Tootfinder

PHASE: Passive Human Activity Simulation Evaluation
Steven Lamp, Jason D. Hiser, Anh Nguyen-Tuong, Jack W. Davidson
https://arxiv.org/abs/2507.13505 https:…

PHASE: Passive Human Activity Simulation Evaluation
Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE (Passive Human Activity Simulation Evaluation), a machine learning framework that analyzes Zeek connection logs and distinguishes human from non-human activity with over 90\% accuracy. PHASE operates entirely passively, relying on standard …

All the bird types who come and feed in my small bird sanctuary (aka garden) have fledglings right now. Many of them bring the little ones along to feed. This is a great opportunity for me to learn what the various birds look like at different ages.
I can't do much about the mess the human world is in; seeing to it that there is a safe space for these young birds to splash in a bird bath and eat good, tasty food is a better activity than just screaming into the void.

DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2507.12132 https://…

DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
Wi-Fi Channel State Information (CSI) has gained increasing interest for remote sensing applications. Recent studies show that Doppler velocity projections extracted from CSI can enable human activity recognition (HAR) that is robust to environmental changes and generalizes to new users. However, despite these advances, generalizability still remains insufficient for practical deployment. Inspired by neural radiance fields (NeRF), which learn a volumetric representation of a 3D scene from 2D im…

SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Lala Shakti Swarup Ray, Mengxi Liu, Deepika Gurung, Bo Zhou, Sungho Suh, Paul Lukowicz
https://arxiv.org/abs/2507.06405

SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction. Bio-impedance sensing offers unique advantages for fine-grained motion capture but remains underutilized due to the scarcity of labeled data. We introduce SImpHAR, a novel framework addressing this limitation through two core contributions. First, we propose a simulation pipeline that generates realistic bio-impedance signals from 3D human meshes using sho…

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2506.12997

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, whic…

Transcranial focused ultrasound for identifying the neural substrate of conscious perception https://arxiv.org/abs/2507.08517 Indeed I see long-term potential for phased-array tFUS as a visual prosthesis for the late-blind. The company Nudge is developing a phased-array tFUS helmet.

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
Timothy F Leslie, Hossein Amiri, Andreas Z\"ufle
https://arxiv.org/abs/2506.13985

Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19…

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey, Daniel Geissler, Paul Lukowicz
https://arxiv.org/abs/2507.07949

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHA…

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
Stefan Buijsman, Sarah Carter, Juan Pablo Berm\'udez
https://arxiv.org/abs/2506.23952

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy -- the capacity for self-governed action within defined realms of skill or expertise -- remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make inform…

Avoid the word "pedestrian." You marginalize a basic human activity when you give it a four-syllable Greek-derived name.
Say walk, on foot, foot traffic whenever you can.

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Sam Frank Kelemen, Joaqu\'in G\~oni, S\'ergio Pequito, Arian Ashourvan
https://arxiv.org/abs/2507.06136

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the physical substrate for functional connectivity (FC) -- the correlated neural activity between brain areas. While these structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways, recurrent netwo…

Multiscale phase oscillations induced by cluster synchronisation in human connectome core network
Bosiljka Tadic, Marija Mitrovic Dankulov, Roderick Melnik
https://arxiv.org/abs/2507.07583

Multiscale phase oscillations induced by cluster synchronisation in human connectome core network
Brain imaging data mapping onto human connectome networks enables the investigation of global brain dynamics, where the brain hubs play an essential role in transferring activity between different brain parts. At this scale, the synchronisation processes are increasingly investigated as one of the key mechanisms revealing many aspects of brain functional coherence in healthy brains and revealing deviations due to various brain disorders. For the human connectome core network, consisting of the …

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Boshra Khajehpiri, Eric Granger, Massimiliano de Zambotti, Fiona C. Baker, Mohamad Forouzanfar
https://arxiv.org/abs/2506.00279

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. To address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed t…

My Personal Privacy - Your resource for achieving better online privacy
Why privacy:
The right to privacy is a well-established, fundamental human right. It is necessary for a safe and just society. It is especially important in regions where intolerance and discrimination are backed by autocratic regimes. While you may not feel the need for privacy in all of your online activity, you should be able to achieve a high level of privacy when you need it. This website and the document…

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery https://arxiv.org/abs/2506.06898

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a chal…

Fedi meta-musings.
Just went to look at a Mastodon account I interacted with a little while back. Their follow requests require approval (which is fair enough) and the bio states
Got a blank or nonsensical avatar, no visible activity, no pointers to your identity? I'll ignore your follow request.
Well I guess that's me out. I have a good reason for wanting to be pseudonymous, as do many others here I imagine.
Of course it's every user's right to set whatever conditions they want on who follows them, but a blanket refusal on anyone not featuring a "real name" and a human-appearing avatar feels quite over-sensitive to me.
#fediverse #mastodon

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher
Tomas Gallo Aquino, Victoria Liu, Habiba Azab, Raissa Mathura, Andrew J Watrous, Eleonora Bartoli, Benjamin Y Hayden, Paul Sajda, Sameer A Sheth, Nuttida Rungratsameetaweemana
https://arxiv.org/abs/2506.20834

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher
Transfer learning enhances the training of novel sensory and decision models by employing rich feature representations from large, pre-trained teacher models. Cognitive neuroscience shows that the human brain creates low-dimensional, abstract representations for efficient sensorimotor coding. Importantly, the brain can learn these representations with significantly fewer data points and less computational power than artificial models require. We introduce Brain2Model Transfer Learning (B2M), a …

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Panpan Ji, Junni Song, Hang Xiao, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02826

