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Dynamic network discovery via infection tracing
Full text linkResearchers, policy makers, and engineers need to make sense of data from spreading processes as diverse as rumor spreading in social networks, viral infections, and water contamination. Classical questions include predicting infection behavior in a given network or deducing the network structure from infection data. Most of the research on network infections studies static graphs, that is, the connections in the network are assumed to not change. More recently, temporal graphs, in which connections change over time, have been used to more accurately represent real-world infections, which rarely occur in unchanging networks. We propose a model for temporal graph discovery that is consistent with previous work on static graphs and embraces the greater expressiveness of temporal graphs. For this model, we give algorithms and lower bounds which are often tight. We analyze different variations of the problem, which make our results widely applicable and it also clarifies which aspects of temporal infections make graph discovery easier or harder. We round off our analysis with an experimental evaluation of our algorithm on real-world interaction data from the Stanford Network Analysis Project and on temporal Erdős-Renyi graphs. On Erdős-Renyi graphs, we uncover a threshold behavior, which can be explained by a novel connectivity parameter that we introduce during our theoretical analysis
Draft new Recommendation ITU-T P.IXC: “Interactive test methods for subjective assessment of extended reality communications (for consent)”
No full textOrthogonal series for si- and related processes, Karhunen-Loève decompositions
Full text linkThis paper reproduces results from Chapter 11 of the forthcoming book. It discusses series expansions of processes with stationary increments (si-processes) and certain associated processes. Making use of de Branges theory of Hilbert spaces of entire functions, it sheds new light on the existing literature and makes available some new results. In particular, it provides some new decompositions of the Karhunen-Loève type
Statistical compressive sensing method for Hadamard-based single-pixel microscopy supported by kernel density estimators
Full text linkHadamard-based single-pixel microscopy (HSPM) is a versatile non-conventional imaging technique where a binary function base is projected over the sample in a microscope setup to recover its information. One HSPM’s main challenge is the need to project numerous patterns to retrieve the image of the object under study. This leads to potential phototoxicity damage and a reduction in temporal resolution. Aiming to reduce the total pattern projection time, this study explores the use of statistical compressive sensing (CS) using the kernel density estimator (KDE) approach to learn the probability distribution of the most relevant Hadamard spectrum (HS) sampling coefficients, based on a large-scale dataset of 50,000 histopathology images. The probability distribution can then be sampled to generate the set of Hadamard patterns to be projected. The proposed KDE-guided CS method is implemented and tested on biological and nonbiological samples. An image resolution of 550 lp/mm was recovered at a 25% sampling ratio (SR) using the proposed method, a level not reached by the well-established TV-based approach. Moreover, compared to TV-based sampling, the Michelson contrast increased from 0.09 to 0.17 at a 25% SR and from 0.12 to 0.29 at a 30% SR. These results demonstrate the feasibility of the proposed method for HSPM CS applications
Semidefinite hierarchies for diagonal unitary invariant bipartite quantum states
Full text linkWe investigate questions about the cone of separable bipartite states, consisting of the Hermitian matrices acting on that can be written as conic combinations of rank one matrices of the form with . Bipartite states that are not separable are said to be entangled. Detecting quantum entanglement is a fundamental task in quantum information and a hard computational problem. We explore the Doherty-Parrilo-Spedaglieri (DPS) hierarchy of semidefinite conic approximations for when the bipartite states have some additional structural properties: first, (i) for states with diagonal unitary invariance, and second (ii) for states with Bose symmetry. In case (i) we show that the DPS hierarchy can be block diagonalized, which, combining with its moment reformulation, leads to a substantially more efficient implementation. In case (ii), we give a characterization of the dual hierarchy, in terms of sums of squares of Hermitian complex polynomials, extending a known result in the generic case. It turns out that the completely positive cone , its dual cone , and their sums-of-squares based conic approximations , play a central role in these two settings (i),(ii). We clarify these connections and test the block diagonal relaxations on classes of examples
Inverse scattering for Schrödinger equation in the frequency domain via data-driven reduced order modeling
Full text linkIn this paper we develop a numerical method for solving an inverse scattering problem of estimating the scattering potential in a Schrödinger equation from frequency domain measurements based on reduced order models (ROM). The ROM is a projection of the Schrödinger operator onto a subspace spanned by its solution snapshots at certain wavenumbers. Provided the measurements are performed at these wavenumbers, the ROM can be constructed in a data-driven manner from the measurements on a surface surrounding the scatterers. Once the ROM is computed, the scattering potential can be estimated using nonlinear optimization that minimizes the ROM misfit. Such an approach typically outperforms the conventional methods based on data misfit minimization. We develop two variants of ROM-based algorithms for inverse scattering and test them on a synthetic example in two spatial dimensions
