25 papers · ranked by Valyu relevance
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Chao Ma, Yikai Hou, Li Xiang, Yinggang Sun + 3 more
'Jiaxing Qu'] Long-term time-series forecasting is essential for planning and decision-making in economics, energy, and transportation, where long foresight is required. To obtain such long foresight, models must be both efficient and effective in processing long sequence. Recent advancements have enhanced the…
Rajshekhar Gannavarpu, Dario Ambrosini
The paper introduces a multi-scale processing method for quantitative study and visualization of convective heat transfer using diffractive optical element based background-oriented schlieren technique. The method relies on robust estimation of phase encoded in the fringe pattern using windowed Fourier transform and…
Antonio Lanata, Mimma Nardelli
Physiological systems are characterized by complex dynamics and nonlinear behaviors due to their intricate structural organization and regulatory mechanisms. Moreover, the optimization of physiological states and functions involves the continuous dynamic interaction of feedback mechanisms across different…
Yuankun Xue, Paul Bogdan
Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the role of interaction…
Hamid Karimi-Rouzbahani
Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect…
Authors not listed
Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies challenging. To address this, we propose SEMixer…
Authors not listed
Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have…
John W. Coulston, Nicola Zaccarelli, Kurt H. Riitters, Frank Koch + 1 more
'Giovanni Zurlini'] Multiple-scale and broad-scale assessments often require rescaling the original data to a consistent grain size for analysis. Rescaling categorical raster data by spatial aggregation is common in large area ecological assessments. However, distortion and loss of information are associated with…
Ya Zhou, Yujie Yang, Jianhuang Gan, Xiangjie Li + 3 more
Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular conditions. While traditional classification models require large volumes of labeled data across multiple disease categories, anomaly detection offers a flexible alternative by identifying deviations from normal patterns-an approach particularly…
Iman Deznabi, Madalina Fiterau
The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard deep learning models are often unsuitable for such datasets, as signals are typically sampled at different rates. To address these issues, we…
Vahid Ganjalizadeh, Gopikrishnan G. Meena, Thomas A. Wall, Matthew A. Stott + 2 more
Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The…
Hüsser Alejandra, Caron-Desrochers Laura, Tremblay Julie, Vannasing Phetsamone + 2 more
Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that uses light at two different wavelengths in the near infrared spectrum to estimate cerebral hemodynamic response, based on concentration changes in both oxygenated and deoxygenated hemoglobin. A multi-dimensional decomposition technique…
Vasile V. Moca, Adriana Nagy-Dăbâcan, Harald Bârzan, Raul C. Mureşan
Time-frequency analysis is ubiquitous in many fields of science. Due to the Heisenberg-Gabor uncertainty principle, a single measurement cannot estimate precisely the localization of a finite oscillation in both time and frequency. Classical spectral estimators, like the short-time Fourier transform (STFT) or the…
Björn Harink, Huy Nguyen, Kurt Thorn, Polly Fordyce + 1 more
'Vadim E. Degtyar'] Multiplexed bioassays, in which multiple analytes of interest are probed in parallel within a single small volume, have greatly accelerated the pace of biological discovery. Bead-based multiplexed bioassays have many technical advantages, including near solution-phase kinetics, small sample volume…
Björn Harink, Huy Nguyen, Kurt Thorn, Polly Fordyce
Multiplexed bioassays, in which multiple analytes of interest are probed in parallel within a single small volume, have greatly accelerated the pace of biological discovery. Bead-based multiplexed bioassays have many technical advantages, including near solution-phase kinetics, small sample volume requirements, many…
Vladimir Guchev, Paolo Buono, Cristina Gena
The analysis of structured complex data, such as clustered graph based datasets, usually applies a variety of visual representa9on techniques and formats. The majority of currently available tools and approaches to exploratory visualiza9on are built on integrated schemes for simultaneous displaying of mul9ple aspects…
Authors not listed
As demand rises for scalable and sustainable energy storage, fast and low-cost diagnostics capable of identifying cell-to-cell variations are urgently needed, particularly for factory-produced sorting and second-life assessment. Electrochemical impedance spectroscopy (EIS) is widely used but remains slow, expensive and…
Mojtaba Taherisadr, Omid Dehzangi, Hossein Parsaei
As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…
Lucas C. Lazari, Ghasem Azemi, Carlo Russo, Livia Rosa-Fernandes + 4 more
Data preprocessing is a critical step in the analysis of matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) spectra for machine learning applications, typically involving steps such as spectra trimming, baseline correction, smoothing, transformation, and peak picking or spectral…
Stefan Bode, Daniel Feuerriegel, Daniel Bennett, Phillip M. Alday
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the…
Giuseppe Bonifazi, Paolo Barontini, Riccardo Gasbarrone, Davide Gattabria + 3 more
In this manuscript, a method that utilizes classical image techniques to assess particle aggregation and segregation, with the primary goal of validating particle size distribution determined by conventional methods, is presented. This approach can represent a supplementary tool in quality control systems for powder…
Authors not listed
Shinyscreen is an R package and Shiny-based web application designed for the exploration, visualization, and quality assessment of raw data from high resolution mass spectrometry instruments. Its versatile compound or mass list-based approach supports the curation of data starting from either known or “suspected”…
Authors not listed
Machine learning models for predicting IR spectra of molecular ions (infrared ion spectroscopy, IRIS) have yet to be reported owing to the relatively sparse experimental datasets available. To overcome this limitation, we employ the Graphormer-IR model for neutral molecules as a knowledgeable starting point, then…