Tootfinder

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#Design #Illustration #Portfolio

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
Zhihao Tao, Athina P. Petropulu
https://arxiv.org/abs/2506.14992 https://…

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative …

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
Oluwadamilola Fasina, Ruben V. C. Pohle, Pei-Chun Su, Ronald R. Coifman
https://arxiv.org/abs/2506.15541

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
We examine the intrinsic (within the attention head) and extrinsic (amongst the attention heads) structure of the self-attention mechanism in transformers. Theoretical evidence for invariance of the self-attention mechanism to softmax activation is obtained by appealing to paradifferential calculus, (and is supported by computational examples), which relies on the intrinsic organization of the attention heads. Furthermore, we use an existing methodology for hierarchical organization of tensors …

Sketched Sum-Product Networks for Joins
Brian Tsan, Abylay Amanbayev, Asoke Datta, Florin Rusu
https://arxiv.org/abs/2506.14034 https://

Sketched Sum-Product Networks for Joins
Sketches have shown high accuracy in multi-way join cardinality estimation, a critical problem in cost-based query optimization. Accurately estimating the cardinality of a join operation -- analogous to its computational cost -- allows the optimization of query execution costs in relational database systems. However, although sketches have shown high efficacy in query optimization, they are typically constructed specifically for predefined selections in queries that are assumed to be given a pr…

Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection
Tairan Huang, Yili Wang
https://arxiv.org/abs/2507.11997 https://

Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection
Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typically use preprocessed node embeddings and predefined graph structures to reveal fraudsters, which ignore the rich semantic cues contained in raw textual information. Although Large Language Models (LLMs) exhibit powerful capabilities in processing textual information, it remai…

Typical #Mastodon (Fediverse?) moment:

• a supportive video on the #Fediverse is referenced in a post. In this case the video is on YouTube...
• one person in the comments saying that the video should really be on PeerTube instead
• @…

This https://arxiv.org/abs/2203.01664 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network…

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
A. Ruzza, G. Lodato, G. P. Rosotti, P. J. Armitage
https://arxiv.org/abs/2506.11200

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
Dust substructures in protoplanetary discs can be signatures of embedded young planets whose detection and characterisation would provide a better understanding of planet formation. Traditional techniques used to link substructures' morphology to the properties of putative embedded planets present several limitations that the use of deep learning methods has partly overcome. In our previous work, we developed DBNets, a tool exploiting an ensemble of Convolutional Neural Networks (CNNs) to estim…

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Nichula Wasalathilaka, Mathivathanan Piratheepan, Roshan Godaliyadda, Mervyn Ekanayake, Vijitha Herath
https://arxiv.org/abs/2507.11523

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applica…

projects in the near future:
1. my mac-mini server runs ubuntu server and need apps. I'm thinking about Heimdall and a next cloud instance that I can reach via tailscale. I will also use it as an exit node for my tailscale network.
2. Making the server sleep when not in use and use an old RPi to 'wake it up' also over tailscale.
3. Making a portable router out of a Radxa Rock 4c that also use tailscale as a vpn alternative using the exit node.