Tootfinder

Vikings' Carson Wentz drawing rave reviews as he prepares for his first meaningful start in nearly three years

https://www.cbssports.com/nfl/news/v…

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann, Christoph Brune, Jelmer M. Wolterink
https://arxiv.org/abs/2509.13846

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstream tasks. In this work, we challenge this assumption and demonstrate that meaningful structure in the latent space does not emerge naturally. Instead, it must be explicitly induced. We propose a method that aligns representations from different views of the data to align complementary information without inducing false…

Democracies should stop focusing on efficiency: Lack thereof and friction are important parts of what defines a healthy democracy. Efficiency will always sacrifice participation, representation and meaningful political discourse. The focus on it is a path into authoritarianism. In the essay ...

Not a guarantuee for succes or a meaningful outcome but Europe is at the table in Washington and Zelensky has some "cards" (instead of none), if you see who are present.
Volodymyr Zelenskyy
Emmanuel Macron.
Friedrich Merz.
Keir Starmer.
Giorgia Meloni.
Alexander Stubb.
Ursula von der Leyen.
(NATO) Mark Rutte.
#EU

The biggest impact of #LLM has not been due to the technology's capabilities, but rather its flaws. Because the #AI revolution must succeed at any cost, but the actual tech is flawed, orgs demanding more AI outputs MUST lower their bar for quality.
This creates space for

Aura UX
Shortcuts to establishing the legitimacy of design sabotage your ability to achieve anything meaningful.

A meaningful optimal control problem in quantum and classical physics
Arnaud Lazarus (DALEMBERT), Emmanuel Tr\'elat (LJLL)
https://arxiv.org/abs/2507.13125

A meaningful optimal control problem in quantum and classical physics
In this paper we study and solve an optimal control problem motivated by applications in quantum and classical physics. Although apparently simple, this optimal control problem is not easy to solve and we resort to various elaborated methods of optimal control theory. We finally show its relationships to two problems in physics: the computation of the ground state for 1D Schr{ö}dinger operators with a finite potential well, and the optimal dynamical Kapitza stabilization problem.

"Unlocking the drivers of voluntary single-use plastic reduction"
#Plastic #Plastics
https://…

Unlocking the drivers of voluntary single-use plastic reduction
A belief that individual actions do make a meaningful difference is among the common factors that motivate people to voluntarily reduce their consumption of single-use plastics (SUP), a Griffith University study has discovered.

"Looks Good to Me"
https://www.manning.com/books/looks-good-to-me?utm_source=frankel&utm_medium=affiliate&utm_campaign=affiliate&a_aid=frankel

Randall Munroe (of xkcd fame) posted about an analysis done by David R. Hagen related to his "missing 11th" comic. Great read, which includes some nice statistical analysis, but more interestingly, bias and artifacts.
https://bsky.app/profile/xkcd.com/post/3lrxgvl677k2w

#Multisensory vs. #unisensory learning: how they shape effective connectivity networks subserving unimodal and multimodal integration

Frontiers | Multisensory vs. unisensory learning: how they shape effective connectivity networks subserving unimodal and multimodal integration
The brain synthesizes meaningful interpretations out of the surrounding environment, by integrating sensory input collected by multiple senses. Learning base...

I just finished "An Ember in the Ashes" by Sabaa Tahir. I picked it up because I found her non-fantasy novel "All My Rage" extremely compelling, and I enjoyed fantasy a lot as a genre in my youth but as my politics have changed I'm too disappointed to enjoy a lot of fantasy any more but I thought Tahir might not fall into that.
Although I don't think Ember in the Ashes is bad, it's definitely not what I was looking for, and I don't think I'll pursue the sequels, at least not right now.
Overall the writing was weaker in a lot of ways than All My Rage, and in terms of basic fantasy quality, the worldbuilding was noticeably lacking, the romantic subplot felt stilted, and the politics were a bit murky. Compared to what I felt was Tahir's deft, delicate, and very meaningful handling of trauma in All My Rage, trauma in Ember felt overused and thin.
I also recently finished "Black Panther Red Wolf" by Marlon James, which had excellent world building and (to me) a much more nuance in both romance and in handling trauma. The politics felt a bit off for this one too I guess, but for me it was an overall more enjoyable (if much more difficult) read.
#AmReading

A Neuromorphic Model of Learning Meaningful Sequences with Long-Term Memory
Laxmi R. Iyer, Ali A. Minai
https://arxiv.org/abs/2509.12850 https://arxiv.org/…

A Neuromorphic Model of Learning Meaningful Sequences with Long-Term Memory
Learning meaningful sentences is different from learning a random set of words. When humans understand the meaning, the learning occurs relatively quickly. What mechanisms enable this to happen? In this paper, we examine the learning of novel sequences in familiar situations. We embed the Small World of Words (SWOW-EN), a Word Association Norms (WAN) dataset, in a spiking neural network based on the Hierarchical Temporal Memory (HTM) model to simulate long-term memory. Results show that in the …

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Yulu Qin, Dheeraj Varghese, Adam Dahlgren Lindstr\"om, Lucia Donatelli, Kanishka Misra, Najoung Kim
https://arxiv.org/abs/2507.13328

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Does vision-and-language (VL) training change the linguistic representations of language models in meaningful ways? Most results in the literature have shown inconsistent or marginal differences, both behaviorally and representationally. In this work, we start from the hypothesis that the domain in which VL training could have a significant effect is lexical-conceptual knowledge, in particular its taxonomic organization. Through comparing minimal pairs of text-only LMs and their VL-trained coun…

😣 Effortful pastimes are perceived as more meaningful than those requiring minimal effort, study finds
https://medicalxpress.com/news/2025-08-effortful-pastimes-meaningful-requiring-minimal.html

Effortful pastimes are perceived as more meaningful than those requiring minimal effort, study finds
Past studies suggest that how people spend their time outside of work or academic endeavors can contribute to their overall well-being and life satisfaction. Yet how humans perceive different leisure activities that they engage in and the extent to which they feel that these activities contribute to their life's purpose has not yet been extensively investigated.

