Improving Spatial Data Processing by Clipping Minimum Bounding Boxes

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

A well-known, previously only 1D, algorithm using the Sparse Representation of Signals and an iterative Block Coordinate Descent method (the SparSpec-1D algorithm) has been further developed and tested in a 2D spatial domain to obtain the toroidal and polo ...

The diffusion of energy technologies is key for supporting the energy transition. While social influence and peer effects have been extensively studied on the demand side and found to impact adoption, less focus has been given to the role of information ne ...

Episodic autobiographical memories are characterized by a spatial context and an affective component. But how do affective and spatial aspects interact? Does affect modulate the way we encode the spatial context of events? We investigated how one element o ...

Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually br ...

We present a loosely coupled scheme for the numerical simulation of the cardiac electrofluid-structure interaction problem, whose solution is typically computationally intensive due to the need to suitably treat the coupling of the different submodels. Our ...

How can one construct spatial histories and architectural theories from gestures, words and voices, bodies and minor threads? This research delves into oral and embodied architectural and spatial knowledge as modes of producing buildings, spaces and spati ...

In this article, we investigate the approximation ability of recurrent neural networks (RNNs) with stochastic inputs in state space model form. More explicitly, we prove that open dynamical systems with stochastic inputs can be well-approximated by a speci ...

Recent traffic flow prediction methods are lacking abilities to determine predictive features. Thus, they will propagate the error in the next timestamps. In this paper, first, we assess the role of spatial and temporal features on the traffic speed predic ...

As IoT applications gain widespread adoption, it becomes important to design and implement IoT protocols with security. Existing research in protocol security reveals that the majority of disclosed protocol vulnerabilities are caused by incorrectly impleme ...

Glacier shrinkage opens new proglacial terrain with pronounced environmental gradients along longitudinal and lateral chronosequences. Despite the environmental harshness of the streams that drain glacier forelands, their benthic biofilms can harbor astoni ...

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or ...

Social Engagement is a novel business model transforming final users of a service from passive into active components. In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward. This arises the compli ...

Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentia ...

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficienc ...

The ability to predict the spatial distribution of tree root system variables (e.g., the Root system Area (RA), the maximum root diameter, the number of roots in diameter classes, the density of fine roots, etc.) under different environmental conditions is ...

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physica ...

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture ...

Improving Spatial Data Processing by Clipping Minimum Bounding Boxes

Chat with Graph Search

The SparSpec algorithm and the application to the detection of spatial periodicities in tokamaks: from a 1D to a 2D analysis

Diffusion of energy technologies: the role and dynamics of supply-side information networks

Aesthetic experience enhances first-person spatial representation

Infrared-active phonons in one-dimensional materials and their spectroscopic signatures

A stable loosely-coupled scheme for cardiac electro-fluid-structure interaction

(from) the repertoire : an architectural theory of operations. Oral and embodied knowledge in architectural and spatial practices

Recurrent Neural Networks Are Universal Approximators With Stochastic Inputs

STATNet: Spatial-temporal attention in the traffic prediction

PROFACTORY: Improving IoT Security via Formalized Protocol Customization

Spatial patterns of benthic biofilm diversity among streams draining proglacial floodplains

FLEET: Online Federated Learning via Staleness Awareness and Performance Prediction

A chance-constraint approach for optimizing Social Engagement-based services

Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Investigating cognitive ability using action-based models of structural brain networks

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks

Tree root distribution modelling in different environmental conditions

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Monitoring Mesoscale to Submesoscale Processes in Large Lakes with Sentinel-1 SAR Imagery: The Case of Lake Geneva

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search