Preface-Special Issue on Misinformation on the Web

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Volumetric representations are popular in inverse rendering because they have a simple parameterization, are smoothly varying, and transparently handle topology changes. However, incorporating the full volumetric transport of light is costly and challengin ...

In this paper we introduce and study the STREAMINGCYCLES problem, a random order streaming version of the Boolean Hidden Hypermatching problem that has been instrumental in streaming lower bounds over the past decade. In this problem the edges of a graph G ...

Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning. We propose that n-grams composed of random character sequences, or garble, ...

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 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 ...

Cars are some of the most security-critical consumer devices. On the one hand, owners expect rich infotainment features, including audio, hands-free calls, contact management, or navigation through their connected mobile phone. On the other hand, the infot ...

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also pose ...

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operato ...

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 ...

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...

The relation between brain functional activity and the underlying structure is complex and varies depending on the specific brain region. Recently, we used graph signal processing to introduce the structural-decoupling index (SDI), a novel metric quantifyi ...

Preface-Special Issue on Misinformation on the Web

Chattez avec Graph Search

Unbiased Inverse Volume Rendering with Differential Trackers

Factorial Lower Bounds for (Almost) Random Order Streams

Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

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

PROFACTORY: Improving IoT Security via Formalized Protocol Customization

On the Insecurity of Vehicles Against Protocol-Level Bluetooth Threats

Differentiable Signed Distance Function Rendering

Bona Fide Riesz Projections For Density Estimation

Recurrent Neural Networks Are Universal Approximators With Stochastic Inputs

Synthetic Data - Anonymisation Groundhog Day

Brain structure-function coupling is unique to individuals across multiple frequency bands: a graph signal processing study