Lmst

Inverse Parentheses

https://kellett.im/a/inverse-parentheses

#HackerNews #InverseParentheses #InverseProblem #CodingChallenge #HackerNews #TechTrends #Programming

Back to the wonderful CIRM (my last time here was in 2018), in Marseille, France for the workshop on "Blind Inverse Problems in Imaging", organized by Luca Calatroni, Emmanuel Soubies, and Pierre Weiss. I'm eager to attend the seminars tomorrow. More information here: https://conferences.cirm-math.fr/3349.html #Imaging #InverseProblem

New publication https://doi.org/10.1103/PhysRevB.111.205143

New algorithm for the #inverseproblem of Kohn-Sham #densityfunctionaltheory (#dft), i.e. to find the #potential from the #density.

Outcome of a fun collaboration of @herbst with the group of Andre Laestadius at #oslomet to derive first mathematical error bounds for this problem

#condensedmatter #planewave #numericalanalysis #convexanalysis #dftk

@iscompsensing Applications for the ISCS 2025 Workshop are now open! https://www.iscs2025.com

2-3 June 2025, Luxembourg

This is an interesting event for PhD students and researchers since this workshop (organized before the ISCS symposium) actually refers to a 2-day summer school with 4 key international researchers (more info in the link) in the field of #optimization #inverseproblem solving, #opticalimaging and #radar systems.

Topics covered by this workshop will be about: Proximal Neural Networks; Wider Horizons in Optical Imaging with the Mesolens; and 2 Hands-on Tutorials: Track 1: DeepInverse: A Pytorch Library For Solving Imaging Inverse Problems With Deep Learning; Track 2: Build Your Own Radar with MIT’s Next-Generation Kit

The 2025 International Symposium of Computational Sensing (#iscs2025) will be held in Clervaux, Luxembourg, from 4-6 June 2025. Preceding the symposium, a 2-day workshop featuring lectures and hands-on tutorials will be held at the University of Luxembourg from June 2-3, 2025.

Let's start designing a new course for applied mathematics students in #UCLouvain, #EPL on high dimensional data analysis with 3 wonderful reference books #inverseproblem #highDimensional #statistics #optimization #Sparsity #teaching

Picture of the 3 books I'm going to use for this course:

Vershynin, R. (2018). High-dimensional probability: An introduction with applications in data science (Vol. 47). Cambridge university press.

Wright, J., & Ma, Y. (2022). High-dimensional data analysis with low-dimensional models: Principles, computation, and applications. Cambridge University Press.

Wright, S. J., & Recht, B. (2022). Optimization for data analysis. Cambridge University Press.

(boosting appreciated) The deadline for the postdoc opening described below has been extended to September 30th, 2024.

We have one postdoc opening in my research group, the Image and Signal Processing Group (ISPGroup), Applied Mathematics department, UCLouvain, Belgium, to work on the QuadSense project, a new project funded by the Belgian Fund for Scientific Research - FNRS. Topics: #InverseProblem #RankOneProjection #Imaging #RadioInterferometry #ComputationalSensing More information about this project and conditions:

https://laurentjacques.gitlab.io/project/quadsense/

@jbaert cool #InverseProblem

(boosting appreciated) We have one postdoc opening in my research group, the Image and Signal Processing Group (ISPGroup), Applied Mathematics department, UCLouvain, Belgium, to work on the QuadSense project, a new project funded by the Belgian Fund for Scientific Research - FNRS. Topics: #InverseProblem #RankOneProjection #Imaging #RadioInterferometry #ComputationalSensing More information about this project and conditions: https://laurentjacques.gitlab.io/project/quadsense/

First day of “Deep learning, image analysis, inverse problems, and optimization (DIPOpt)” was very fruitful. Three talks by Martin Storath, Odyssée Merveille, and Xiaohao Cai. In addition, I had the chance to present a poster on Electron Ptychography.
#ptychography #microscopy #electronmicroscop #phaseretrieval #InverseProblem

(Boosting is welcomed*) I'd like to find a #peertube server (instance?) accepting general #scientific #research videos, coming from international #workshops #conferences, or remote #seminars I made. My main research fields are #appliedmathematics, #inverseproblem, #optics, and #machinelearning. I don't have the possibility to create one server myself but I'm ready to support one. So far, I've parsed many possibilities but either they are closed and do not accept any new users, or I'm off topic with respect to their content and objectives. Any advice?

