Tootfinder

Physics-Informed Neural Networks for the Relativistic Burgers Equation in the Exterior of a Schwarzschild Black Hole
Shuyang Xiang
https://arxiv.org/abs/2506.00951

Physics-Informed Neural Networks for the Relativistic Burgers Equation in the Exterior of a Schwarzschild Black Hole
We introduce a Physics-Informed Neural Networks(PINN) to solve a relativistic Burgers equation in the exterior domain of a Schwarzschild black hole. Our main contribution is a PINN architecture that is able to simulate shock wave formations in such curved spacetime, by training a shock- aware network block and introducing a Godunov-inspired residuals in the loss function. We validate our method with numerical experiments with different kinds of initial conditions. We show its ability to reprodu…

Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
Mohammad Shah Alam, William Ott, Ilya Timofeyev
https://arxiv.org/abs/2505.24099

Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
In this paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sivashinsky (gKS) equation, an archetypal nonlinear PDE that exhibits spatiotemporal chaos. We introduce a novel methodology that integrates ESNs with transfer learning, aiming to enhance predictive performance across various parameter regimes of the gKS model. Our research focuses on predicting changes in long-term statistical patterns of the gKS model that result from varying the d…

This https://arxiv.org/abs/2501.00417 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

PureRank: A Parameter-Free Recursive Importance Measure for Network Nodes
Classical parameter-free centrality measures based on the recursive definition of importance (RDI), such as eigenvector centrality and Seeley centrality, are limited to strongly connected networks, while widely used methods like Katz centrality and PageRank rely on free parameters, such as the damping factor, to handle general networks. This motivates our central question: can an RDI-based importance (centrality) measure be defined for arbitrary networks without any tunable parameters? We answe…

This https://arxiv.org/abs/2308.13135 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recovery
We propose a nonparametric additive model for estimating interpretable value functions in reinforcement learning, with an application in optimizing postoperative recovery through personalized, adaptive recommendations. While reinforcement learning has achieved significant success in various domains, recent methods often rely on black-box approaches such as neural networks, which hinder the examination of individual feature contributions to a decision-making policy. Our novel method offers a fle…

A network of parametrically driven silicon nitride mechanical membranes
Luis Mestre, Suyash Singh, Gabriel Margiani, Letizia Catalini, Alexander Eichler, Vincent Dumont
https://arxiv.org/abs/2506.00850

A network of parametrically driven silicon nitride mechanical membranes
Networks of nonlinear resonators offer a promising platform for analog computing and the emulation of complex systems. However, realizing such networks remains challenging, as it requires resonators with high quality factors, individual frequency tunability, and strong inter-resonator coupling. In this work, we present a system that meets all these criteria. Our system is based on metallized silicon nitride membranes that are coupled via their common substrate and controlled capacitively via el…

Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation
Runjie Zhang, Kaiping Qu, Changhong Zhao, Wanjun Huang
https://arxiv.org/abs/2505.24677

Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation
The integration of intermittent renewable energy sources into distribution networks introduces significant uncertainties and fluctuations, challenging their operational security, stability, and efficiency. This paper considers robust distribution network reconfiguration (RDNR) with renewable generator resizing, modeled as a two-stage robust optimization (RO) problem with decision-dependent uncertainty (DDU). Our model optimizes resizing decisions as the upper bounds of renewable generator outpu…

B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining
Yimin Zhao, Weibo Wang, Xiong Wang, Linghe Kong, Jiadi Yu, Yifei Zhu, Shiyuan Li, Chong He, Guihai Chen
https://arxiv.org/abs/2505.24140

B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining
With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link reliability, as evidenced by the measurement results based on our real-world setup. In this paper, we propose a blind coherent combining d…

This https://arxiv.org/abs/2411.03502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Adaptive Shock Compensation in the Multi-layer Network of Global Food Production and Trade
Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our find…

Self-orthogonalizing attractor neural networks emerging from the free energy principle
Tamas Spisak, Karl Friston
https://arxiv.org/abs/2505.22749 https://…

Self-orthogonalizing attractor neural networks emerging from the free energy principle
Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding of neuronal computations and the design of artificial intelligence systems. Here we formalize how attractor networks emerge from the free energy principle applied to a universal partitioning of random dynamical systems. Our approach obviates the need for explicitly imposed learning and inference ru…

This https://arxiv.org/abs/2301.06136 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantitative Verification with Neural Networks
We present a data-driven approach to the quantitative verification of probabilistic programs and stochastic dynamical models. Our approach leverages neural networks to compute tight and sound bounds for the probability that a stochastic process hits a target condition within finite time. This problem subsumes a variety of quantitative verification questions, from the reachability and safety analysis of discrete-time stochastic dynamical models, to the study of assertion-violation and terminatio…

This https://arxiv.org/abs/2408.03431 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Compactifications of phylogenetic systems and species of electrical networks
We describe new spaces and maps. Our graphical map is a visual and numerical correspondence between spaces of circular electrical networks and circular planar split systems. When restricted to the planar circular electrical case, this graphical map finds the split system uniquely associated with the Kalmanson resistance distance of the dual network, matching the induced split system familiar from phylogenetics. This correspondence is extended to compactifications of the respective spaces, takin…

Hear our @…, interviewed by @… , explain in The Brake podcast the motivation and results of our recent paper on highways and their significance to urban planning, with a "lovely" Italian accent 😄👌

Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation
Junyong Shin, Eunsung Jeon, Inhyoung Kim, Yo-Seb Jeon
https://arxiv.org/abs/2505.23198

Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation
To achieve higher throughput in next-generation Wi-Fi systems, a station (STA) needs to efficiently compress channel state information (CSI) and feed it back to an access point (AP). In this paper, we propose a novel deep learning (DL)-based CSI feedback framework tailored for next-generation Wi-Fi systems. Our framework incorporates a pair of encoder and decoder neural networks to compress and reconstruct the angle parameters of the CSI. To enable an efficient finite-bit representation of the …

Synaptic shot-noise triggers fast and slow global oscillations in balanced neural networks
Denis S. Goldobin, Maria V. Ageeva, Matteo di Volo, Ferdinand Tixidre, Alessandro Torcini
https://arxiv.org/abs/2505.22373

Synaptic shot-noise triggers fast and slow global oscillations in balanced neural networks
Neural dynamics is determined by the transmission of discrete synaptic pulses (synaptic shot-noise) among neurons. However, the neural responses are usually obtained within the diffusion approximation modeling synaptic inputs as continuous Gaussian noise. Here, we present a rigorous mean-field theory that encompasses synaptic shot-noise for sparse balanced inhibitory neural networks driven by an excitatory drive. Our theory predicts new dynamical regimes, in agreement with numerical simulations…

Trade Networks and the Rise of a Global Currency
Tomoo Kikuchi, Lien Pham
https://arxiv.org/abs/2505.22080 https://arxiv.org/pdf/2505…

Trade Networks and the Rise of a Global Currency
We develop a model of strategic competition between global currencies. Issuers choose their commitment levels for currency internationalization, while users -- interconnected in a trade network -- choose their usage of each currency for trade settlement. Our theoretical findings highlight not only the advantage of the status-quo issuer in maintaining dominance, but also the conditions under which an emerging issuer can attract users, potentially leading to a multi-currency payment system. The n…

This https://arxiv.org/abs/2501.00417 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

PureRank: A Parameter-Free Recursive Importance Measure for Network Nodes
Classical parameter-free centrality measures based on the recursive definition of importance (RDI), such as eigenvector centrality and Seeley centrality, are limited to strongly connected networks, while widely used methods like Katz centrality and PageRank rely on free parameters, such as the damping factor, to handle general networks. This motivates our central question: can an RDI-based importance (centrality) measure be defined for arbitrary networks without any tunable parameters? We answe…

I never guessed that our paper on non-state and philanthropic financing networks of girls' and women's education would come out amidst global ODA cuts, data obfuscation, and mentioning 🚺 in scientific publications would be flagged.
I, Ivan Matovich, Robin Shields, Yogesh Jadhav analyse the financing and implementation networks of girls' and women's education in Asia in the last issue of Comparative Education Review (68:4).

EPBench: A Benchmark for Short-term Earthquake Prediction with Neural Networks
Zhiyu Xu, Qingliang Chen
https://arxiv.org/abs/2505.15588 https://

EPBench: A Benchmark for Short-term Earthquake Prediction with Neural Networks
Since the beginning of this century, the significant advancements in artificial intelligence and neural networks have offered the potential to bring new transformations to short-term earthquake prediction research. However, currently, there is no widely used benchmark for this task. To address this, we have built a new benchmark (EPBench), which is, to our knowledge, the first global regional-scale short-term earthquake prediction benchmark. Our benchmark comprises 924,472 earthquake records an…

🌍🛰️ How do NRENs & RRENs support Earth Observation research?
At our April infoshare with the Group on Earth Observations (GEO), RedCLARA & ASREN, we explored how R&E networks enable Earth Observation research, supporting data access, policy, training, and cross-border collaboration.
Daniela Brauner also shared these insights at GEO Global Forum 2025 last week.
📅 Don’t miss Part 2 of the infoshare series on 27 May!
🔗 Register today:

Infoshare Series on Earth Observations Support Through NRENs - Part 2

Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
…

Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Fur…

This https://arxiv.org/abs/2505.03458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain…

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Shunyao Wang, Ming Cheng, Christina Dan Wang
https://arxiv.org/abs/2505.06864

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Stochastic Discount Factor (SDF) models provide a unified framework for asset pricing and risk assessment, yet traditional formulations struggle to incorporate unstructured textual information. We introduce NewsNet-SDF, a novel deep learning framework that seamlessly integrates pretrained language model embeddings with financial time series through adversarial networks. Our multimodal architecture processes financial news using GTE-multilingual models, extracts temporal patterns from macroecono…

This https://arxiv.org/abs/2505.15588 has been replaced.
initial toot: https://mastoxiv.page/@arX…

EPBench: A Benchmark for Short-term Earthquake Prediction with Neural Networks
Since the beginning of this century, the significant advancements in artificial intelligence and neural networks have offered the potential to bring new transformations to short-term earthquake prediction research. However, currently, there is no widely used benchmark for this task. To address this, we have built a new benchmark (EPBench), which is, to our knowledge, the first global regional-scale short-term earthquake prediction benchmark. Our benchmark comprises 924,472 earthquake records an…

✨ Spring SIGs wrapped! ✨
From Prague to Paris to Ispra, 4 GÉANT Special Interest Groups (SIGs) met to explore AI, Time & Frequency networks, service strategy development, and network operations.
Spring days packed with great ideas, useful insights, new collaborations, and our beloved Lightning Talks.
👏 Big thanks to CESNET, RENATER, EC JRC & all who joined in-person and online.
Catch up on all the highlights and takeaways here 👉

GÉANT Community Programme | GÉANT CONNECT Online
The GÉANT community programme supports research and education through the activities of Special Interest Groups and Task Forces and connects academia, the business community and governmental organisations.

I read "Then I Am Myself the World: What Consciousness Is and How to Expand It" by Christof Koch.
Interesting book which spends like 8 or 9 chapters detailing all the experiments which prove beyond much doubt that consciousness, and self awareness, is a thing done by a brain.
It describes how perception is a construction of a description, has a chapter called "computational mind"
And then spends the last two chapters describing why he thinks the mind can't be computed, because drugs have made him think experience is some kind of magic associated with highly interconnected causal structures.
Apparently, he thinks, once things become interconnected enough they become able to cause things independently of the physics running those connections.
Which is crazy, obviously. There's nothing causal in direct connections between neurons that isn't equally causal in modeled connections between virtual neurons.
All his evidence in the book from neural MRI scans to the effects of psychedelic drugs and symptoms of strokes and disease point to the brain simulating a virtual reality which is the basis of perception.
That simulated world in which we live is full of colour and shape and sounds and emotions and millions of mental constructs that are built to be correlated by the senses with the outside world, but are not equal to the world itself. We live in a dream constructed to correlate with reality.
But then instead of taking the next step: That consciousness itself is a property of a simulated being inside that mental model of the universe, a property which the brain simulates and applies to the virtual self that's doing the experiencing inside that model, he jumps towards some magic implying pan-psychism or that sufficiently interconnected networks become causally self-complete for some reason nobody can fathom.
Sure, colour and shape and emotions are all made up by the brain but experience can't be! For some reason.
You see in truth dualism is false, in that there is no spirit realm in which ghosts animate the matter of the body somehow.
Yet also, dualism is true, in that there is a simulated mental reality which we live in, computed by the brain in which all perception and experience are created, which is related-to but separate-from the unfolding complicated dance of energy that is the universe our bodies interact with.
People take some DMT trip, and the model of the universe emulated by their brain collapses and breaks. Their virtual simulated self inside their mind has these experiences of being one with the universe or the experience of feeling dead yet conscious or whatever, and these hippies think that the broken down simulated experience is real and reflects how consciousness is more fundamental than the atoms that make up the neurons in their brain.
Instead of realizing it shows them that their experienced universe is a simulacrum, they think they get a more direct experience of reality somehow. A consciousness more pure than any mere base atom.
"Then I am myself the world" is a great title. Everything you ever experience is created and simulated in your brain like a dream, the whole universe is inside your head. Even the fact of experience itself.
But that isn't the conclusion Koch reaches somehow, he just jumps from describing the evidence that this is so straight into ascribing super-causal magic consciousness to particular arrangements of atoms that integrated information theory suggest have high correlation, and thinks therefore conciousness is itself the entire universe.
Ah well, fun book. I like arguing in my head with authors that are wrong.
#reading #books #consciousness #thenIAmMyselfTheWorld