Funding AI for Good: A Call for Meaningful Engagement
Hongjin Lin, Anna Kawakami, Catherine D'Ignazio, Kenneth Holstein, Krzysztof Gajos
https://arxiv.org/abs/2509.12455 htt…

Funding AI for Good: A Call for Meaningful Engagement
Artificial Intelligence for Social Good (AI4SG) is a growing area exploring AI's potential to address social issues like public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face inadequate community engagement and sustainability challenges. Funding agendas play a crucial role in framing AI4SG initiatives and shaping their approaches. Through a qualitative analysis of 35 funding documents -- representing about $410 m…

"How can we learn and use AI at the same time?:: Participatory Design of GenAI with High School Students
Isabella Pu, Prerna Ravi, Linh Dieu Dinh, Chelsea Joe, Caitlin Ogoe, Zixuan Li, Cynthia Breazeal, Anastasia K. Ostrowski
https://arxiv.org/abs/2506.15525

A Modular PyTheus Quantum Network Interpreter: Automated Analysis and Visualization of Optimized Quantum Architectures
S. K. Rithvik
https://arxiv.org/abs/2507.12997

A Modular PyTheus Quantum Network Interpreter: Automated Analysis and Visualization of Optimized Quantum Architectures
We present a modular interpreter for PyTheus-optimized quantum networks that automatically analyzes and visualizes complex quantum architectures discovered through automated optimization. The interpreter addresses the critical challenge of understanding machine-designed quantum networks by providing robust algorithms for functional role identification, graph-theoretical analysis, and physically meaningful visualization across the major classes of PyTheus-generated networks. Our interpreter acce…

NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification
Wajih Hassan Raza, Aamir Bader Shah, Yu Wen, Yidan Shen, Juan Diego Martinez Lemus, Mya Caryn Schiess, Timothy Michael Ellmore, Renjie Hu, Xin Fu
https://arxiv.org/abs/2506.14970…

NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification
The integration of multi-modal Magnetic Resonance Imaging (MRI) and clinical data holds great promise for enhancing the diagnosis of neurological disorders (NDs) in real-world clinical settings. Deep Learning (DL) has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis. However, existing DL approaches struggle to effectively leverage multi-modal MRI and clinical data, leading to suboptimal performance. To address this challenge, we util…

The Demographics of Binary Companions to Stripped-Envelope Supernovae: Confronting Observations with Population Synthesis
E. Zapartas, O. D. Fox, J. Su, D. Souropanis, M. R. Drout, K. A. Rocha, S. D. van Dyk, B. F. Williams, M. Briel, M. Renzo, J. J. Andrews, T. Fragos, S. Gossage, M. U. Kruckow, C. Liotine, S. D. Ryder, P. M. Srivastava, E. Teng
https://

The Demographics of Binary Companions to Stripped-Envelope Supernovae: Confronting Observations with Population Synthesis
Stripped-envelope supernovae (SESNe) mark the deaths of massive stars without hydrogen-rich envelopes. Most SESNe likely originate from binary systems where a companion stripped the progenitor of its envelope. Years of HST imaging of nearby SESNe sites have produced a statistically meaningful sample of constraints on surviving binary companions. We assemble the current sample of six companion detections and six non-detections from the literature, re-analyzing whenever needed. We then conduct th…

AI tools are already helping marginalized communities daily - translations, image descriptions, text simplification.
You can/should hate Big Tech while recognizing real impacts - anything else isn't meaningful discourse.
#AI #CCC

Though we often crave a silver bullet solution, in reality, meaningful change often comes from many small efforts working in harmony, just like it happens in climate activism. full article @ https://www.brichapman.com/p/silver-buckshot

Silver Buckshot
Why complex problems need plural solutions

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing
https://arxiv.org/abs/2508.11672

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive…

VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Jun Du, Weiwei Xing, Ming Li, Fei Richard Yu
https://arxiv.org/abs/2509.14060 ht…

VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Current multi-object tracking (MOT) algorithms typically overlook issues inherent in low-quality videos, leading to significant degradation in tracking performance when confronted with real-world image deterioration. Therefore, advancing the application of MOT algorithms in real-world low-quality video scenarios represents a critical and meaningful endeavor. To address the challenges posed by low-quality scenarios, inspired by vision-language models, this paper proposes a Visual Semantic Enhanc…

Responsibility and Engagement - Evaluating Interactions in Social Robot Navigation
Malte Probst, Raphael Wenzel, Monica Dasi
https://arxiv.org/abs/2509.12890 https://

Responsibility and Engagement - Evaluating Interactions in Social Robot Navigation
In Social Robot Navigation (SRN), the availability of meaningful metrics is crucial for evaluating trajectories from human-robot interactions. In the SRN context, such interactions often relate to resolving conflicts between two or more agents. Correspondingly, the shares to which agents contribute to the resolution of such conflicts are important. This paper builds on recent work, which proposed a Responsibility metric capturing such shares. We extend this framework in two directions: First, w…

Representing Speech Through Autoregressive Prediction of Cochlear Tokens
Greta Tuckute, Klemen Kotar, Evelina Fedorenko, Daniel L. K. Yamins
https://arxiv.org/abs/2508.11598 htt…

Representing Speech Through Autoregressive Prediction of Cochlear Tokens
We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms raw audio into a time-frequency representation based on the human cochlea, from which we extract discrete \textbf{cochlear tokens}. The second stage applies an autoregressive sequence model over the cochlear tokens. AuriStream learns meaningful phoneme and word representations, and state-of-the-art lexical semantics.…

Generative Large Language Models for Knowledge Representation: A Systematic Review of Concept Map Generation
Xiaoming Zhai
https://arxiv.org/abs/2509.14554 https://

Generative Large Language Models for Knowledge Representation: A Systematic Review of Concept Map Generation
The rise of generative large language models (LLMs) has opened new opportunities for automating knowledge representation through concept maps, a long-standing pedagogical tool valued for fostering meaningful learning and higher-order thinking. Traditional construction of concept maps is labor-intensive, requiring significant expertise and time, limiting their scalability in education. This review systematically synthesizes the emerging body of research on LLM-enabled concept map generation, foc…

All that structure matches does not glitter
Maya M. Martirossyan, Thomas Egg, Philipp Hoellmer, George Karypis, Mark Transtrum, Adrian Roitberg, Mingjie Liu, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani
https://arxiv.org/abs/2509.12178

All that structure matches does not glitter
Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field depends critically on robust benchmarks and minimal, information-rich datasets that enable meaningful model evaluation. This paper critically examines common datasets and reported metrics for a crystal structure prediction task$\unicode{x2014}$generating the most likely structures given the chemical composition of a m…

Anyone who follows me has probably gathered that I've become a fan of Discourse after running a few Discourse instances. They are currently requesting feedback.
I was pleased to see that they used a survey vendor that appears to have meaningful a11y — I didn't look for aria labels so I won't speak to that, but what I did notice is that there is explicitly-labeled support for keyboard-only interaction on every page, and I really appreciated that thoughtfulness.