*: as I believe it could interest others in similar contexts

From Julián Tachella @JulianTachella, posted on "Chi":

📰""Learning to reconstruct signals from binary measurements alone"📰

We present theory + a #selfsupervised approach for learning to reconstruct incomplete (!) and binary (!) measurements using the binary data itself. See the first figure and its alt-text.

https://arxiv.org/abs/2303.08691
with @lowrankjack
---

The theory characterizes

- the best approximation of a set of signals from incomplete binary observations
- its sample complexity
- complements existing theory for signal recovery from binary measurements

See the third figure and its alt-text.
---

The proposed self-supervised algorithm obtains performances on par with supervised learning and outperforms standard reconstruction techniques (such as binary iterative hard thresholding)

See the second figure and its alt-text.

---

Code based on the deepinverse library is available at https://github.com/tachella/ssbm

Check out the paper for more details!

#SelfSupervisedLearning #CompressiveSensing #Quantization #InverseProblem #1bitcamera

On this figure, a description of a self-supervised method for learning an image reconstruction algorithm (taking as input its binary observations) when one has access only to binary observations of many images belonging to the same, low complexity signal set. The figure shows a sensing device on the left acquiring numerous binary observations that are feeding either a linear inversion method on a top branch (such as a pseudo inverse associated with the sensing model before binarisation) with poor image reconstruction quality shown on the top right of the figure, or, on a bottom branch, a neural network model learned from binary observations alone, by promoting consistency with the binary sensing model (more information in the paper). This second branch achieves higher quality in the estimated images, as shown on the bottom right of the figure.

On this figure, a table of images with 5 rows and 10 columns showing 10 images of the FashionMnist dataset (on the last row) reconstructed from their binary observations for 4 different algorithms, one per row, for 10 images of the dataset (such as a shoe, a shirt, ...). The 4 algorithms are: linear inversion, binary iterative hard thresholding (or BIHT, proposed in one bit compressive sensing), the proposed method using self-supervision, and a supervised method having access to the initial images to learn the reconstruction method. The proposed approach provides better quality than BIHT with a wavelet prior and the linear approach, and it is close to the image quality of the supervised method.

Slides and abstract of my talk "Interferometric single-pixel imaging with a multicore fiber" made at ISCS23 (Luxembourg, June 12-14, 2023) now available at https://laurentjacques.gitlab.io/event/interferometric-single-pixel-imaging-with-a-multicore-fiber/ and https://laurentjacques.gitlab.io/publication/interferometric-single-pixel-imaging-with-a-multicore-fiber/ All this is based on the work of Olivier Leblanc. Long journal preprint version "Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations" on https://arxiv.org /abs/2306.12698 in colaboration with S. Sivankutty, M. Hofer, H. Rigneault, #imaging #lensless #compressivesensing #computationalimaging #speckle #compressedsensing #InverseProblem (toot generated thanks to https://tootpick.org directly from my website)

#Introduction The Image and Signal Processing Group (UCLouvain, ICTEAM, Belgium) is active in theories, models, techniques, codes, datasets, and codecs related to signal and image processing, for intance in #SignalAcquisition, #Compression and #Streaming; #MachineLearning and #DeepLearning for #ComputerVision; #ObjectTracking; #DataRetrieval; #Biomedical #SignalProcessing and #Medical #ImageProcessing; and #CompressiveSensing #InverseProblem, #ComputationalImaging

https://ispgroup.gitlab.io

RT Laurent Demanet @laurentdemanet

"Two postdoctoral openings in the general area of computational mathematics, machine learning, and geophysical inverse problems! https://math.mit.edu/icg/openings/ " #postdoc #machinelearning #geophysics #InverseProblem

Currently enjoying attending the Mathematical Models for Plug-and-play Image Restoration in Paris. Very interesting introductory tutorial talks by Samuel Hurault and Regev Cohen on PnP and RED in common framework built on potentials, convergence analysis and improvements #Workshop #PnP #InverseProblem #Denoiser https://gdr-mia.math.cnrs.fr/events/pnpworkshop/

@gabrielpeyre This is indeed a key experiment by Alan V. Oppenheim. Following his results, I realized with T. Feuillen, a few years ago, that this is also true in complex compressive sensing with Gaussian matrices. Paper, slides, demo, and video here:
https://laurentjacques.gitlab.io/publication/the-importance-of-phase-in-complex-compressive-sensing/
https://laurentjacques.gitlab.io/event/paris-saclay-the-importance-of-phase-in-complex-compressive-sensing/

#CompressiveSensing #Signal #Phase #Fourier #InverseProblem

#InverseProblem

Client Info