Tootfinder

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, Andreas Vlachos
https://arxiv.org/abs/2509.11804

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce \textsc{PledgeTracker}, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists…

Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
Timon Merk, Saeed Salehi, Richard M. Koehler, Qiming Cui, Maria Olaru, Amelia Hahn, Nicole R. Provenza, Simon Little, Reza Abbasi-Asl, Phil A. Starr, Wolf-Julian Neumann
https://arxiv.org/abs/2508.10160

Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
Neural decoding of pathological and physiological states can enable patient-individualized closed-loop neuromodulation therapy. Recent advances in pre-trained large-scale foundation models offer the potential for generalized state estimation without patient-individual training. Here we present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days. Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30…

Experimental study of turbulent mixing in a T-shaped mixer
Huixin Li, Mohammad Mehdi Zamani Asl, Bastian B\"auerlein, Kerstin Avila, Duo Xu, Marc Avila
https://arxiv.org/abs/2509.10264

Experimental study of turbulent mixing in a T-shaped mixer
One of the most widespread canonical devices for fluid mixing is the T-shaped mixer, in which two opposing miscible liquid streams meet at a junction and then mix along a main channel. Laminar steady and time-periodic flows in T-shaped mixers have been thoroughly studied, but turbulent flows have received much less scrutiny despite their prevalence in applications. We here introduce a novel experimental setup with a hydraulic diameter of four centimetres that enables the optical study of turbul…

Which Rope Breaks? A Study of Tension Distribution in Multi-Rope Systems
Amir Eskandari-asl, Roberto De Luca
https://arxiv.org/abs/2509.09973 https://arxiv…

Which Rope Breaks? A Study of Tension Distribution in Multi-Rope Systems
We investigate the tension distribution in systems of mass-less ropes under different loading conditions. For a two-rope system, we demonstrate how the breaking scenario depends on the applied force dynamics: rapid pulling causes the lower rope to break, while gradual pulling leads to upper rope failure. Extending to a three-rope Y-shaped configuration, we identify a critical angle theta_C=60° that determines which rope breaks first. When the angle between the upper ropes exceeds this critical…

The Visual Iconicity Challenge: Evaluating Vision-Language Models on Sign Language Form-Meaning Mapping
Onur Kele\c{s}, Asl{\i} \"Ozy\"urek, Gerardo Ortega, Kadir G\"okg\"o, Esam Ghaleb
https://arxiv.org/abs/2510.08482

The Visual Iconicity Challenge: Evaluating Vision-Language Models on Sign Language Form-Meaning Mapping
Iconicity, the resemblance between linguistic form and meaning, is pervasive in signed languages, offering a natural testbed for visual grounding. For vision-language models (VLMs), the challenge is to recover such essential mappings from dynamic human motion rather than static context. We introduce the \textit{Visual Iconicity Challenge}, a novel video-based benchmark that adapts psycholinguistic measures to evaluate VLMs on three tasks: (i) phonological sign-form prediction (e.g., handshape, …

@… I just came across this and thought I’d check with you to see if it’s accurate, instead of just shitposting it like I usually do with stuff.

Crosslisted article(s) found for physics.pop-ph. https://arxiv.org/list/physics.pop-ph/new
[1/1]:
- Which Rope Breaks? A Study of Tension Distribution in Multi-Rope Systems
Amir Eskandari-asl, Roberto De Luca

Low-Cost Open-Source Ambidextrous Robotic Hand with 23 Direct-Drive servos for American Sign Language Alphabet
Kelvin Daniel Gonzalez Amador
https://arxiv.org/abs/2509.03690 htt…

Low-Cost Open-Source Ambidextrous Robotic Hand with 23 Direct-Drive servos for American Sign Language Alphabet
Accessible communication through sign language is vital for deaf communities, 1 yet robotic solutions are often costly and limited. This study presents VulcanV3, a low- 2 cost, open-source, 3D-printed ambidextrous robotic hand capable of reproducing the full 3 American Sign Language (ASL) alphabet (52 signs for right- and left-hand configurations). 4 The system employs 23 direct-drive servo actuators for precise finger and wrist movements, 5 controlled by an Arduino Mega with dual PCA9685 modul…

Crosslisted article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Which Rope Breaks? A Study of Tension Distribution in Multi-Rope Systems
Amir Eskandari-asl, Roberto De Luca

CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series
Rahul Biswas, SuryaNarayana Sripada, Somabha Mukherjee, Reza Abbasi-Asl
https://arxiv.org/abs/2508.01920

CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series
Understanding how signals propagate through neural circuits is central to deciphering brain computation. While functional connectivity captures statistical associations, it does not reveal directionality or causal mechanisms. We introduce CITS (Causal Inference in Time Series), a non-parametric method for inferring statistically causal neural circuitry from high-resolution time series data. CITS models neural dynamics using a structural causal model with arbitrary Markov order and tests for tim…

CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Ehsan Yousefzadeh-Asl-Miandoab, Reza Karimzadeh, Bulat Ibragimov, Florina M. Ciorba, P{\i}nar T\"oz\"un
https://arxiv.org/abs/2508.19073

CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Studies conducted on enterprise-scale infrastructure have shown that GPUs -- the core computational resource for deep learning (DL) training -- are often significantly underutilized. DL task collocation on GPUs is an opportunity to address this challenge. However, it may result in (1) out-of-memory crashes for the subsequently arriving task and (2) slowdowns for all tasks sharing the GPU due to resource interference. The former challenge poses a threat to robustness, while the latter affects th…

Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design
Zhuoran Li, Hanieh Totonchi Asl, Ebrahim Nouri, Yifei Cai, Danella Zhao
https://arxiv.org/abs/2508.13357

Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design
Secure Multi-Party Computation (MPC) offers a practical foundation for privacy-preserving machine learning at the edge, with MPC commonly employed to support nonlinear operations. These MPC protocols fundamentally rely on Oblivious Transfer (OT), particularly Correlated OT (COT), to generate correlated randomness essential for secure computation. Although COT generation is efficient in conventional two-party settings with resource-rich participants, it becomes a critical bottleneck in real-worl…

Iterative Methods for Computing the Moore-Penrose Pseudoinverse of Quaternion Matrices, with Applications
Valentin Leplat, Salman Ahmadi-Asl, JunJun Pan, Ning Zheng
https://arxiv.org/abs/2508.16979

Iterative Methods for Computing the Moore-Penrose Pseudoinverse of Quaternion Matrices, with Applications
We develop iterative methods to compute the Moore-Penrose pseudoinverse of quaternion matrices directly in $\mathbb{H}$. We introduce a damped Newton-Schulz (NS) iteration, proving its convergence under spectral scaling $X_0=αA^H$ with $α\in(0,2/\|A\|_2^2)$. We derive higher-order hyperpower NS schemes with optimized factorizations. Beyond NS, we propose a randomized sketch-and-project method (RSP-Q) and a conjugate gradient on normal equations (CGNE-Q). Numerically, spectrally-scal…

SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning
Lanmiao Liu, Esam Ghaleb, Asl{\i} \"Ozy\"urek, Zerrin Yumak
https://arxiv.org/abs/2507.19359

SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning
Creating a virtual avatar with semantically coherent gestures that are aligned with speech is a challenging task. Existing gesture generation research mainly focused on generating rhythmic beat gestures, neglecting the semantic context of the gestures. In this paper, we propose a novel approach for semantic grounding in co-speech gesture generation that integrates semantic information at both fine-grained and global levels. Our approach starts with learning the motion prior through a vector-qua…

Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Hamed Jafarzadeh Asl, Mahsa Ghazvini Nejad, Amin Edraki, Masoud Asgharian, Vahid Partovi Nia
https://arxiv.org/abs/2507.22157

Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, voice-to-text, conversational agents, etc, where noise can severely degrade the performance. A modern application includes the voice assistant, specially mounted on Artificial Intelligence of Things (AIoT) devices such as cell phones, smart glasses, earbuds, etc, where the voice signal includes background noise. Therefo…

Crosslisted article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Yousefzadeh-Asl-Miandoab, Karimzadeh, Ibragimov, Ciorba, T\un

It's time to lower your inhibitions towards just asking a human the answer to your question.
In the early nineties, effectively before the internet, that's how you learned a lot of stuff. Your other option was to look it up in a book. I was a kid then, so I asked my parents a lot of questions.
Then by ~2000 or a little later, it started to feel almost rude to do this, because Google was now a thing, along with Wikipedia. "Let me Google that for you" became a joke website used to satirize the poor fool who would waste someone's time answering a random question. There were some upsides to this, as well as downsides. I'm not here to judge them.
At this point, Google doesn't work any more for answering random questions, let alone more serous ones. That era is over. If you don't believe it, try it yourself. Between Google intentionally making their results worse to show you more ads, the SEO cruft that already existed pre-LLMs, and the massive tsunami of SEO slop enabled by LLMs, trustworthy information is hard to find, and hard to distinguish from the slop. (I posted an example earlier: #AI #LLMs #DigitalCommons #AskAQuestion

Pass-efficient Randomized Algorithms for Low-rank Approximation of Quaternion Matrices
Salman Ahmadi-Asl, Malihe Nobakht Kooshkghazi, Valentin Leplat
https://arxiv.org/abs/2507.13731

Pass-efficient Randomized Algorithms for Low-rank Approximation of Quaternion Matrices
Randomized algorithms for low-rank approximation of quaternion matrices have gained increasing attention in recent years. However, existing methods overlook pass efficiency, the ability to limit the number of passes over the input matrix-which is critical in modern computing environments dominated by communication costs. We address this gap by proposing a suite of pass-efficient randomized algorithms that let users directly trade pass budget for approximation accuracy. Our contributions include…