Osu2MIR: Beat Tracking Dataset Derived From Osu! Data
Ziyun Liu, Chris Donahue
https://arxiv.org/abs/2509.12667 https://arxiv.org/pdf/2509.12667

Osu2MIR: Beat Tracking Dataset Derived From Osu! Data
In this work, we explore the use of Osu!, a community-based rhythm game, as an alternative source of beat and downbeat annotations. Osu! beatmaps are created and refined by a large, diverse community and span underrepresented genres such as anime, Vocaloid, and video game music. We introduce a pipeline for extracting annotations from Osu! beatmaps and partition them into meaningful subsets. Through manual analysis, we find that beatmaps with a single timing point or widely spaced multiple timin…

The iterated Golub-Kahan-Tikhonov method
Davide Bianchi, Marco Donatelli, Davide Furch\`i, Lothar Reichel
https://arxiv.org/abs/2507.12307 https://

The iterated Golub-Kahan-Tikhonov method
The Golub-Kahan-Tikhonov method is a popular solution technique for large linear discrete ill-posed problems. This method first applies partial Golub-Kahan bidiagonalization to reduce the size of the given problem and then uses Tikhonov regularization to compute a meaningful approximate solution of the reduced problem. It is well known that iterated variants of this method often yield approximate solutions of higher quality than the standard non-iterated method. Moreover, it produces more accur…

What's the best way to average a bunch of angles to give a meaningful result around the wrap (i.e. -179 and 179 degrees should average to 180 degrees, not zero)?
My initial thought is to convert the angle to 2D I/Q coordinates on the complex plane, then do a vector average of the points, then atan2 to get back an angle.
Is there a better option that doesn't do trig for every sample?

Automata Models for Effective Bug Description
Tom Yaacov, Gera Weiss, Gal Amram, Avi Hayoun
https://arxiv.org/abs/2507.11146 https://…

Automata Models for Effective Bug Description
Debugging complex systems is a crucial yet time-consuming task. This paper presents the use of automata learning and testing techniques to obtain concise and informative bug descriptions. We introduce the concepts of Failure Explanations (FE), Eventual Failure Explanations (EFE), and Early Detection (ED) to provide meaningful summaries of failing behavior patterns. By factoring out irrelevant information and focusing on essential test patterns, our approach aims to enhance bug detection and und…

Formalizing dimensional analysis using the Lean theorem prover
Maxwell P. Bobbin, Colin Jones, John Velkey, Tyler R. Josephson
https://arxiv.org/abs/2509.13142 https://

Formalizing dimensional analysis using the Lean theorem prover
Dimensional analysis is fundamental to the formulation and validation of physical laws, ensuring that equations are dimensionally homogeneous and scientifically meaningful. In this work, we use Lean 4 to formalize the mathematics of dimensional analysis. We define physical dimensions as mappings from base dimensions to exponents, prove that they form an Abelian group under multiplication, and implement derived dimensions and dimensional homogeneity theorems. Building on this foundation, we intr…

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Haritha Ananthakrishnan, Oktie Hassanzadeh, Horst Samulowitz, Kavitha Srinivas
https://arxiv.org/abs/2507.11505

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinable tables have been discovered through a search for similar columns, where two columns are considered similar syntactically if there is a set overlap or they are considered similar semantically if either the column embeddings or value embeddings are closer in the embedding space. However, for enterprise data lakes, col…

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
Kelly Kurowski, Xixi Lu, Hajo A. Reijers
https://arxiv.org/abs/2507.11364

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as emails, reports, and scans. It is estimated to constitute approximately 80% of enterprise data. Despite the valuable insights it can offer, extracting meaningful information from unstructured data is more complex compared to structured data. Robotic Process Automation (RPA) has ga…

This Is Not the World I Wanted to Leave for You: Reflections on Legacy, Loss, and the Future We Shape
I have been thinking a great deal lately about living and dying, and about the strange, stubborn human hunger to leave something meaningful behind. The faces of those I have known who have already passed return to me in quiet moments, and I find myself watching those who are, even now, nearing the end of their own stories. I also include my final braided prairie knot…

The pearl-clutching centrist crowd likes to imagine that shaming and shunning are the slippery slope that leads to political violence.
They have it backwards.
Shaming and shunning are the •alternative• to political violence. So is meaningful democracy. So is a free press that remains steadfastly grounded in reality. So is a judiciary that applies the law, however flawed, with actual judgement and justice.
5/

Why does your graph neural network fail on some graphs? Insights from exact generalisation error
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
https://arxiv.org/abs/2509.10337

Why does your graph neural network fail on some graphs? Insights from exact generalisation error
Graph Neural Networks (GNNs) are widely used in learning on graph-structured data, yet a principled understanding of why they succeed or fail remains elusive. While prior works have examined architectural limitations such as over-smoothing and over-squashing, these do not explain what enables GNNs to extract meaningful representations or why performance varies drastically between similar architectures. These questions are related to the role of generalisation: the ability of a model to make acc…

Understanding the effect of wall elasticity in turbulent channel flows
M. Koseki, M. S. Aswathy, M. E. Rosti
https://arxiv.org/abs/2509.11142 https://arxiv…

Understanding the effect of wall elasticity in turbulent channel flows
This study compares turbulent channel flows over elastic walls with those over rough walls, to explore the role of the dynamic change of shape of the wall on turbulence. The comparison is made meaningful by generating rough walls from instantaneous configurations of the elastic cases. The aim of this comparison is to individually understand the role of fluid-structure interaction effects and the role of wall shape/undulations in determining the overall physics of flow near elastic walls. With a…

