Tootfinder

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…

Modeling Human Spatial Mobility Patterns with the L\'evy Flight Cluster Model
Malcolm Wolff, Adrian Dobra, Anton H. Westveld, Grace S. Chiu
https://arxiv.org/abs/2509.00298 …

Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model
Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the Lévy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic…

A human EEG dataset for multisensory perception and mental imagery #YOTO (You Only Think Once)

A Human EEG Dataset for Multisensory Perception and Mental Imagery - Scientific Data
The YOTO (You Only Think Once) dataset presents a human electroencephalog- raphy (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 26 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to in…

Human activity is choking Oman's fragile desert rivers, scientists warn #Oman

Human activity is choking Oman's fragile desert rivers, scientists warn
Ephemeral desert rivers known as wadis—lifelines for biodiversity and water in some of the world's driest landscapes—are being dangerously constricted by human activity, new research has found.

Go witheFlow: Real-time Emotion Driven Audio Effects Modulation
Edmund Dervakos, Spyridon Kantarelis, Vassilis Lyberatos, Jason Liartis, Giorgos Stamou
https://arxiv.org/abs/2510.02171

Go witheFlow: Real-time Emotion Driven Audio Effects Modulation
Music performance is a distinctly human activity, intrinsically linked to the performer's ability to convey, evoke, or express emotion. Machines cannot perform music in the human sense; they can produce, reproduce, execute, or synthesize music, but they lack the capacity for affective or emotional experience. As such, music performance is an ideal candidate through which to explore aspects of collaboration between humans and machines. In this paper, we introduce the witheFlow system, designed t…

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…

"We can conclude that the brain has the capacity to distinguish virtual infectious patterns,
become activated,
and link this activation to a downstream response,
⭐️ resulting in systemic immunity,"
wrote Camilla Jandus, co-author of the study paper
https://www.

Human immune system reacts to the mere sight of infection
Researchers monitored brain activity when people were shown images of sick-looking avatars via VR. The brain appeared to spark an immune response.

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…

Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
Changde Du, Yizhuo Lu, Zhongyu Huang, Yi Sun, Zisen Zhou, Shaozheng Qin, Huiguang He
https://arxiv.org/abs/2509.24298

Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
The ability to represent emotion plays a significant role in human cognition and social interaction, yet the high-dimensional geometry of this affective space and its neural underpinnings remain debated. A key challenge, the `behavior-neural gap,' is the limited ability of human self-reports to predict brain activity. Here we test the hypothesis that this gap arises from the constraints of traditional rating scales and that large-scale similarity judgments can more faithfully capture the brain'…

Rotational migration in human pancreatic ductal organoids depends on actin and myosin activity
Gengqiang Xie, Chaity Modak, Olalekan H Usman, Raphael WF Tan, Nicole Coca, Gabriela De Jesus, Yue Julia Wang, D. Thirumalai, Xin Li, Jerome Irianto
https://arxiv.org/abs/2509.25680

Rotational migration in human pancreatic ductal organoids depends on actin and myosin activity
Rotational migration is one specific form of collective cell migration when epithelial cells are confined in a spherical geometry, such as in the epithelial acini. This tissue-level rotation motion is crucial for the morphogenesis of multiple epithelial systems. Here, we introduce a new primary human model for the study of rotational migration, pancreatic ductal organoids. Live imaging revealed the persistent rotation of the organoids over time. By tracking the nuclei, the three- dimensional tr…

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
Margarita Loupa, Antonios Argyriou, Yanwei Liu
https://arxiv.org/abs/2507.20657 htt…

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
A subset of Human Activity Classification (HAC) systems are based on AI algorithms that use passively collected wireless signals. This paper presents the micro-Doppler attack targeting HAC from wireless orthogonal frequency division multiplexing (OFDM) signals. The attack is executed by inserting artificial variations in a transmitted OFDM waveform to alter its micro-Doppler signature when it reflects off a human target. We investigate two variants of our scheme that manipulate the waveform at …

🎯 Smartwatches allow researchers to identify human activity with 78% accuracy
#smartwatch

Smartwatches allow researchers to identify human activity with 78% accuracy
Researchers have long been able to use information from smartwatches to identify physical movement, such as sitting or walking, that wearers are performing in a controlled lab setting.

Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
Shanjid Hasan Nishat, Srabonti Deb, Mohiuddin Ahmed
https://arxiv.org/abs/2509.02511

Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
Fitness movement recognition, a focused subdomain of human activity recognition (HAR), plays a vital role in health monitoring, rehabilitation, and personalized fitness training by enabling automated exercise classification from video data. However, many existing deep learning approaches rely on computationally intensive 3D models, limiting their feasibility in real-time or resource-constrained settings. In this paper, we present a lightweight and effective framework that integrates pre-trained…

Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
Farah Baracat, Giacomo Indiveri, Elisa Donati
https://arxiv.org/abs/2507.23474 https://

Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike trains from individual motor neurons, extracted from high-density EMG. These biologically grounded signals drive a spiking neural network …

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 …

Distinct weak asymmetric interactions shape human brain functions as probability fluxes
Yoshiaki Horiike, Shin Fujishiro
https://arxiv.org/abs/2508.20961 https://

Distinct weak asymmetric interactions shape human brain functions as probability fluxes
The functional computation of the human brain arises from the collective behaviour of the underlying neural network. The emerging technology enables the recording of population activity in neurons, and the theory of neural networks is expected to explain and extract functional computations from the data. Thermodynamically, a large proportion of the whole-body energy is consumed by the brain, and functional computation of the human brain seems to involve high energy consumption. The human brain,…

Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device Few-Shot Learning
Pixi Kang, Julian Moosmann, Mengxi Liu, Bo Zhou, Michele Magno, Paul Lukowicz, Sizhen Bian
https://arxiv.org/abs/2508.15413

Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device Few-Shot Learning
Human Activity Recognition (HAR) using wearable devices has advanced significantly in recent years, yet its generalization remains limited when models are deployed to new users. This degradation in performance is primarily due to user-induced concept drift (UICD), highlighting the importance of efficient personalization. In this paper, we present a hybrid framework that first generalizes across users and then rapidly adapts to individual users using few-shot learning directly on-device. By upda…

RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online
Dong Yoon Lee, Alyssa Weakley, Hui Wei, Blake Brown, Keyana Carrion, Shijia Pan
https://arxiv.org/abs/2508.21167

RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online
One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and act…

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
Kazushi Kato, Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
https://arxiv.org/abs/2507.23298

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
In human dialogue, nonverbal information such as nodding and facial expressions is as crucial as verbal information, and spoken dialogue systems are also expected to express such nonverbal behaviors. We focus on nodding, which is critical in an attentive listening system, and propose a model that predicts both its timing and type in real time. The proposed model builds on the voice activity projection (VAP) model, which predicts voice activity from both listener and speaker audio. We extend it …

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Wenqi Zheng, Yutaka Arakawa
https://arxiv.org/abs/2508.10371 https://

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Reinforcement learning in large reasoning models enables learning from feedback on their outputs, making it particularly valuable in scenarios where fine-tuning data is limited. However, its application in multi-modal human activity recognition (HAR) domains remains largely underexplored. Our work extends reinforcement learning to the human activity recognition domain with multimodal large language models. By incorporating visual reinforcement learning in the training process, the model's gener…

Data-Driven Discrete Geofence Design Using Binary Quadratic Programming
Keisuke Otaki, Akihisa Okada, Tadayoshi Matsumori, Hiroaki Yoshida
https://arxiv.org/abs/2509.24679 https…

Data-Driven Discrete Geofence Design Using Binary Quadratic Programming
Geofences have attracted significant attention in the design of spatial and virtual regions for managing and engaging spatiotemporal events. By using geofences to monitor human activity across their boundaries, content providers can create spatially triggered events that include notifications about points of interest within a geofence by pushing spatial information to the devices of users. Traditionally, geofences were hand-crafted by providers. In addition to the hand-crafted approach, recent …

Der #Ganges trocknet laut einer neuen Studie so stark aus wie seit 1.300 Jahren nicht mehr.
Die Flussmenge ist heute um 76 Prozent geringer als beim schlimmsten historischen #Dürreereignis im 16. Jahrhundert. Hauptursache ist menschliches Handeln, darunter ein geschwächter Sommer-

The Ganges River is drying at an unprecedented rate, new study finds
The Ganges River is in crisis. This lifeline for around 600 million people in India and neighboring countries is experiencing its worst drying period in 1,300 years. Using a combination of historical data, paleoclimate records and hydrological models, researchers from IIT Gandhinagar and the University of Arizona discovered that human activity is the main cause. They also found that the current drying is more severe than any recorded drought in the river's history.

Human Activity Recognition Based on Electrocardiogram Data Only
Sina Montazeri, Waltenegus Dargie, Yunhe Feng, Kewei Sha
https://arxiv.org/abs/2509.19328 https://

Human Activity Recognition Based on Electrocardiogram Data Only
Human activity recognition is critical for applications such as early intervention and health analytics. Traditional activity recognition relies on inertial measurement units (IMUs), which are resource intensive and require calibration. Although electrocardiogram (ECG)-based methods have been explored, these have typically served as supplements to IMUs or have been limited to broad categorical classification such as fall detection or active vs. inactive in daily activities. In this paper, we ad…

A new wave of climate research is sounding a stark warning:
Human activity may be driving drought more intensely
– and more directly
– than previously understood.
The southwestern United States has been in a historic #megadrought for much of the past two decades,
with its reservoirs including lakes Mead and Powell dipping to record lows
and legal disputes erupting ov…

Climate models reveal how human activity may be locking the Southwest into permanent drought
The drought has been linked to the Pacific Decadal Oscillation, a natural climate pattern. A new study finds global warming is now influencing that natural phenomenon.

🌵 Climate models reveal how human activity may be locking the Southwest into permanent drought
https://theconversation.com/climate-models-reveal-how-human-activity-may-be-locking-the-southwest-into-permanent-drou…

Climate models reveal how human activity may be locking the Southwest into permanent drought
The drought has been linked to the Pacific Decadal Oscillation, a natural climate pattern. A new study finds global warming is now influencing that natural phenomenon.

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
https://arxiv.org/abs/2507.21018

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress. Unlike general human activity recognition, rehabilitation motion assessment focuses on analyzing the quality of movement within the same action class, requiring the detection of subtle deviations from ideal motion. Recent advances in deep learning and video-based skeleton extraction have opened new possibilities for accessible, scalable motion assessment …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Weiyu Luo, Chenfeng Xiong
https://arxiv.org/abs/2508.02734 …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the In…

Exploring Similarity between Neural and LLM Trajectories in Language Processing
Xin Xiao, Kaiwen Wei, Jiang Zhong, Dongshuo Yin, Yu Tian, Xuekai Wei, Mingliang Zhou
https://arxiv.org/abs/2509.24307

Exploring Similarity between Neural and LLM Trajectories in Language Processing
Understanding the similarity between large language models (LLMs) and human brain activity is crucial for advancing both AI and cognitive neuroscience. In this study, we provide a multilinguistic, large-scale assessment of this similarity by systematically comparing 16 publicly available pretrained LLMs with human brain responses during natural language processing tasks in both English and Chinese. Specifically, we use ridge regression to assess the representational similarity between LLM embed…

Deep learning-based control of electrically evoked activity in human visual cortex https://www.biorxiv.org/content/10.1101/2025.09.24.678361v1 (Toward) closed-loop for tuning stimulation parameters; Utah array,

Scaling and Distilling Transformer Models for sEMG
Nicholas Mehlman, Jean-Christophe Gagnon-Audet, Michael Shvartsman, Kelvin Niu, Alexander H. Miller, Shagun Sodhani
https://arxiv.org/abs/2507.22094

Scaling and Distilling Transformer Models for sEMG
Surface electromyography (sEMG) signals offer a promising avenue for developing innovative human-computer interfaces by providing insights into muscular activity. However, the limited volume of training data and computational constraints during deployment have restricted the investigation of scaling up the model size for solving sEMG tasks. In this paper, we demonstrate that vanilla transformer models can be effectively scaled up on sEMG data and yield improved cross-user performance up to 110M…

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
Zuofu Huang, Yingling Fan, James Hodges, Julian Wolfson
https://arxiv.org/abs/2507.17900

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
The widespread collection of data from mobile and wearable devices has created unprecedented opportunities to study human behavior in fine temporal resolution. One common structure for such data is categorical sequences: ordered, multinomial observations across many time points. These sequences present unique statistical challenges due to their high dimensionality and complex temporal dependence, including both short- and long-term correlations. Yet, there has been relatively little methodologi…

Characterizing Human Limb Movements Using An In-House Multi-Channel Non-Invasive Surface-EMG System
Vinay C K, Vikas Vazhayil, Madhav rao
https://arxiv.org/abs/2509.13840 https:…

Characterizing Human Limb Movements Using An In-House Multi-Channel Non-Invasive Surface-EMG System
Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and neuromuscular disorders. Conventional methods involve placement of invasive electrodes within the muscles to record EMG signals. The goal is to showcase the usage of surface based EMG signals to characterize all possible human limb movements. An in-house non-invasive EM…

#Culture #Nationalism #Fascism #Anarchism

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Zinan Zheng, Yang Liu, Jia Li
https://arxiv.org/abs/2509.20911 https://

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Graph neural networks have shown promising results in weather forecasting, which is critical for human activity such as agriculture planning and extreme weather preparation. However, most studies focus on finite and local areas for training, overlooking the influence of broader areas and limiting their ability to generalize effectively. Thus, in this work, we study global weather forecasting that is irregularly distributed and dynamically varying in practice, requiring the model to generalize t…

On The Road - To Xi’An/ Departure 🔜
在路上 - 去西安/ 离 🔜
📷 Pentax MX
🎞️Kodak Double-X 5222
#filmphotography #Photography #blackandwhite

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi, Michele Rossi
https://arxiv.org/abs/2509.23303

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model re…

Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Jingjing Wang, Pengyu Liang, Tingyu Wang, Ming Li, Yanmei Cui, Siwei Liu, Xin Huang, Xiang Li, Minghui Zhang, Yunshi Zeng, Zhu Cao, Jiekang Feng, Qinghua Hu, Bingxian Luo, Bing Cao
https://arxiv.org/abs/2508.06892…

Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Solar activity drives space weather, affecting Earth's magnetosphere and technological infrastructure, which makes accurate solar flare forecasting critical. Current space weather models under-utilize multi-modal solar data, lack iterative enhancement via expert knowledge, and rely heavily on human forecasters under the Observation-Orientation-Decision-Action (OODA) paradigm. Here we present the "Solar Activity AI Forecaster", a scalable dual data-model driven framework built on foundational mo…

$\gamma$-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Mishal Fatima, Shashank Agnihotri, Marius Bock, Kanchana Vaishnavi Gandikota, Kristof Van Laerhoven, Michael Moeller, Margret Keuper
https://arxiv.org/abs/2509.22448

$γ$-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Most pattern recognition models are developed on pre-proce\-ssed data. In computer vision, for instance, RGB images processed through image signal processing (ISP) pipelines designed to cater to human perception are the most frequent input to image analysis networks. However, many modern vision tasks operate without a human in the loop, raising the question of whether such pre-processing is optimal for automated analysis. Similarly, human activity recognition (HAR) on body-worn sensor data comm…

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
Maximilian Burzer, Tobias King, Till Riedel, Michael Beigl, Tobias R\"oddiger
https://arxiv.org/abs/2508.16604

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFl…

It's good to see The Economist taking the impact of climate change on the #AMOC seriously. It isn't a particularly good article - no info on likely timescales, no sources - but it is something that should worry us all. Here's Carbon Brief's recent piece:

Ocean current ‘collapse’ could trigger ‘profound cooling’ in northern Europe – even with global warming - Carbon Brief
A “collapse” of key Atlantic ocean currents would cause winter temperatures to plunge across northern Europe, overriding the warming driven by human activity.

GmSLM : Generative Marmoset Spoken Language Modeling
Talia Sternberg, Michael London, David Omer, Yossi Adi
https://arxiv.org/abs/2509.09198 https://arxiv.…

GmSLM : Generative Marmoset Spoken Language Modeling
Marmoset monkeys exhibit complex vocal communication, challenging the view that nonhuman primates vocal communication is entirely innate, and show similar features of human speech, such as vocal labeling of others and turn-taking. Studying their vocal communication offers a unique opportunity to link it with brain activity-especially given the difficulty of accessing the human brain in speech and language research. Since Marmosets communicate primarily through vocalizations, applying standard L…

Estrogen? Hot drinks will suffice!
"""
Naturally, cold water cooled. For that reason it was used in mania and frenzy, sicknesses of heat where the spirits were in ebullition, solids tightened and liquids were heated to the point of evaporation, leaving the brain of the patient ‘dry and brittle’, as anatomists regularly demonstrated. Reasonably enough Boissieu includes cold water among his list of refreshing cures: baths were the foremost ‘antiphlogistic’, purifying the body of any excessive igneous particles to be found there. Taken as a drink, it was a ‘dilutive procastinant’ that diminished the resistance of fluids to the action of solids, thereby indirectly lowering the general heat of the body.
But it was also said that cold water brought heat and that hot water cooled. Such at least was the thesis defended by Darut. Cold baths chased the blood from the periphery of the body and pushed it ‘with increased vigour towards the heart’. As the heart was the seat of natural heat, the blood was warmed there, all the more so as “the heart, which struggles alone against all the other parts, makes renewed efforts to expel the blood and overcome capillary resistance. What results is a greater intensity of circulation, the division of the blood, the fluidity of the humours, the destruction of congestions, an increase in the strength of the natural heat, of the appetite of the digestive forces, and the activity of the body and the mind.” A symmetrical paradox operated regarding hot baths: blood was attracted to the extremities of the body, as were the humours, sweat, and all forms of liquid, both beneficial and harmful. The vital centres were therefore deserted, the heart slowed and the organism thus began to cool down. This fact was confirmed by the ‘fainting, lipothymia… weakness, nonchalance, lassitude, and lack of vigour’ that generally accompanied excessive bathing with hot water.
But there was more. So great was the polyvalence of water, so great was its aptitude to submit itself to the qualities that it carried, that it sometimes lost its efficacy as a liquid and acted as a desiccant instead. Water could Prevent dampness. In part, this was the old principle of similia similibus, but in another sense, and by the intermediary of a visible mechanism. For some, it was cold water that brought dryness, as heat kept water humid. Heat dilated the pores of the organism, distended its membranes, and allowed humidity to impregnate them as a secondary effect. Liquids made their way through heat. For that reason, the hot drinks so widely used in the seventeenth century risked becoming a danger, and those who took too many risked relaxation, general dampness and a weakness of the whole organism. As these were traits commonly associated with the feminine body, as opposed to the dry, virile solidity of the male, the abuse of hot drinks could lead to a general feminisation of the human race: “Not without reason, the reproach is made to the majority of men that they have softened and degenerated, taking on the habits and inclinations of women – the only thing lacking is a physical resemblance. The abuse of humectants could accelerate the metamorphosis, and render the two sexes almost identical both physically and morally. Woe betide the human race if this prejudice ever spreads to the masses: there will be no more labourers, artisans or soldiers, as they will have lost the strength and vigour necessary for their profession.” [Pressavin]
"""
(Michel Foucault, History of Madness)

Removing motion artifacts from mechanomyographic signals: an innovative filtering method applied to human movement analysis
Matthieu Correa (CIAMS), Nicolas Vignais (UR2, M2S, ComBO), Isabelle A. Siegler (CIAMS), Maxime Projetti
https://arxiv.org/abs/2508.20602

Removing motion artifacts from mechanomyographic signals: an innovative filtering method applied to human movement analysis
Mechanomyography (MMG) is a promising tool for measuring muscle activity in the field but its sensitivity to motion artifacts limits its application. In this study, we proposed an adaptative filtering method for MMG accelerometers based on the complete ensemble empirical mode decomposition, with adaptative noise and spectral fuzzy entropy, to isolate motions artefacts from the MMG signal in dynamic conditions. We compared our method with the traditional band-pass filtering technique, demonstrat…

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Hao Chen, Fang Qiu, Li An, Douglas Stow, Eve Bohnett, Haitao Lyu, Shuang Tian
https://arxiv.org/abs/2508.15629

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Wildlife and human activities are key components of landscape systems. Understanding their spatial distribution is essential for evaluating human wildlife interactions and informing effective conservation planning. Multiperspective monitoring of wildlife and human activities by combining camera traps and drone imagery. Capturing the spatial patterns of their distributions, which allows the identification of the overlap of their activity zones and the assessment of the degree of human wildlife c…

Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
Doai Ngo, Mingxuan Sun, Zhengji Zhang, Ashwin G Ramayya, Mark Schnitzer, Zhe Zhao
https://arxiv.org/abs/2509.08831

Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
Large language models (LLMs) have demonstrated human-like abilities in language-based tasks. While language is a defining feature of human intelligence, it emerges from more fundamental neurophysical processes rather than constituting the basis of intelligence itself. In this work, we study the similarity between LLM internal states and human brain activity in a sensory-motor task rooted in anticipatory and visuospatial behavior. These abilities are essential for cognitive performance that cons…

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…

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 …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device ...
Pixi Kang, Julian Moosmann, Mengxi Liu, Bo Zhou, Michele Magno, Paul Lukowicz, Sizhen Bian

Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2509.15129

Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
With the IEEE 802.11bf Task Group introducing amendments to the WLAN standard for advanced sensing, interest in using Wi-Fi Channel State Information (CSI) for remote sensing has surged. Recent findings indicate that learning a unified three-dimensional motion representation through Doppler Radiance Fields (DoRFs) derived from CSI significantly improves the generalization capabilities of Wi-Fi-based human activity recognition (HAR). Despite this progress, CSI signals remain affected by asynchro…

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📷 Pentax MX
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BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu
https://arxiv.org/abs/2508.07106

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and desynchronise their activity in a highly structured manner, even when an individual is at rest. These functional connectivity dynamics may be related to behaviour and neuropsychiatric disease. To model these dynamics, temporal brain connectivity representations are essential, as they reflect evolving interactions betwe…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using P...
Nobuyuki Oishi, Philip Birch, Daniel Roggen, Paula Lago

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Mohammad Alikhani
https://arxiv.org/abs/2508.11186 https://arxiv.org/pdf/2508.1118…

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Human Activity Recognition (HAR) plays a critical role in numerous applications, including healthcare monitoring, fitness tracking, and smart environments. Traditional deep learning (DL) approaches, while effective, often require extensive parameter tuning and may lack interpretability. In this work, we investigate the use of a single three-axis accelerometer and the Kolmogorov--Arnold Network (KAN) for HAR tasks, leveraging its ability to model complex nonlinear relationships with improved int…

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…

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…

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…

Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Gianluca Gaglioti, Alessandra Cardinale, Cosimo Lupo, Thierry Nieus, Federico Marmoreo, Robin Gutzen, Michael Denker, Andrea Pigorini, Marcello Massimini, Simone Sarasso, Pier Stanislao Paolucci, Giulia De Bonis
https://

Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
The simulation of whole-brain dynamics should reproduce realistic spontaneous and evoked neural activity across different scales, including emergent rhythms, spatio-temporal activation patterns, and macroscale complexity. Once a mathematical model is selected, its configuration must be determined by properly setting its parameters. A critical preliminary step in this process is defining an appropriate set of observables to guide the selection of model configurations (parameter tuning), laying t…

Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications
Qiang Liu, Tiantian Wang, Binbin Nian, Feiyang Ma, Siqi Zhao, Andr\'es F. V\'asquez, Liping Guo, Chao Ding, Mehdi D. Davari
https://arxiv.org/abs/2508.03456

Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications
Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear m…

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…

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WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches
Taeyoung Yeon, Vasco Xu, Henry Hoffmann, Karan Ahuja
https://arxiv.org/abs/2509.04736 https://…

WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches
Despite advances in practical and multimodal fine-grained Human Activity Recognition (HAR), a system that runs entirely on smartwatches in unconstrained environments remains elusive. We present WatchHAR, an audio and inertial-based HAR system that operates fully on smartwatches, addressing privacy and latency issues associated with external data processing. By optimizing each component of the pipeline, WatchHAR achieves compounding performance gains. We introduce a novel architecture that unifi…

"The Amazon Rainforest Approaches a Point of No Return"
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The Amazon Rainforest Approaches a Point of No Return
In “Amazon Tipping Point” — Third-Place Winner of the 2025 Yale Environment 360 Film Contest — Brazilian filmmakers capture striking images of clear-cutting and explore how human activity is so damaging the world’s largest rainforest that it will not be able to recover.

Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
Behdad Khodabandehloo, Reza Rajimehr
https://arxiv.org/abs/2509.16973 …

Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
Deep learning has advanced fMRI analysis, yet it remains unclear which architectural inductive biases are most effective at capturing functional patterns in human brain activity. This issue is particularly important in small-sample settings, as most datasets fall into this category. We compare models with three major inductive biases in deep learning including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and Transformers for the task of biological sex classific…

RadHARSimulator V1: Model-Based FMCW Radar Human Activity Recognition Simulator
Weicheng Gao
https://arxiv.org/abs/2509.06751 https://arxiv.org/pdf/2509.06…

RadHARSimulator V1: Model-Based FMCW Radar Human Activity Recognition Simulator
Radar-based human activity recognition (HAR) is a pivotal research area for applications requiring non-invasive monitoring. However, the acquisition of diverse and high-fidelity radar datasets for robust algorithm development remains a significant challenge. To overcome this bottleneck, a model-based frequency-modulated continuous wave (FMCW) radar HAR simulator is developed. The simulator integrates an anthropometrically scaled $13$-scatterer kinematic model to simulate $12$ distinct activitie…

IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
Haozhe Zhou, Riku Arakawa, Yuvraj Agarwal, Mayank Goel
https://arxiv.org/abs/2508.01894

IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
IMUs are regularly used to sense human motion, recognize activities, and estimate full-body pose. Users are typically required to place sensors in predefined locations that are often dictated by common wearable form factors and the machine learning model's training process. Consequently, despite the increasing number of everyday devices equipped with IMUs, the limited adaptability has seriously constrained the user experience to only using a few well-explored device placements (e.g., wrist and …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contra...
Panpan Ji, Junni Song, Yifan Lu, Hang Xiao, Hanyu Liu, Chao Li

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Kartik Pandey, Arun Balasubramanian, Debasis Samanta

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…

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…

Insights from the Algonauts 2025 Winners
Paul S. Scotti, Mihir Tripathy
https://arxiv.org/abs/2508.10784 https://arxiv.org/pdf/2508.10784

Insights from the Algonauts 2025 Winners
The Algonauts 2025 Challenge just wrapped up a few weeks ago. It is a biennial challenge in computational neuroscience in which teams attempt to build models that predict human brain activity from carefully curated stimuli. Previous editions (2019, 2021, 2023) focused on still images and short videos; the 2025 edition, which concluded last month (late July), pushed the field further by using long, multimodal movies. Teams were tasked with predicting fMRI responses across 1,000 whole-brain parce…

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…

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Yifan Wang, Jingyuan Sun, Jichen Zheng, Yunhao Zhang, Chunyu Ye, Jixing Li, Chengqing Zong, Shaonan Wang
https://arxiv.org/abs/2508.04749

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language mod…