Tootfinder

Quantum Markov Chain Monte Carlo for Cosmological Functions
Giuseppe Sarracino, Vincenzo Fabrizio Cardone, Roberto Scaramella, Giuseppe Riccio, Andrea Bulgarelli, Carlo Burigana, Luca Cappelli, Stefano Cavuoti, Farida Farsian, Irene Graziotti, Massimo Meneghetti, Giuseppe Murante, Nicol\`o Parmiggiani, Alessandro Rizzo, Francesco Schillir\`o, Vincenzo Testa, Tiziana Trombetti

Quantum Markov Chain Monte Carlo for Cosmological Functions
We present an implementation of Quantum Computing for a Markov Chain Monte Carlo method with an application to cosmological functions, to derive posterior distributions from cosmological probes. The algorithm proposes new steps in the parameter space via a quantum circuit whose resulting statevector provides the components of the shift vector. The proposed point is accepted or rejected via the classical Metropolis-Hastings acceptance method. The advantage of this hybrid quantum approach is that…

Trump announces he will posthumously award Charlie Kirk the Presidential Medal of Freedom (Alexandra Hutzler/ABC News)
https://abcnews.go.com/Politics/trump-announces-posthumously-award-charlie-kirk-presidential-medal/story?id=125474603
http://www.memeorandum.com/250911/p52#a250911p52

Where the Raiders Stand Entering Titans Clash https://www.si.com/nfl/raiders/las-vegas-tennessee-titans-pete-carroll-geno-smith-indianapolis-colts

Perfectly-Private Analog Secure Aggregation in Federated Learning
Delio Jaramillo-Velez, Charul Rajput, Ragnar Freij-Hollanti, Camilla Hollanti, Alexandre Graell i Amat
https://arxiv.org/abs/2509.08683

Perfectly-Private Analog Secure Aggregation in Federated Learning
In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the masked local models to be aggregated. This raises a problem when working with real valued da…

Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
Marco Bertuletti, Yichao Zhang, Mahdi Abdollahpour, Samuel Riedel, Alessandro Vanelli-Coralli
https://arxiv.org/abs/2508.06141

Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
The fast-rising demand for wireless bandwidth requires rapid evolution of high-performance baseband processing infrastructure. Programmable many-core processors for software-defined radio (SDR) have emerged as high-performance baseband processing engines, offering the flexibility required to capture evolving wireless standards and technologies. This trend must be supported by a design framework enabling functional validation and end-to-end performance analysis of SDR hardware within realistic r…

Bayesian Nonparametric Dynamical Clustering of Time Series
Adri\'an P\'erez-Herrero, Paulo F\'elix, Jes\'us Presedo, Carl Henrik Ek
https://arxiv.org/abs/2510.06919

Bayesian Nonparametric Dynamical Clustering of Time Series
We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the parameters of a Switching Linear Dynamical System and a Gaussian process prior to model the statistical variations in amplitude and temporal alignment within each cluster. By modeling the evolution of time series patterns, the method…

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano
https://arxiv.org/abs/2510.06235

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations from large language models, video encoders, audio models, and vision-language models, combining both off-the-shelf and fine-tuned variants. To improve performance, we enhanced textual inputs with detailed transcripts and summaries, and we explored stimulus-tuning and fine-tuning strategies for language and vision models…

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G Software-Defined Radio Uplinks
Marco Bertuletti, Yichao Zhang, Alessandro Vanelli-Coralli, Luca Benini
https://arxiv.org/abs/2508.06176

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G+ Software-Defined Radio Uplinks
Following the scale-up of new radio (NR) complexity in 5G and beyond, the physical layer's computing load on base stations is increasing under a strictly constrained latency and power budget; base stations must process > 20-Gb/s uplink wireless data rate on the fly, in < 10 W. At the same time, the programmability and reconfigurability of base station components are the key requirements; it reduces the time and cost of new networks' deployment, it lowers the acceptance threshold for industry pl…

Titans’ Cam Ward Gets Strong Message From Raiders’ Pete Carroll https://heavy.com/sports/nfl/las-vegas-raiders/titans-cam-ward-message-pete-carroll/?adt_ei=[email]

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Wonseo Jang, Dongjae Kim
https://arxiv.org/abs/2509.04815 https://

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Deep reinforcement learning (RL) models, despite their efficiency in learning an optimal policy in static environments, easily loses previously learned knowledge (i.e., catastrophic forgetting). It leads RL models to poor performance in continual reinforcement learning (CRL) scenarios. To address this, we present an arbitration control mechanism over an ensemble of RL agents. It is motivated by and closely aligned with how humans make decisions in a CRL context using an arbitration control of m…