From Amnesty
Right now, governments in Canada are pushing forward development bills Bill 5 (Ontario), Bill 15 (British Columbia), and Bill C-5 (Federal) that fast-track corporate interests while sidelining Indigenous Peoples’ rights, labour rights, and environmental protections.
These bills would:
Bypass the right of Free, Prior and Informed Consent (FPIC);
Rush through major infrastructure projects, like pipelines and highways, without meaningful public input or envi…

So the main "arguments" when I say "AI doesn't work" and "it will collapse" are:
1. "You don't know what you're talking about"
2. "It's inevitable and here to stay, might as well go with the program"
3. "But it's almost there! Just last week they released [name of model] that is so close!"
Literally no one ever replies with any concrete examples with how it reliably, ethically and non-wastefully works for them to increase their productivity and improve their and other people's lives in any meaningful way.
It's always ad hominems, hypotheticals or deeply flawed "it sort of works for this".

Curious to read #IcanHazPdf? Thanks!

Spatiotemporal Pattern Mining of Moving Objects: Concepts, Methods, and Challenges - Journal of Geovisualization and Spatial Analysis
Mining potentially meaningful movement patterns of moving objects is of great significance for analyzing human movement law, understanding traffic mechanisms, and analyzing wild animal migration activities. In recent years, the growing availability of movement data from sensor networks and location-aware devices has provided great opportunities for investigating the spatiotemporal patterns of moving objects. A systematic review of this rapidly evolving field is necessary to synthesize existing …

Frame redundancy and Beurling density
Marcin Bownik, Jordy Timo van Velthoven
https://arxiv.org/abs/2509.11887 https://arxiv.org/pdf/2509.11887

Frame redundancy and Beurling density
We show that the frame measure function of a frame in certain reproducing kernel Hilbert spaces on metric measure spaces is given by the reciprocal of the Beurling density of its index set. In addition, we show that each such frame with Beurling density greater than one contains a subframe with Beurling density arbitrary close to one. This confirms that the concept of frame measure function as introduced by Balan and Landau is a meaningful quantitative definition for the redundancy of a large c…

The disappearance of the unclear question: AI speeds up research, but without friction, do students lose the deep thinking that turns bad questions into meaningful learning? https://www.unesco.org/en/articles/disappearance-unclear-question

The disappearance of the unclear question
AI speeds up research, but without friction, do students lose the deep thinking that turns bad questions into meaningful learning?

A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases
Meghyn Bienvenu, Camille Bourgaux, Katsumi Inoue, Robin Jean
https://arxiv.org/abs/2508.07742

A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases
Repair-based semantics have been extensively studied as a means of obtaining meaningful answers to queries posed over inconsistent knowledge bases (KBs). While several works have considered how to exploit a priority relation between facts to select optimal repairs, the question of how to specify such preferences remains largely unaddressed. This motivates us to introduce a declarative rule-based framework for specifying and computing a priority relation between conflicting facts. As the express…

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants
Jacinto Colan, Ana Davila, Yutaro Yamada, Yasuhisa Hasegawa
https://arxiv.org/abs/2507.11460

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants
Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature…

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube
Rongyi Chen, Ziyan Xin, Qing Xiao, Ruiwei Xiao, Jingjia Xiao, Bingbing Zhang, Hong Shen, Zhicong Lu
https://arxiv.org/abs/2509.10957

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube
The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet HCI research lacks systematic understanding of how narrative and visual elements create meaningful spiritual experiences and foster viewer engagement. We present a mixed-methods study of religious videos on YouTube across major religions, developing taxonomies of narrative frameworks, visual…

I did a short keynote for a bunch of composers and music people a few days ago on "AI". And since it landed well but wasn't recorded I'll try to redo it (maybe expand it marginally) and upload it.
It's about AI but more about the permission to to what feels right and humane anyways.
And it sounds weird to give people permission for something natural, but the reaction in that room felt like it was meaningful. That standing against the tide of "AI is wit…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Zetian Sun, Dongfang Li, Zhuoen Chen, Yuhuai Qin, Baotian Hu
https://arxiv.org/abs/2508.10548 https://…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Reward sparsity in long-horizon reinforcement learning (RL) tasks remains a significant challenge, while existing outcome-based reward shaping struggles to define meaningful immediate rewards without introducing bias or requiring explicit task decomposition. Alternatively, verification-based reward shaping uses stepwise critics, but misalignment between immediate rewards and long-term objectives can lead to reward hacking and suboptimal policies. In this work, we address this problem in the con…

X-Part: high fidelity and structure coherent shape decomposition
Xinhao Yan, Jiachen Xu, Yang Li, Changfeng Ma, Yunhan Yang, Chunshi Wang, Zibo Zhao, Zeqiang Lai, Yunfei Zhao, Zhuo Chen, Chunchao Guo
https://arxiv.org/abs/2509.08643

X-Part: high fidelity and structure coherent shape decomposition
Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bound…

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Meghan Wilkinson, Robert H Thomson
https://arxiv.org/abs/2509.09564 https://

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Supervised machine learning techniques rely on labeled data to achieve high task performance, but this requires the labels to capture some meaningful differences in the underlying data structure. For training network intrusion detection algorithms, most datasets contain a series of attack classes and a single large benign class which captures all non-attack network traffic. A review of intrusion detection papers and guides that explicitly state their data preprocessing steps identified that the…

If I went all in on give people deep meaningful thoughtful compliments I think I could way farther in the world then I have just being mildly good at it.

From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
Christian Meske, Justin Brenne, Erdi Uenal, Sabahat Oelcer, Ayseguel Doganguen
https://arxiv.org/abs/2508.06352

From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
Current explainable AI (XAI) approaches prioritize algorithmic transparency and present explanations in abstract, non-adaptive formats that often fail to support meaningful end-user understanding. This paper introduces "Explanatory AI" as a complementary paradigm that leverages generative AI capabilities to serve as explanatory partners for human understanding rather than providers of algorithmic transparency. While XAI reveals algorithmic decision processes for model validation, Explanatory AI…

Our Compass is a vegan abolitionist community focused on nonhuman animals, the harm and suffering inflicted on them due to human exploitation and speciesism, and the necessity of veganism as the only meaningful and humane response to support animals and their liberation from humans. https://our-compass.org

Our Compass
Because justice directs us ...