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Sensor-based Human Activity Recognition (HAR) is a core technology that enables intelligent systems to perceive and interact with their environment. However, multimodal HAR systems still encounter key challenges, such as difficulties in cross-modal feature alignment and imbalanced modality contributions. To address these issues, we propose a novel framework called the Dynamic Contrastive Dual-Path Network (DCDP-HAR). The framework comprises three key components. First, a dual-path feature extra…

HabSim: Architecture for modelling disruptions, propagation, detection and repair in deep space habitats
Luca Vaccino, Alana K. Lund, Shirley J. Dyke, Mohsen Azimi, Ethan Vallerga
https://arxiv.org/abs/2506.08903

HabSim: Architecture for modelling disruptions, propagation, detection and repair in deep space habitats
Establishing long-term human settlements in deep space presents significant challenges. Harsh environmental conditions, such as extreme temperature fluctuations, micrometeorite impacts, seismic activity, and exposure to solar and cosmic radiation pose obstacles to the design and operation of habitat systems. Prolonged mission duration and the vast distances from Earth introduce further complications in the form of delayed communication and limited resources, making autonomy especially desirable…

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
https://arxiv.org/abs/2507.05532 …

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specif…

How urban scaling and resource distribution shape social welfare and migration dynamics
Bryce Morsky
https://arxiv.org/abs/2506.03384 https://

How urban scaling and resource distribution shape social welfare and migration dynamics
Many outputs of cities scale in universal ways, including infrastructure, crime, and economic activity. Through a mathematical model, this study investigates the interplay between such scaling laws in human organization and governmental allocations of resources, focusing on impacts to migration patterns and social welfare. We find that if superlinear scaling resources of cities -- such as economic and social activity -- are the primary drivers of city dwellers' utility, then cities tend to conv…

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Daniel K. Freeman, Brian Odegaard, Seung-Schik Yoo, Matthias Michel
https://arxiv.org/abs/2507.08517

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
Philipp Lepold, Tobias R\"oddiger, Michael Beigl
https://arxiv.org/abs/2506.03219

HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
Research into human activity recognition (HAR) suffers from a lack of comprehensive, freely available field data. Commercial systems are rarely open source, offer little expandability, and have shortcomings in node synchronisation, data throughput, placement clarity, setup complexity, and cost. As a result, only a few intuitively placed sensors are often used, and field trials are generally reduced. HARNode addresses these obstacles as a fully open-source hardware and software platform for rapi…

Late emergence of pathological oscillatory activity in the retina of the Retinitis pigmentosa model RCS (Royal College of Surgeons) rat https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324345

Late emergence of pathological oscillatory activity in the retina of the Retinitis pigmentosa model RCS (Royal College of Surgeons) rat
Retinitis pigmentosa (RP) is a leading cause of blindness. The best studied models of human RP are the rd1 and rd10 mouse and the RCS rat (Royal College of Surgeons). In many models after degeneration of the photoreceptors, a pathological rhythmic activity of the retina as well as lowered efficiency of electrical stimulation were observed. In rd10 retina, both events were shown to be intimately linked. Surprisingly, to our knowledge no retinal oscillations have been reported in RCS retina. As o…

Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
Y. Suzuki, Y. Yukutake, T. Ohminato, M. Yamasaki, Ahyi Kim
https://arxiv.org/abs/2507.01260

Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
Precisely classifying earthquake types is crucial for elucidating the relationship between volcanic earthquakes and volcanic activity. However, traditional methods rely on subjective human judgment, which requires considerable time and effort. To address this issue, we developed a deep learning model using a transformer encoder for a more objective and efficient classification. Tested on Mount Asama's diverse seismic activity, our model achieved high F1 scores (0.930 for volcano tectonic, 0.931…

"David Attenborough’s ‘Ocean’ is a brutal, beautiful wake-up call from the sea"
#Oceans #Environment #Film #Media

David Attenborough's 'Ocean' exposes the unseen destruction of the seas
The new film “Ocean With David Attenborough” is both a breathtaking celebration of marine life and a sobering exposé of the industrial forces pushing the ocean toward collapse. With never-before-seen footage from mass coral bleaching to the violent destruction of bottom trawling, it reveals the hidden toll of human activity beneath the waves. But it’s also a story of hope that spotlights thriving marine reserves and the power of protection. Timed to World Oceans Day and the U.N. Ocean Co…

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
Ying Yu, Hang Xiao, Siyao Li, Jiarui Li, Haotian Tang, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02827

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensi…

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
Rub\'en Calvo, Carles Martorell, Adri\'an Roig, Miguel A. Mu\~noz
https://arxiv.org/abs/2506.03640

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
The idea that information-processing systems operate near criticality to enhance computational performance is supported by scaling signatures in brain activity. However, external signals raise the question of whether this behavior is intrinsic or input-driven. We show that autocorrelated inputs and temporal resolution influence observed scaling exponents in simple neural models. We also demonstrate analytically that under subsampling, non-critical systems driven by independent autocorrelated si…

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Weiheng Lu, Chunfeng Song, Jiamin Wu, Pengyu Zhu, Yuchen Zhou, Weijian Mai, Qihao Zheng, Wanli Ouyang
https://arxiv.org/abs/2506.18962

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fa…

Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
Guobin Shen, Dongcheng Zhao, Yiting Dong, Qian Zhang, Yi Zeng
https://arxiv.org/abs/2507.01966

Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
Artificial and biological systems may evolve similar computational solutions despite fundamental differences in architecture and learning mechanisms -- a form of convergent evolution. We demonstrate this phenomenon through large-scale analysis of alignment between human brain activity and internal representations of over 600 AI models spanning language and vision domains, from 1.33M to 72B parameters. Analyzing 60 million alignment measurements reveals that higher-performing models spontaneousl…