Tootfinder

Stationary and Non-Stationary Transition Probabilities in Decision Making: Modeling COVID-19 Dynamics
Romario Gildas Foko Tiomela, Serges Love Teutu Talla, Samson Adekola Alagbe, Olawale Nasiru Lawal, Isabella Kemajou-Brown
https://arxiv.org/abs/2505.21519

Stationary and Non-Stationary Transition Probabilities in Decision Making: Modeling COVID-19 Dynamics
This study explores the complexities of stationary and non-stationary transition probabilities within the framework of a Markov Decision Process (MDP), specifically applied to COVID-19. The research highlights the critical role these probabilities play in accurately modeling disease dynamics and informing evidence-based decisions by policymakers and public health authorities. By incorporating both stationary transition probabilities (which assume constant rates of state changes) and non-station…

DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty
Nicolas Samson, William Larriv\'ee-Hardy, William Dubois, \'Elie Roy-Brouard, Edith Brotherton, Dominic Baril, Julien L\'epine, Fran\c{c}ois Pomerleau
https://arxiv.org/abs/2506.16593

DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty
Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two…

Supervised Similarity for Firm Linkages
Ryan Samson, Adrian Banner, Luca Candelori, Sebastien Cottrell, Tiziana Di Matteo, Paul Duchnowski, Vahagn Kirakosyan, Jose Marques, Kharen Musaelian, Stefano Pasquali, Ryan Stever, Dario Villani
https://arxiv.org/abs/2506.19856

Supervised Similarity for Firm Linkages
We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- II. Model priors and posterior odds
Peter H. Sims, Judd D. Bowman, Steven G. Murray, John P. Barrett, Rigel C. Cappallo, Colin J. Lonsdale, Nivedita Mahesh, Raul A. Monsalve, Alan E. E. Rogers, Titu Samson, Akshatha K. Vydula
https://…

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- II. Model priors and posterior odds
The reliable detection of the global 21-cm signal, a key tracer of Cosmic Dawn and the Epoch of Reionization, requires meticulous data modelling and robust statistical frameworks for model validation and comparison. In Paper I of this series, we presented the Beam-Factor-based Chromaticity Correction (BFCC) model for spectrometer data processed using BFCC to suppress instrumentally induced spectral structure. We demonstrated that the BFCC model, with complexity calibrated by Bayes factor-based …

"10 1 im Lesesaal {der @…], diesmal: Spröde Samtpfoten" https://blog.sbb.berlin/?p=132437

Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
https://arxiv.org/abs/2506.12287

Relative Error Fair Clustering in the Weak-Strong Oracle Model
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak oracle providing potentially inaccurate distance estimates at a low cost. The goal is to produce a near-optimal fair clustering on $n$ input points with a minimum number of strong oracle queries. This models the increasingly common trade-off between accurate but expensive similarity measures (e.g., large-scale embedding…

On-chip pulse generation at 8 {\mu}m wavelength
Annabelle Bricout, Mathieu Bertrand, Philipp T\"aschler, Barbara Schneider, Victor Turpaud, Stefano Calcaterra, Davide Impelluso, Marco Faverzani, David Bouville, Jean-Ren\'e Coudevylle, Samson Edmond, Etienne Herth, Carlos Alonso-Ramos, Laurent Vivien, Jacopo Frigerio, Giovanni Isella, J\'er\^ome Faist, Delphine Marris-Morini

On-chip pulse generation at 8 μm wavelength
The mid-infrared spectral region holds growing importance for applications such as gas sensing and spectroscopy. Although compact ultrashort pulse laser sources are essential to enable these applications, their realization in this spectral range remains an open challenge. We demonstrate an integrated approach to generate pulses in the mid-infrared based on chirped Bragg gratings engineered to compensate for the group delay dispersion of quantum cascade laser frequency comb sources. SiGe graded-…

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink
Tierui Gong, Chau Yuen, Chong Meng Samson See, M\'erouane Debbah, Lajos Hanzo
https://arxiv.org/abs/2506.01355 …

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink
Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution for evolving wireless receivers from the classical to the quantum domain. To further unleash their great potential in wireless communications, we propose a flexible architecture for Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) receivers in the multi-user uplink. Then the corresponding signal model of the RAQ-MIMO system is constructed by paving the way from quantum physics to classical wireless com…

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
Giuseppe Di Caro, Vahagn Kirakosyan, Alexander G. Abanov, Luca Candelori, Nadine Hartmann, Ernest T. Lam, Kharen Musaelian, Ryan Samson, Dario Villani, Martin T. Wells, Richard J. Wenstrup, Mengjia Xu
https://arxiv.org/abs/2506.03199

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-pred…

Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
https://arxiv.org/abs/2506.05495 https://

Learning-Augmented Hierarchical Clustering
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong barriers to approximation algorithms with the hardness of approximation. Thus, we consider the problem of hierarchical clustering given auxiliary information from natural oracles. Specifically, we fo…

🇺🇦 #NowPlaying on BBCRadio3's #InTune
Maurice Ravel, Pavel Kolesnikov & Samson Tsoy:
🎵 Ma mere l'oye - suite vers. for piano duet; Le Jardin feerique
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