Biomedical Signal Processing: EEG and ECG Classification with Discrete Wavelet Transforms, Energy Distribution, and Convolutional Neural Networks
Justin London
https://arxiv.org/abs/2508.08602

Biomedical Signal Processing: EEG and ECG Classification with Discrete Wavelet Transforms, Energy Distribution, and Convolutional Neural Networks
Biomedical signal processing extract meaningful information from physiological signals like electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs) to diagnose, monitor, and treat medical conditions and diseases such as seizures, cardiomyopathy, and neuromuscular disorders, respectively. Traditional manual physician analysis of electrical recordings is prone to human error as subtle anomolies may not be detected. Recently, advanced deep learning has significantly imp…

I have a strong urgency to pipe docx files I receive from collaborators through pandoc before editing them. Even a docx-to-docx conversion would separate semantically meaningful formatting from accidental artifacts of previous editing steps like different line height, changing fonts, varying indentation etc. (I resist that urge b/c I don’t want to be responsible for any loss of meaningful formatting, but I cringe every time I have to edit a Word document.)

Learning-Based Data-Assisted Port-Hamiltonian Control for Free-Floating Space Manipulators
Mostafa Eslami, Maryam Babazadeh
https://arxiv.org/abs/2509.09563 https://

Learning-Based Data-Assisted Port-Hamiltonian Control for Free-Floating Space Manipulators
A generic data-assisted control architecture within the port-Hamiltonian framework is proposed, introducing a physically meaningful observable that links conservative dynamics to all actuation, dissipation, and disturbance channels. A robust, model-based controller combined with a high-gain decentralized integrator establishes large robustness margins and strict time-scale separation, ensuring that subsequent learning cannot destabilize the primary dynamics. Learning, selected for its generaliz…

BILL GATE

Psychometric Evaluation of the Culture around Systemic Change Survey: A tool for Assessing Departmental Culture in Physics
Diana Sachmpazidi (Randy), Mike Verostek (Randy), Jayna Petrella (Randy), Siwoo (Randy), Lee, Chandra Turpen
https://arxiv.org/abs/2508.09953

Psychometric Evaluation of the Culture around Systemic Change Survey: A tool for Assessing Departmental Culture in Physics
Physics programs are continually evolving to better support student learning and meet the diverse needs of their populations. Achieving many of these goals requires not only structural adjustments but also fundamental shifts in departmental culture. Recognizing this, disciplinary organizations in physics have placed systemic change and equity at the center of reform efforts, identifying them as essential pillars of meaningful and sustainable change. Yet tools for assessing departmental culture …

Object Fidelity Diffusion for Remote Sensing Image Generation
Ziqi Ye, Shuran Ma, Jie Yang, Xiaoyi Yang, Ziyang Gong, Xue Yang, Haipeng Wang
https://arxiv.org/abs/2508.10801 htt…

Object Fidelity Diffusion for Remote Sensing Image Generation
High-precision controllable remote sensing image generation is both meaningful and challenging. Existing diffusion models often produce low-fidelity images due to their inability to adequately capture morphological details, which may affect the robustness and reliability of object detection models. To enhance the accuracy and fidelity of generated objects in remote sensing, this paper proposes Object Fidelity Diffusion (OF-Diff), which effectively improves the fidelity of generated objects. Spe…

The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
Shrabani Ghosh, Erik Saule
https://arxiv.org/abs/2509.09045 https://

The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
In real-world scenarios, large graphs represent relationships among entities in complex systems. Mining these large graphs often containing millions of nodes and edges helps uncover structural patterns and meaningful insights. Dividing a large graph into smaller subgraphs facilitates complex system analysis by revealing local information. Community detection extracts clusters or communities of graphs based on statistical methods and machine learning models using various optimization techniques.…

From spatial to null infinity: Connecting initial data to peeling
Berend Schneider, Neev Khera
https://arxiv.org/abs/2507.07977 https://

From spatial to null infinity: Connecting initial data to peeling
The asymptotic structure of space-time is studied by imposing conditions on the asymptotics of the metric. These conditions are weak enough to include large classes of physically relevant isolated space-times, but have a rich enough structure to be able to define important physically meaningful quantities like mass, angular momentum, and gravitational waves. By using a unified expansion of the metric in a neighborhood of spatial infinity that includes a piece of null infinity, we connect the …

Question for those who know: is it meaningful to attempt to geolocate an #IPv6 address?

Has Anthony Richardson played his last meaningful snaps for the Colts? History not on QB's side

https://www.cbssports.com/nfl/news/has-ant

Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses
The Tien Mai
https://arxiv.org/abs/2509.07501 https://ar…

Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses
Sparse regression problems, where the goal is to identify a small set of relevant predictors, often require modeling not only main effects but also meaningful interactions through other variables. While the pliable lasso has emerged as a powerful frequentist tool for modeling such interactions under strong heredity constraints, it lacks a natural framework for uncertainty quantification and incorporation of prior knowledge. In this paper, we propose a Bayesian pliable lasso that extends this ap…

This is a wrong leaning.
The chart is technically correct — but misleading.
GDP per capita ≠ income.
It ignores cost of living, inequality, and social safety nets.
Mississippi isn’t “richer” than Germany in any meaningful way.
https://noc.social/@todayilearned/11493…

Today I learned (@todayilearned@noc.social)
Attached: 1 image TIL that America's poorest state (Mississippi) has a higher GDP per capita than UK, France, and Italy https://themarketear.com/posts/cgERhgr0OS/image/0 #til #todayilearned https://www.reddit.com/r/todayilearned/comments/1mc38r3/til_that_americas_poorest_state_mississippi_has_a/

