra.bandcamp.com/track/rage-against-the-machine-bulls-on-parade
https://open.spotify.com/track/0tZ3mElWcr74OOhKEiNz1x

WebXAII: an open-source web framework to study human-XAI interaction
Jules Leguy, Pierre-Antoine Jean, Felipe Torres Figueroa, S\'ebastien Harispe
https://arxiv.org/abs/2506.14777

WebXAII: an open-source web framework to study human-XAI interaction
This article introduces WebXAII, an open-source web framework designed to facilitate research on human interaction with eXplainable Artificial Intelligence (XAI) systems. The field of XAI is rapidly expanding, driven by the growing societal implications of the widespread adoption of AI (and in particular machine learning) across diverse applications. Researchers who study the interaction between humans and XAI techniques typically develop ad hoc interfaces in order to conduct their studies. The…

Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Haosheng Zuo, Feifei Niu, Chuanyi Li
https://arxiv.org/abs/2506.15098 …

Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Enhancement reports (ERs) serve as a critical communication channel between users and developers, capturing valuable suggestions for software improvement. However, manually processing these reports is resource-intensive, leading to delays and potential loss of valuable insights. To address this challenge, enhancement report approval prediction (ERAP) has emerged as a research focus, leveraging machine learning techniques to automate decision-making. While traditional approaches have employed fe…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- The Target Polish: A New Approach to Outlier-Resistant Non-Negative Matrix and Tensor Factorization
Paul Fogel, Christophe Geissler, George Luta

The first open machine translation system for the Chechen language
Abu-Viskhan A. Umishov, Vladislav A. Grigorian
https://arxiv.org/abs/2507.12672 https://…

The first open machine translation system for the Chechen language
We introduce the first open-source model for translation between the vulnerable Chechen language and Russian, and the dataset collected to train and evaluate it. We explore fine-tuning capabilities for including a new language into a large language model system for multilingual translation NLLB-200. The BLEU / ChrF++ scores for our model are 8.34 / 34.69 and 20.89 / 44.55 for translation from Russian to Chechen and reverse direction, respectively. The release of the translation models is accomp…

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Yang Luo, Arunprakash Jayaprakash, Gaojie Chen, Chong Huang, Qu Luo, De Mi, Pei Xiao
https://arxiv.org/abs/2506.14557

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Althoug…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Local minima of the empirical risk in high dimension: General theorems and convex examples
Kiana Asgari, Andrea Montanari, Basil Saeed

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang, Jie Fu, Wendy Hui Wang, Weiran Liu, Yuan Hong
https://arxiv.org/abs/2506.13009

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods. However, the lack of formal guarantees in these methods necessitates the need for robust evaluation frameworks to assess their privacy and effectiveness. In this wo…

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?
Matthew Walker, Keith T. Butler
https://arxiv.org/abs/2507.13246

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?
Computational screening has become a powerful complement to experimental efforts in the discovery of high-performance photovoltaic (PV) materials. Most workflows rely on density functional theory (DFT) to estimate electronic and optical properties relevant to solar energy conversion. Although more efficient than laboratory-based methods, DFT calculations still entail substantial computational and environmental costs. Machine learning (ML) models have recently gained attention as surrogates for …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Haoxuan Ma, Xishun Liao, Yifan Liu, Qinhua Jiang, Chris Stanford, Shangqing Cao, Jiaqi Ma

The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Zhouqi Hua, Wenwei Zhang, Chengqi Lyu, Yuzhe Gu, Songyang Gao, Kuikun Liu, Kai Chen
https://arxiv.org/abs/2507.13332

The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Length generalization, the ability to solve problems of longer sequences than those observed during training, poses a core challenge of Transformer-based large language models (LLM). Although existing studies have predominantly focused on data-driven approaches for arithmetic operations and symbolic manipulation tasks, these approaches tend to be task-specific with limited overall performance. To pursue a more general solution, this paper focuses on a broader case of reasoning problems that are…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Boolformer: Symbolic Regression of Logic Functions with Transformers
d'Ascoli, Renard, Papadopoulos, Bengio, Susskind, Abb\'e

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
Sanghyun Kim, Jiwon Seo
https://arxiv.org/abs/2507.12706

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient …

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning
Dustin Ray, Caroline El Jazmi
https://arxiv.org/abs/2506.11458 https…

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning
Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service (MLaaS) to market. Outsourcing the operation and training of these systems to powerful hardware carries numerous advantages, but challenges arise when privacy and the correctness of work carried out must be ensured. Recent advance…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Au...
Anadria, Dobbe, Giachanou, Kuiper, Bartels, van Amsterdam, de Troya, Z\"urcher, Oberski
…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
Bhiman Kumar Baghel, Scott M. Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li

[2025-06-19 Thu (UTC), 6 new articles found for stat.ML Machine Learning]
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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/4]:
- Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation
Xiang Geng, Zhejian Lai, Jiajun Chen, Hao Yang, Shujian Huang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Mohamed elShehaby, Ashraf Matrawy

[2025-06-20 Fri (UTC), no new articles found for stat.ML Machine Learning]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/7]:
- A Comprehensive Survey on Continual Learning in Generative Models
Guo, Zeng, Zhu, Wang, Wang, Zhou, Zhao, Liu, Ma, Wang, Zhang, Liu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and ...
Debabrota Basu, Debarshi Chanda

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series...
Yijun Lin, Yao-Yi Chiang

An Observation on Lloyd's k-Means Algorithm in High Dimensions
David Silva-S\'anchez, Roy R. Lederman
https://arxiv.org/abs/2506.14952 https://

An Observation on Lloyd's k-Means Algorithm in High Dimensions
Clustering and estimating cluster means are core problems in statistics and machine learning, with k-means and Expectation Maximization (EM) being two widely used algorithms. In this work, we provide a theoretical explanation for the failure of k-means in high-dimensional settings with high noise and limited sample sizes, using a simple Gaussian Mixture Model (GMM). We identify regimes where, with high probability, almost every partition of the data becomes a fixed point of the k-means algorith…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
Kazakov, Kulichenko, Kovalev, Treskova, Barma, Malakhov, Oseledets, Shipulin

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
Joris Suurmeijer, Ivo Pascal de Jong, Matias Valdenegro-Toro, Andreea Ioana Sburlea

[2025-06-20 Fri (UTC), no new articles found for cs.LG Machine Learning]
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FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, Alexander Keller
https://arxiv.org/abs/2507.12144…

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed to respect spherical geometry and to accurately model the spatially correlated probabilistic nature of the problem, resulting in stable spectra and realistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-ba…

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu, Ruibin Mao, Xiaojuan Qi, X. Sharon Hu, Xunzhao Yin, Can Li
https://arxiv.org/abs/2507.12384

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the diff…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Multi-view biomedical foundation models for molecule-target and property prediction
Parthasarathy Suryanarayanan, et al.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation
Ray, Pan, Gu, Du, Feng, Ananthanarayanan, Netravali, Jiang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- A Computational Theory and Semi-Supervised Algorithm for Clustering
Nassir Mohammad

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/8]:
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing