Where to go from here? Mobility prediction from instantaneous information

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Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

In our daily lives, our mobile phones sense our movements and interactions via a rich set of embedded sensors such as a GPS, Bluetooth, accelerometers, and microphones. This enables us to use mobile phones as agents for collecting spatio-temporal data. The ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Mobile communication - anytime, anywhere access to data and communication services - has been continuously increasing since the operation of the first wireless communication link by Guglielmo Marconi. The demand for higher data rates, despite the limited b ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

The Kamusi Project, a multilingual online dictionary website, has as one of its goals to document the lexicons of en-dangered and less-resourced languages (LRLs). Kamusi.org provides a unified platform and repository for this kind of data that is both simp ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

We analyze behavior patterns and photographic habits of the Nokia Mobile Data Challenge (NMDC) participants using GPS and timestamp data. We show that these patterns and habits can be used to estimate image appeal ratings of geotagged Flickr images. In ord ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we r ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. A place-of-interest is defined as a location where ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Mobility is a central aspect of our life; the locations we visit reflect our tastes and lifestyle and shape our social relationships. The ability to foresee the places a user will visit is therefore beneficial to numerous applications, ranging from forecas ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on hea ...

Where to go from here? Mobility prediction from instantaneous information

Chattez avec Graph Search

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Population Sensing Using Mobile Devices : a Statistical Opportunity or a Privacy Nightmare?

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Energy Efficient VLSI Circuits for MIMO-WLAN

Person Re-Identification and its Application to Multi-Object Tracking

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Small Languages, Big Data: Multilingual Computational Tools and Techniques for the Lexicography of Endangered Languages

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Rare is Interesting: Connecting Spatio-Temporal Behavior Patterns with Subjective Image Appeal

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

From big smartphone data to worldwide research: The Mobile Data Challenge

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Discovering places of interest in everyday life from smartphone data

Fair Decentralized Learning

Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Abstract IA02: Cancer-specific foundation models: Friend or foe in healthcare AI?