Search · four archives
Search · four archives
28 papers · ranked by Valyu relevance
Emilio Corcione, Diana Pfezer, Mario Hentschel, Harald Giessen + 2 more
'Cristina Tarín' 'Andrea Facchinetti'] The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose…
Angesom Ataklity Tesfay, Laurent Clavier
—These days we live in a world with a permanent electromagnetic field. This raises many questions about our health and the deployment of new equipment. The problem is that these fields remain difficult to visualize easily, which only some experts can understand. To tackle this problem, we propose to spatially estimate…
Ekaterina Noskova, Viacheslav Borovitskiy, A Kern
Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware…
Gracie M. White, Amanda P. Siegel, Andres Tovar, Jacek Mateusz Bajkowski + 3 more
The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently…
Keonwook Kim, Yujin Hong, Iren E. Kuznetsova
To extract the phase information from multiple receivers, the conventional sound source localization system involves substantial complexity in software and hardware. Along with the algorithm complexity, the dedicated communication channel and individual analog-to-digital conversions prevent an increase in the system’s…
Jan-Eirik W. Skaar, Nicolai Haug, Alexander J. Stasik, Hans Ekkehard Plesser + 2 more
In computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated by comparison against experimental data. The model outputs of neural…
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea + 1 more
'Thomas Pinder'] Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming…
Ozgur Kisi, Salim Heddam, Kulwinder Singh Parmar, Andrea Petroselli + 2 more
'Christoph Külls' 'Mohammad Zounemat-Kermani'] Accurate rainfall-runoff modeling is crucial for effective watershed management, hydraulic infrastructure safety, and flood mitigation. However, predicting rainfall-runoff remains challenging due to the nonlinear interplay between hydro-meteorological and topographical…
Galvis-Florez, Cristian A., Farooq Ahmad, Särkkä + 1 more
—Probabilistic machine learning models are distinguished by their ability to integrate prior knowledge of noise statistics, smoothness parameters, and training data uncertainty. A common approach involves modeling data with Gaussian processes; however, their computational complexity quickly becomes intractable as the…
Juan J. L. Velázquez, Luis A. Escamilla, Purba Mukherjee, J. Alberto Vázquez
Processes Regression Authors: ['Juan J. L. Velázquez' 'Luis A. Escamilla' 'Purba Mukherjee' 'J. Alberto Vázquez'] The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine…
Kanta Matsunaga, Takuto Harada, Shintaro Harada, Akinori Sato + 4 more
Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process Authors: ['Kanta Matsunaga' 'Takuto Harada' 'Shintaro Harada' 'Akinori Sato' 'Shota Terai' 'Mutsunori Uenuma' 'Tomoyuki Miyao' 'Yukiharu Uraoka'] During data-driven process condition optimization on…
Huang Zhang, Xixi Liu, Faisal Altaf, Torsten Wik
— Gaussian process (GP) models have been used in a wide range of battery applications, in which different kernels were manually selected with considerable expertise. However, to capture complex relationships in the ever-growing amount of real-world data, selecting a suitable kernel for the GP model in battery…
Gaurav Shrivastava
Obstacle-aware trajectory navigation is an important need for many systems. For instance, in real-world navigation tasks, an agent would want to avoid obstacles, like furniture items in a room when planning a trajectory to traverse. Gaussian Process (GP) regression in its current construct is designed to fit a curve on…
Sho Takaoka, Hiromasa Kaneko
Green ammonia is attracting attention as a way of realizing a decarbonized society. In this study, we designed a green ammonia production process under limited power supply. In conventional process optimization, candidates for the design variables are selected based on the experience of the process engineer and…
Saad M. Alshahrani, Ahmed Al Saqr, Munerah M. Alfadhel, Abdullah S. Alshetaili + 6 more
'Abdullah S. Alshetaili' 'Bjad K. Almutairy' 'Amal M. Alsubaiyel' 'Ali H. Almari' 'Jawaher Abdullah Alamoudi' 'Mohammed A. S. Abourehab' 'Francisco Torrens'] Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great…
Cecile Le Sueur, Magnus Rattray, Mikhail Savitski
Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein…
Yuta Shikuri
Quasi-Likelihood Authors: ['Yuta Shikuri'] Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit from this development. Difficulties…
Samantha Petti, Carlos Martí-Gómez, Justin B. Kinney, Juannan Zhou + 1 more
Mappings from biological sequences (DNA, RNA, protein) to quantitative measures of sequence functionality play an important role in contemporary biology. We are interested in the related tasks of (i) inferring predictive sequence-to-function maps and (ii) decomposing sequence-function maps to elucidate the…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Baptiste Py, Adeleke Maradesa, Francesco Ciucci
Electrochemical impedance spectroscopy (EIS) is a widespread characterization technique used to study electrochemical systems. However, several shortcomings still limit the application of this technique. First, EIS data is intrinsically noisy, hindering spectra regression and prediction at unknown frequencies. Second…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
M. Rule, P. Chaudhuri-Vayalambrone, M. Krstulovic, M. Bauza + 2 more
We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid…
Allen Ross, Jason Lloyd-Price, Ali Rahnavard
Identifying meaningful associations from small-sample longitudinal data is challenging, especially in low signal-to-noise environments where the Gaussian likelihood assumption does not hold. We introduce two methods to algorithmically perform variable selection with sparse, irregularly sampled, longitudinal count data…
M. Nicolás Cruz-Bournazou, Harini Narayanan, Alessandro Fagnani, Alessandro Butté
Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…