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#cs

5 posts5 participants1 post today
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Sr. Estegosaurio 🦕

Hey fedi, I need some advice regarding managing identities online:

I am now studying CS, and as much as I dislike it, GitHub and LinkedIn "profesional" profiles have become a requerimient.

My conflict arises when that need clashes with not wanting to have PII on my profiles. But not wanting to segregate my contributions on two different accounts.

What do you folks do?

#askFedi#studying#uni
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Rxiv mechanobio

📰 "High order treatment of moving curved boundaries: Arbitrary-Lagrangian-Eulerian methods with a shifted boundary polynomials correction"
arxiv.org/abs/2504.15963 #Physics.Comp-Ph #Dynamics #Math.Na #Cs.Na #Cell

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arXiv.orgHigh order treatment of moving curved boundaries: Arbitrary-Lagrangian-Eulerian methods with a shifted boundary polynomials correctionIn this paper we present a novel approach for the prescription of high order boundary conditions when approximating the solution of the Euler equations for compressible gas dynamics on curved moving domains. When dealing with curved boundaries, the consistency of boundary conditions is a real challenge, and it becomes even more challenging in the context of moving domains discretized with high order Arbitrary-Lagrangian-Eulerian (ALE) schemes. The ALE formulation is particularly well-suited for handling moving and deforming domains, thus allowing for the simulation of complex fluid-structure interaction problems. However, if not properly treated, the imposition of boundary conditions can lead to significant errors in the numerical solution, which can spoil the high order discretization of the underlying mathematical model. In order to tackle this issue, we propose a new method based on the recently developed shifted boundary polynomial correction, which was originally proposed on fixed meshes. The new method is integrated into the space-time corrector step of a direct ALE finite volume method to account for the local curvature of the moving boundary by only exploiting the high order reconstruction polynomial of the finite volume control volume. It relies on a correction based on the extrapolated value of the cell polynomial evaluated at the true geometry, thus not requiring the explicit evaluation of high order Taylor series. This greatly simplifies the treatment of moving curved boundaries, as it allows for the use of standard simplicial meshes, which are much easier to generate and move than curvilinear ones, especially for 3D time-dependent problems. Several numerical experiments are presented demonstrating the high order convergence properties of the new method in the context of compressible flows in moving curved domains, which remain approximated by piecewise linear elements.
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amen zwa, esq.

I was an #EE undergrad, when I backed into #CS. This was the age when assembly was the JavaScript of the day and structured programming was the state-of-the-fart. So, it was a jolt, when I first encountered Test-Driven Development #TDD in the early 2000s, thanks to the luminaries like Kent Beck, et al. Brilliant stuff!

But, at the risk of being drawn and quartered, I do say that TDD is a bit of a misnomer and an overstatement. Honestly, answer this: do EEs really create the hardware test suits first, before conducting analysis and design, and do CSs really create the software test suits first, before performing analysis and design?

No, we do not! We analyse the problem, we study the requirements, we search for inspiration in the literature, we sip our tea or coffee, we select a candidate solution, we create a plausible design, we implement a prototype, then we test—yes, THEN, WE TEST—if our wild imaginations have any basis in reality at all.

So, I prefer a more down-to-earth description of TDD, which says, "test early, test often, test as much as practicable", not "test first, because".

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Petr Nuska

#AcademicJob

Research Fellow – Generative Audio AI

📍 Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK

Join the GenAI Hub to research generative models for audio and multimodal content. #PhD in #EE, #CS, #AI or related field required. Expertise in #AudioML, diffusion models, or related areas essential.

Deadline: 13/05/2025

jobs.surrey.ac.uk/021025

CC @academicjobs

Jobs at the University of SurreyJob Vacancy at the University of Surrey: Research Fellow in Generative Audio AIThe University of Surrey is a global community of ideas and people, dedicated to life-changing education and research.We are ambitious and have a bold vision of what we want to achieve - shaping ourselves into one of the best universities in the...
#GenerativeAI#AudioAI#MachineLearning
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Petr Nuska

#AcademicJob | #PhDStudentship

#PhD Position – Computational Music Perception (#AURA Project)

📍 Institute of Computational Perception, JKU Linz/Vienna

Work on real-time musical interaction, combining ML, #MIR, and embodied avatars. Strong background in #CS, #AI, or related fields; music knowledge is a plus. Supervision by Carlos Cancino-Chacón & Gerhard Widmer.