"What’s clear is that when we share music — whether at backyard barbecues, holiday gatherings, or road trips with the radio blaring — we’re doing something deeply meaningful. Our brains are literally synchronizing, creating shared neural patterns across generations and differences. In those moments when everyone finds themselves humming the same chorus or tapping to the same beat, we’re experiencing one of life’s simplest yet most profound connections."
When Your Favorite Song Plays, Your Brain 'Physically Embodies' Music
https://studyfinds.org/brain-cells-synchronize-to-music/

Finite-dimensional approximations of generalized squeezing
Sahel Ashhab, Felix Fischer, Davide Lonigro, Daniel Braak, Daniel Burgarth
https://arxiv.org/abs/2508.09041 https://…

Finite-dimensional approximations of generalized squeezing
We show unexpected behaviour in simulations of generalized squeezing performed with finite-dimensional truncations of the Fock space: even for extremely large dimension of the state space, the results depend on whether the truncation dimension is even or odd. This situation raises the question whether the simulation results are physically meaningful. We demonstrate that, in fact, the two truncation schemes correspond to two well-defined, distinct unitary evolutions whose generators are defined …

On the Geometric Accuracy of Implicit and Primitive-based Representations Derived from View Rendering Constraints
Elias De Smijter, Renaud Detry, Christophe De Vleeschouwer
https://arxiv.org/abs/2509.10241

On the Geometric Accuracy of Implicit and Primitive-based Representations Derived from View Rendering Constraints
We present the first systematic comparison of implicit and explicit Novel View Synthesis methods for space-based 3D object reconstruction, evaluating the role of appearance embeddings. While embeddings improve photometric fidelity by modeling lighting variation, we show they do not translate into meaningful gains in geometric accuracy - a critical requirement for space robotics applications. Using the SPEED+ dataset, we compare K-Planes, Gaussian Splatting, and Convex Splatting, and demonstrate…

Speculative politics
As an anarchist (okay, maybe not in practice), I'm tired of hearing why we have to suffer X and Y indignity to "preserve the rule of law" or "maintain Democratic norms." So here's an example of what representative democracy (a form of government that I believe is inherently flawed) could look like if its proponents had even an ounce of imagination, and/or weren't actively trying to rig it to favor a rich donor class:
1. Unicameral legislature, where representatives pass laws directly. Each state elects 3 statewide representatives: the three most-popular candidates in a statewide race where each person votes for one candidate (ranked preference voting would be even better but might not be necessary, and is not a solution by itself). Instead of each representative getting one vote in the chamber, they get N votes, where N is the number of people who voted for them. This means that in a close race, instead of the winner getting all the power, the power is split. Having 3 representatives trades off between leisure size and ensuring that two parties can't dominate together.
2. Any individual citizen can contact their local election office to switch or withdraw their vote at any time (maybe with a 3-day delay or something). Voting power of representatives can thus shift even without an election. They are limited to choosing one of the three elected representatives, or "none of the above." If the "none of the above" fraction exceeds 20% of eligible voters, a new election is triggered for that state. If turnout is less than 80%, a second election happens immediately, with results being final even at lower turnout until 6 months later (some better mechanism for turnout management might be needed).
3. All elections allow mail-in ballots, and in-person voting happens Sunday-Tuesday with the Monday being a mandatory holiday. (Yes, election integrity is not better in this system and that's a big weakness.)
4. Separate nationwide elections elect three positions for head-of-state: one with diplomatic/administrative powers, another with military powers, and a third with veto power. For each position, the top three candidates serve together, with only the first-place winner having actual power until vote switches or withdrawals change who that is. Once one of these heads loses their first-place status, they cannot get it again until another election, even if voters switch preferences back (to avoid dithering). An election for one of these positions is triggered when 20% have withdrawn their votes, or if all three people initially elected have been disqualified by losing their lead in the vote count.
5. Laws that involve spending money are packaged with specific taxes to pay for them, and may only be paid for by those specific revenues. Each tax may be opted into or out of by each taxpayer; where possible opting out of the tax also opts you out of the service. (I'm well aware of a lot of the drawbacks of this, but also feel like they'd not necessarily be worse than the drawbacks of our current system.) A small mandatory tax would cover election expenses.
6. I'm running out of attention, but similar multi-winner elections could elect panels of judges from which a subset is chosen randomly to preside in each case.
Now I'll point out once again that this system, in not directly confronting capitalism, racism, patriarchy, etc., is probably doomed to the same failures as our current system. But if you profess to want a "representative democracy" as opposed to something more libratory, I hope you'll at least advocate for something like this that actually includes meaningful representation as opposed to the current US system that's engineered to quash it.
Key questions: "Why should we have winner-take-all elections when winners-take-proportionately-to-votes is right there?" and "Why should elected officials get to ignore their constituents' approval except during elections, when vote-withdrawal or -switching is possible?"
2/2
#Democracy

Real-world Music Plagiarism Detection With Music Segment Transcription System
Seonghyeon Go
https://arxiv.org/abs/2509.08282 https://arxiv.org/pdf/2509.082…

Real-world Music Plagiarism Detection With Music Segment Transcription System
As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is increasing to safeguard individual music copyrights. In this work, we propose a system for detecting music plagiarism by combining various MIR technologies. We developed a music segment transcription system that extracts musically meaningful segments from audio recordin…

Every time I manage to not buy an item on amazon and use a small online seller is a tiny but meaningful victory.

Beyond Technocratic XAI: The Who, What & How in Explanation Design
Ruchira Dhar, Stephanie Brandl, Ninell Oldenburg, Anders S{\o}gaard
https://arxiv.org/abs/2508.09231 https…

Beyond Technocratic XAI: The Who, What & How in Explanation Design
The field of Explainable AI (XAI) offers a wide range of techniques for making complex models interpretable. Yet, in practice, generating meaningful explanations is a context-dependent task that requires intentional design choices to ensure accessibility and transparency. This paper reframes explanation as a situated design process -- an approach particularly relevant for practitioners involved in building and deploying explainable systems. Drawing on prior research and principles from design t…

Micro-Health Interventions: Exploring Design Strategies for 1-Minute Interventions as a Gateway to Healthy Habits
Zahra Hassanzadeh, David Haag, Lydia Chilton, Jan Smeddinck, Norman Farb, Joseph Jay Williams
https://arxiv.org/abs/2508.09312