Bi-level optimization and implicit differentiation as a framework for optimal experimental design in tomography
No full textTotal Variation (TV) regularized reconstruction is one of the most relevant methods to improve the quality of limited-angle tomographic reconstructions. Nevertheless, the accuracy of computed tomography (CT) reconstructions with a limited number of measurements can be further improved by selecting the most informative acquisition angles. This optimal experimental design (OED) task can be formulated as a bi-level optimization problem, with selecting optimal angle combinations (experimental design parameter) on the upper-level and tomographic reconstruction on the lower-level. However, integrating TV regularized reconstruction into the bi-level optimization approach is non-trivial because of the large number of iterations required for the algorithm convergence, which impedes naive computation of gradients of the upper-level objective with respect to the experimental design parameter. In this work, we address this problem by employing implicit differentiation approach to calculate the upper-level objective gradient. Moreover, we utilize inexact methods to dynamically adjust the accuracy of the lower-level solver, refining the gradient calculation as needed. We demonstrate that this approach makes OED with TV regularized reconstruction applicable to realistic 3D data. Our numerical results demonstrate that the angles selected by our bi-level optimization framework significantly outperform the standard equidistant angular selection. The proposed approach is therefore effective in minimizing experimental time and radiation dose requirements for CT reconstruction of objects benefiting from TV regularization, and can be readily extended to other types of computationally demanding iterative reconstruction algorithms
Understanding AI disclosure needs for news production and journalism
Full text linkArtificial Intelligence (AI) is revolutionizing the way content is produced and integrated into journalistic workflows. The EU AI act’s Article 50 sets up transparency requirements aimed at encouraging the adoption and disclosure of AI in an ethical and responsible manner. In this study, we organized focus group interviews with Dutch citizens (N=21) to understand their expectations and needs regarding AI disclosures in the context of news production and journalism. These conversations are essential to understand if legal and regulatory policies are grounded in real-world experiences of citizens, and adequately address their concerns and enhance their digital interactions. We found that citizens predominantly favor disclosures of AI usage in journalistic content, in the form of (1) source references, (2) visual indicators (logos/watermarks) and (3) have varying preferences regarding information presentation and interaction modalities. Our findings highlight the need for interdisciplinary approaches to align standardization efforts with AI disclosures for news media
IXR ’25: 3rd International Workshop on Interactive eXtended Reality
Full text linkDespite remarkable advances, current Extended Reality (XR) applications are in their majority local and individual experiences. A plethora of interactive applications, such as teleconferencing, telesurgery, interconnection in new buildings project chain, cultural heritage, and museum contents communication, are well on their way to integrating immersive technologies. However, interconnected, and interactive XR, where participants can virtually interact across vast distances, remains a distant dream. In fact, three great barriers stand between current technology and remote immersive interactive life-like experiences, namely (i) content realism, (ii) motion-to-photon latency, and accurate (iii) human-centric quality assessment and control. Overcoming these barriers will require novel solutions at all elements of the end-to-end transmission chain. This workshop focuses on the challenges, applications, and major advancements in multimedia, networks, and end-user infrastructures to enable the next generation of interactive XR applications and services. The workshop proceedings can be found at: https://dl.acm.org/doi/proceedings/10.1145/374626
Multi-objective deep-learning-based biomechanical deformable image registration with MOREA
No full textWhen choosing a deformable image registration (DIR) approach for images with large deformations and content mismatch, the realism of found transformations often needs to be traded off against the required runtime. DIR approaches using deep learning (DL) techniques have shown remarkable promise in instantly predicting a transformation. However, on difficult registration problems, the realism of these transformations can fall short. DIR approaches using biomechanical, finite element modeling (FEM) techniques can find more realistic transformations, but tend to require much longer runtimes. This work proposes the first hybrid approach to combine them, with the aim of getting the best of both worlds. This hybrid approach, called DL-MOREA, combines a recently introduced multi-objective DL-based DIR approach which leverages the VoxelMorph framework, called DL-MODIR, with MOREA, an evolutionary algorithm-based, multi-objective DIR approach in which a FEM-like biomechanical mesh transformation model is used. In our proposed hybrid approach, the DL results are used to smartly initialize MOREA, with the aim of more efficiently optimizing its mesh transformation model. We empirically compare DL-MOREA against its components, DL-MODIR and MOREA, on CT scan pairs capturing large bladder filling differences of 15 cervical cancer patients. While MOREA requires a median runtime of 45 minutes, DL-MOREA can already find high-quality transformations after 5 minutes. Compared to the DL-MODIR transformations, the transformations found by DL-MOREA exhibit far less folding and improve or preserve the bladder contour distance error