Persistence in complex systems

Salcedo‐Sanz, Sancho; Casillas-Pérez, David; Ser, Javier Del; Casanova-Mateo, C.; Cuadra, L.; Piles, María; Camps‐Valls, Gustau

doi:10.1016/j.physrep.2022.02.002

Cited by 40 publications

(17 citation statements)

References 458 publications

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“…Note that this behavior is somehow related to the concept of the persistence of the system, a concept related in turn to the memory of the system. More specifically, persistence is an important characteristic of many complex systems in nature, related to how long the system remains in a certain state before changing to a different one [41]. In the case of prediction problems with human activity patterns, persistence is related to the existing information in the training set to predict a given unseen sample in the test set.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

et al. 2022

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Prediction problems in timed datasets related to human activities are especially difficult to solve, because of the specific characteristics and the scarce number of predictive (input) variables available to tackle these problems. In this paper, we try to find out whether Machine Learning (ML) approaches can be successfully applied to these problems. We deal with timed datasets with human activity patterns, in which the input variables are exclusively related to the day or type of day when the prediction is carried out and, usually, to the meteorology of those days. These problems with a marked human activity pattern frequently appear in mobility and traffic-related problems, delivery prediction (packets, food), and many other activities, usually in cities. We evaluate the performance in these problems of different ML methods such as artificial neural networks (multi-layer perceptrons, extreme learning machines) and support vector regression algorithms, together with an Analogue-type (KNN) approach, which serves as a baseline algorithm and provides information about when it is expected that ML approaches will fail, by looking for similar situations in the past. The considered ML algorithms are evaluated in four real prediction problems with human activity patterns, such as school absences, bike-sharing demand, parking occupation, and packets delivered in a post office. The results obtained show the good performance of the ML algorithms, revealing that they can deal with scarce information in all the problems considered. The results obtained have also revealed the importance of including meteorology as the input variables, showing that meteorology is frequently behind demand peaks or valleys in this kind of problem. Finally, we show that having a number of similar situations in the past (training set) prevents ML algorithms from making important mistakes in the prediction obtained.

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Section: Resultsmentioning

confidence: 99%

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

et al. 2022

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“…After data preparation, a preprocessing step is applied to remove any artifacts, so that minimum signal loss occurs [18]. The dataset was also augmented using a timeseries generator to extend data samples and their targets [9]. The augmentation step resulted in 1056 records of sEMG sequence signal in terms of the five corresponding muscles.…”

Section: B Signal Preparation and Pre-processingmentioning

confidence: 99%

“…The spectrogram generated by wavelet transform is known as a scalogram [8]. Continuous wavelets transform (CWT) preserves time shifts and time scale in terms of variable time-frequency resolution [9], as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Lower Limb Analysis Based on Surface Electromyography (sEMG) Using Different Time-frequency Representation Techniques

Ibraheem¹

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Using time-frequency representation techniques, projecting 1D sEMG signals onto a 2D image space can help diagnose several muscle activities. The acquired sEMG signal can provide valuable representative information about the muscle activity firing rates during muscle contraction. Different phases of muscle activity can be discernible via the sEMG signals by extracting discriminating features. The behavior of muscle activity was acquired in measurements of five muscles, i.e., RF, BF, VM, ST, and FX. Previous attempts to visualize lower limb analysis to extract sEMG features adopted One-dimensional (1D) sEMG segments. This work proposes a comparative experiment between three time-frequency representation techniques. The three time-frequency representation techniques, scalogram, spectrogram, and persistence spectrum, were used to map muscles' (1D) sEMG signal straightening the knee. The two-dimensional (2D) projected images are then fed into a convolutional neural network (CNN) model for detecting knee abnormality. The experiments are performed via 10-fold cross-validation. The number of kernels is incremented along with model layers. The fully connected layers were adjusted according to the loss value. Besides, tuning the hyper-parameters of the dropout parameters and the ReLU activation function to verify optimal performance. This research shows that the scalogram image representation gives significantly better performance than the spectrogram and persistence spectrum in recognizing knee abnormality. In addition, this study may help in guiding the diagnosis of several human muscle activities via the sEMG signal. A more diverse of muscles can be further investigated and can be useful for future work to enhance the diagnosis accuracy.

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“…More examples can be found in [ 88 , 95 , 107 ] and the references therein. Furthermore, NS math tools [ 107 , 108 , 109 ] also help analyze dynamic processes involving the spread of epidemics [ 110 ] such as COVID-19 [ 111 ], cascading failures in technological networks [ 112 , 113 ] or the spreading [ 114 ] and persistence of information, memes or ideas [ 115 ]. For deeper concepts, the interested reader is referred to [ 107 ].…”

Section: Introductionmentioning

confidence: 99%

Carrier Transport in Colloidal Quantum Dot Intermediate Band Solar Cell Materials Using Network Science

Cuadra

Salcedo‐Sanz

Borge

2023

IJMS

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Colloidal quantum dots (CQDs) have been proposed to obtain intermediate band (IB) materials. The IB solar cell can absorb sub-band-gap photons via an isolated IB within the gap, generating extra electron-hole pairs that increase the current without degrading the voltage, as has been demonstrated experimentally for real cells. In this paper, we model the electron hopping transport (HT) as a network embedded in space and energy so that a node represents the first excited electron state localized in a CQD while a link encodes the Miller–Abrahams (MA) hopping rate for the electron to hop from one node (=state) to another, forming an “electron-HT network”. Similarly, we model the hole-HT system as a network so that a node encodes the first hole state localized in a CQD while a link represents the MA hopping rate for the hole to hop between nodes, leading to a “hole-HT network”. The associated network Laplacian matrices allow for studying carrier dynamics in both networks. Our simulations suggest that reducing both the carrier effective mass in the ligand and the inter-dot distance increases HT efficiency. We have found a design constraint: It is necessary for the average barrier height to be larger than the energetic disorder to not degrade intra-band absorption.

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Persistence in complex systems

Cited by 40 publications

References 458 publications

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

Lower Limb Analysis Based on Surface Electromyography (sEMG) Using Different Time-frequency Representation Techniques

Carrier Transport in Colloidal Quantum Dot Intermediate Band Solar Cell Materials Using Network Science

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