Micro-Health Interventions: Exploring Design Strategies for 1-Minute Interventions as a Gateway to Healthy Habits
One-minute behavior change interventions might seem too brief to matter. Could something so short really help people build healthier routines? This work explores this question through two studies examining how ultra-brief prompts might encourage meaningful actions in daily life. In a formative study, we explored how participants engaged with one-minute prompts across four domains: physical activity, eating, screen use, and mental well-being. This revealed two common design approaches: Immediate…

Meaningful Data Erasure in the Presence of Dependencies
Vishal Chakraborty, Youri Kaminsky, Sharad Mehrotra, Felix Naumann, Faisal Nawab, Primal Pappachan, Mohammad Sadoghi, Nalini Venkatasubramanian
https://arxiv.org/abs/2507.00343

Meaningful Data Erasure in the Presence of Dependencies
Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechani…

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
Niranjana Arun Menon, Iqra Farooq, Yulong Li, Sara Ahmed, Yutong Xie, Muhammad Awais, Imran Razzak
https://arxiv.org/abs/2508.07127

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availability of genomic and electrophysiological data, extracting biologically meaningful insights from such high-dimensional, noisy, and sparsely annotated datasets remains a non-trivial task. Recently, LLMs has been applied effectively to predict structural variations in biological sequences. In this work, we explore the po…

X-ray transferable polyrepresentation learning
Weronika Hryniewska-Guzik, Przemyslaw Biecek
https://arxiv.org/abs/2507.06264 https://…

X-ray transferable polyrepresentation learning
The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Central to this challenge is the quality of data representation. However, the ability to generalize and extract these features effectively from unseen datasets is also crucial. In light of this, we introduce a novel concept: the polyrepresentation. Polyrepresentation integrates multiple representations of the same modality…

OpenCoderRank: AI-Driven Technical Assessments Made Easy
Hridoy Sankar Dutta, Sana Ansari, Swati Kumari, Shounak Ravi Bhalerao
https://arxiv.org/abs/2509.06774 https://

OpenCoderRank: AI-Driven Technical Assessments Made Easy
Organizations and educational institutions use time-bound assessment tasks to evaluate coding and problem-solving skills. These assessments measure not only the correctness of the solutions, but also their efficiency. Problem setters (educator/interviewer) are responsible for crafting these challenges, carefully balancing difficulty and relevance to create meaningful evaluation experiences. Conversely, problem solvers (student/interviewee) apply coding efficiency and logical thinking to arrive …

Great article from NYT about sheikh Mansour bin Zayed al-Nahyan from the UAE. For me it illustrates the bigger problem of the middle-east/gulfstates, the absence of any meaningful democracy, too rich oligarchs with too much power. Politically the people are living in medieval times there.

Conquering Soccer and Arming Warlords
The Emirati vice president is best known as the owner of Manchester City, a top English soccer team. Behind the scenes, he has been described as the “handler” guiding his country’s secret foreign wars.

QB Tiers are revealed, plus an incredible catch https://www.nytimes.com/athletic/6545954/2025/08/11/nfl-qb-tiers-lamar-jackson-jayden-daniels-the-pulse/

DTECT: Dynamic Topic Explorer & Context Tracker
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.07910 https://arxiv…

DTECT: Dynamic Topic Explorer & Context Tracker
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines that lack robust support for interpretation and user-friendly exploration. We introduce DTECT (Dynamic Topic Explorer & Context Tracker), an end-to-end system that bridges the gap between raw textual data and meaningful temporal insights. DTECT provides a unified workflow that sup…

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Ziliang Shen, Caixing Wang, Shaoli Wang, Yibo Yan
https://arxiv.org/abs/2508.05212

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive personal information. Differential privacy provides a rigorous framework for safeguarding individual privacy while enabling meaningful statistical analysis. In this paper, we propose a differentially private quantile regression method for high-dimensional data in a distributed setting. Quantile regression is a powerful an…

Emergence of Entropic Time in a Tabletop Wheeler-DeWitt Universe
Giovanni Barontini
https://arxiv.org/abs/2509.07745 https://arxiv.org/pdf/2509.07745

Emergence of Entropic Time in a Tabletop Wheeler-DeWitt Universe
We implement an analogue Wheeler-DeWitt mini-universe constituted by a well-isolated atomic Bose-Einstein condensate in a time-independent conservative potential. In exact analogy with the Wheeler-DeWitt framework for the actual universe, our system has the fundamental problem of defining from within a meaningful time variable over which to order the events. Here, we partition the mini-universe into a bright and a dark sector, enabling entropy exchange between them through a potential barrier. …

Quantum Walks for Chemical Reaction Networks
Seenivasan Hariharan, Sebastian Zur, Sachin Kinge, Lucas Visscher, Kareljan Schoutens, Stacey Jeffery
https://arxiv.org/abs/2509.07890

Quantum Walks for Chemical Reaction Networks
We lay the foundation for a quantum algorithmic framework to analyse fixed-structure chemical reaction networks (CRNs) using quantum random walks (QRWs) via electrical circuit theory. We model perturbations to CRNs, such as, species injections that shift steady-state concentrations, while keeping the underlying species-reaction graph fixed. Under physically meaningful mass-action constraints, we develop quantum algorithms that (i) decide reachability of target species after perturbation, (ii) s…

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu, Aditya Bhat, Rick Cory, Eric Cousineau, Hongkai Dai, Ching-Hsin Fang, Kunimatsu Hashimoto, Muhammad Zubair Irshad, Masha Itkina, Naveen Kuppuswamy, Kuan-Hui Lee, Katherine Liu, Dale McConachie, Ian McMahon, Haruki Nishimura, Calder Phillips-Grafflin, Charles Richter, Paarth Shah, Krishnan Srinivasan, Blake Wulfe, Chen Xu, Mengchao Zhang, Alex Alspach, Maya …

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently, scaling data and model size has led to the development of capable language and vision foundation models, motivating large-scale efforts to create general-purpose robot foundation models. While these models have garnered significant enthusiasm and investment, meaningful evaluation of real-world performance remains a …

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
He Wang, Liang Zeng
https://arxiv.org/abs/2508.03661