Deadline: 01/06/2025

jku.at/en/institute-of-computa

CC @academicjobs

Science Park Fassade Detailansicht
JKU - Johannes Kepler Universität LinzInstitute of Computational Perception
#MachineLearning#ComputationalMusicology#DigitalMusicology
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Rxiv mechanobio

📰 "Geometric Learning Dynamics"
arxiv.org/abs/2504.14728 #Dynamics #Quant-Ph #Q-Bio.Pe #Matrix #Cs.Lg

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arXiv.orgGeometric Learning DynamicsWe present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship $g \propto κ^a$ between the metric tensor $g$ in the space of trainable variables and the noise covariance matrix $κ$. The quantum regime corresponds to $a = 1$ and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to $a = \tfrac{1}{2}$ and describes very fast machine learning algorithms. The equilibration regime corresponds to $a = 0$ and describes classical models of biological evolution. We argue that the emergence of the intermediate regime $a = \tfrac{1}{2}$ is a key mechanism underlying the emergence of biological complexity.
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Rxiv mechanobio

📰 "Equilibrium Conserving Neural Operators for Super-Resolution Learning"
arxiv.org/abs/2504.13422 #Physics.Comp-Ph #Mechanics #Matrix #Cs.Lg

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arXiv.orgEquilibrium Conserving Neural Operators for Super-Resolution LearningNeural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-resolution training data. In this paper, we break this limitation; we introduce a framework for super-resolution learning in solid mechanics problems. Our approach allows one to train a high-resolution neural network using only low-resolution data. Our Equilibrium Conserving Operator (ECO) architecture embeds known physics directly into the network to make up for missing high-resolution information during training. We evaluate this ECO-based super-resolution framework that strongly enforces conservation-laws in the predicted solutions on two working examples: embedded pores in a homogenized matrix and randomly textured polycrystalline materials. ECO eliminates the reliance on high-fidelity data and reduces the upfront cost of data collection by two orders of magnitude, offering a robust pathway for resource-efficient surrogate modeling in materials modeling. ECO is readily generalizable to other physics-based problems.
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Rxiv mechanobio

📰 "The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map"
arxiv.org/abs/2504.13044 #Physics.Bio-Ph #Q-Bio.Qm #Dynamics #Cs.Lg #Cell

arXiv logo
arXiv.orgThe Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging MapWe propose a new theory for aging based on dynamical systems and provide a data-driven computational method to quantify the changes at the cellular level. We use ergodic theory to decompose the dynamics of changes during aging and show that aging is fundamentally a dissipative process within biological systems, akin to dynamical systems where dissipation occurs due to non-conservative forces. To quantify the dissipation dynamics, we employ a transformer-based machine learning algorithm to analyze gene expression data, incorporating age as a token to assess how age-related dissipation is reflected in the embedding space. By evaluating the dynamics of gene and age embeddings, we provide a cellular aging map (CAM) and identify patterns indicative of divergence in gene embedding space, nonlinear transitions, and entropy variations during aging for various tissues and cell types. Our results provide a novel perspective on aging as a dissipative process and introduce a computational framework that enables measuring age-related changes with molecular resolution.
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C.

@bignose @Natanox

Indeed! "Perfection is when there is nothing more to take away, rather than nothing more to add".

There's also a joke in software circles that every program contains at least one bug and can be optimized by at least one instruction -- and that therefore all software is reducible to a single instruction that doesn't work 😉

#CS#CompSci#SoftwareEngineering
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Rxiv mechanobio

📰 "Cellular Development Follows the Path of Minimum Action"
arxiv.org/abs/2504.08096 #Physics.Comp-Ph #Physics.Bio-Ph #Elasticity #Cs.Ai #Cell

arXiv.orgCellular Development Follows the Path of Minimum ActionCellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.
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