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
Computational scientific discovery increasingly relies on algorithms to process complex data and identify meaningful patterns - yet faces persistent challenges in gravitational-wave signal identification. While existing algorithmic approaches like matched filtering (MF) and deep neural networks (DNNs) have achieved partial success, their limitations directly stem from fundamental limitations: MF's excessive computational demands arise from its reliance on predefined theoretical waveform templat…

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

A Robot That Listens: Enhancing Self-Disclosure and Engagement Through Sentiment-based Backchannels and Active Listening
Hieu Tran, Go-Eum Cha, Sooyeon Jeong
https://arxiv.org/abs/2509.07873

A Robot That Listens: Enhancing Self-Disclosure and Engagement Through Sentiment-based Backchannels and Active Listening
As social robots get more deeply integrated intoour everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots' communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy,and forming positive rapport and relationships with people.Thus, we developed an L…

Aaron Rodgers dismisses Jets in New York return, tosses four touchdowns in Steelers debut https://www.nfl.com/news/aaron-rodgers-dismisses-jets-in-new-york-return-tosses-four-touchdowns-in-steelers-debut

From Passive to Participatory: How Liberating Structures Can Revolutionize Our Conferences
Daniel Russo, Margaret-Anne Storey
https://arxiv.org/abs/2509.07046 https://

From Passive to Participatory: How Liberating Structures Can Revolutionize Our Conferences
Our conferences face a growing crisis: an overwhelming flood of submissions, increased reviewing burdens, and diminished opportunities for meaningful engagement. With AI making paper generation easier than ever, we must ask whether the current model fosters real innovation or simply incentivizes more publications. This article advocates for a shift from passive paper presentations to interactive, participatory formats. We propose Liberating Structures, facilitation techniques that promote colla…

Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
Anandi Dutta, Shivani Mruthyunjaya, Jessica Saddington, Kazi Sifatul Islam
https://arxiv.org/abs/2509.04456

Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
The emergence of large language models (LLMs) has unlocked boundless possibilities, along with significant challenges. In response, we developed a mental health support chatbot designed to augment professional healthcare, with a strong emphasis on safe and meaningful application. Our approach involved rigorous evaluation, covering accuracy, empathy, trustworthiness, privacy, and bias. We employed a retrieval-augmented generation (RAG) framework, integrated prompt engineering, and fine-tuned a p…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel
https://arxiv.org/abs/2507.22020 https://…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we propose a novel point-shifting mechanism to introduce perturbations in point cloud data. Recently, AI has seen an exponential growth. Hence, it is important to understand the decision-making process of AI algorithms when they are applied in critical areas. Our work focuses on explaining AI algorithms that class…

T2UE: Generating Unlearnable Examples from Text Descriptions
Xingjun Ma, Hanxun Huang, Tianwei Song, Ye Sun, Yifeng Gao, Yu-Gang Jiang
https://arxiv.org/abs/2508.03091 https://

T2UE: Generating Unlearnable Examples from Text Descriptions
Large-scale pre-training frameworks like CLIP have revolutionized multimodal learning, but their reliance on web-scraped datasets, frequently containing private user data, raises serious concerns about misuse. Unlearnable Examples (UEs) have emerged as a promising countermeasure against unauthorized model training, employing carefully crafted unlearnable noise to disrupt the learning of meaningful representations from protected data. Current approaches typically generate UEs by jointly optimizi…

AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models
Chinnappa Guggilla, Budhaditya Roy, Trupti Ramdas Chavan, Abdul Rahman, Edward Bowen
https://arxiv.org/abs/2507.05157

AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models
Large Language Models (LLMs) possess an extraordinary capability to produce text that is not only coherent and contextually relevant but also strikingly similar to human writing. They adapt to various styles and genres, producing content that is both grammatically correct and semantically meaningful. Recently, LLMs have been misused to create highly realistic phishing emails, spread fake news, generate code to automate cyber crime, and write fraudulent scientific articles. Additionally, in many…

Social Dynamics of DAOs: Power, Onboarding, and Inclusivity
Victoria Kozlova, Ben Biedermann
https://arxiv.org/abs/2509.06163 https://arxiv.org/pdf/2509.06…

Social Dynamics of DAOs: Power, Onboarding, and Inclusivity
This report explores the often-overlooked cultural and social dynamics shaping participation and power in DAOs. Drawing on qualitative interviews and ethnographic observations, it shows how factors such as financial privilege, informal gatekeeping, visibility bias, and onboarding structures create barriers to meaningful inclusion. While DAOs are frequently framed as permissionless and egalitarian, the lived experiences of contributors reveal a more complex reality, one in which soft power and i…

Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism
Andreas Mayer
https://arxiv.org/abs/2507.05187 https://

Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism
The proliferation of AI-driven systems presents a fundamental challenge to Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW), often diminishing user agency and failing to account for value pluralism. Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability. This leaves users and communities unable to challenge or shape the values embedded in the systems that govern their digital lives,…

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Ariel Basso Madjoukeng, J\'er\^ome Fink, Pierre Poitier, Edith Belise Kenmogne, Benoit Frenay
https://arxiv.org/abs/2509.05188

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing negative pairs (different from the positive pairs) apart. In SLR, in a sign video, only certain parts provide information that is truly useful for its r…

Differential Attention for Multimodal Crisis Event Analysis
Nusrat Munia, Junfeng Zhu, Olfa Nasraoui, Abdullah-Al-Zubaer Imran
https://arxiv.org/abs/2507.05165

Differential Attention for Multimodal Crisis Event Analysis
Social networks can be a valuable source of information during crisis events. In particular, users can post a stream of multimodal data that can be critical for real-time humanitarian response. However, effectively extracting meaningful information from this large and noisy data stream and effectively integrating heterogeneous data remains a formidable challenge. In this work, we explore vision language models (VLMs) and advanced fusion strategies to enhance the classification of crisis data in…

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Xinzhu Li, Juepeng Zheng, Yikun Chen, Xudong Mao, Guanghui Yue, Wei Zhou, Chenlei Lv, Ruomei Wang, Fan Zhou, Baoquan Zhao
https://arxiv.org/abs/2508.03397

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait r…