Time-dependent Data Research Articles

Toward Explainable Affective Computing: A Review.

Affective computing has an unprecedented potential to change the way humans interact with technology. While the last decades have witnessed vast progress in the field, multimodal affective computing systems are generally black box by design. As affective systems start to be deployed in real-world scenarios, such as education or healthcare, a shift of focus toward improved transparency and interpretability is needed. In this context, how do we explain the output of affective computing models? and how to do so without limiting predictive performance? In this article, we review affective computing work from an explainable AI (XAI) perspective, collecting and synthesizing relevant papers into three major XAI approaches: premodel (applied before training), in-model (applied during training), and postmodel (applied after training). We present and discuss the most fundamental challenges in the field, namely, how to relate explanations back to multimodal and time-dependent data, how to integrate context and inductive biases into explanations using mechanisms such as attention, generative modeling, or graph-based methods, and how to capture intramodal and cross-modal interactions in post hoc explanations. While explainable affective computing is still nascent, existing methods are promising, contributing not only toward improved transparency but, in many cases, surpassing state-of-the-art results. Based on these findings, we explore directions for future research and discuss the importance of data-driven XAI and explanation goals, and explainee needs definition, as well as causability or the extent to which a given method leads to human understanding.

Continuous Biosensor Based on Particle Motion: How Does the Concentration Measurement Precision Depend on Time Scale?

Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation.

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications.

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.

Polymer Free Volume-Controlled Molecular Clock and Emitter for Multicolored Transient Data Display in Advanced Photonic Cryptography.

The booming demand on data security has aroused great interest for developing smart materials with temporal display feature and dynamic multicolor fluorescence. However, it remains challenging to implement both features on most responsive molecules. Herein, we construct a polymer free volume-controlled "molecular clock and emitter" via covalently embedding a multi-stimuli responsive molecular switch (i.e., spiropyran) into a polymer network (i.e., poly(pentafluorophenyl acrylate)) with programmable crosslink density and free volume. By the aminolysis of pentafluorophenyl ester with different amount of diamine crosslinkers, pPFPA-co-SP networks with controllable crosslink densities are generated, which have different confinement effects on the rate constant of SP/MC isomerization, thus leading to time-dependent photochromism. In addition, PTF1, a fluorescent probe that is sensitive to polymer rigidity, is introduced to further endow pPFPA-co-SP system with phototunable dynamic full-color emission. Therefore, relying on their synergistical responses to the rigidity of the polymer network, we have successfully developed a versatile molecular clock and emitter via an "one stone two birds" manner, which shows time-dependent data display along with dynamic multicolor fluorescence switching, providing great potential for advanced encryption and anticounterfeiting with a high security level.

Research on Intelligent Translation System of Spoken English Based on Cyclic Neural Network Model

This paper explores the development of an intelligent translation system for spoken English using Recurrent Neural Network (RNN) models. The fundamental principles of RNNs and their advantages in processing sequential data, particularly in handling time-dependent natural language data, are discussed. The methodology for constructing the translation system is outlined, covering key steps such as data preprocessing, model architecture design, and training optimization. The system's performance is evaluated in terms of translation accuracy, fluency, and real-time processing capabilities. The study identifies limitations of the current system and proposes future research directions, including the integration of attention mechanisms, refinement of model architectures, and enhancement of multilingual translation capabilities. Ultimately, this research contributes theoretical insights and practical guidance to the ongoing development of intelligent translation systems for spoken English.

A novel naphthalimide-derived “turn-off” chemosensor for the detection of Cu2+, F- and CN- ions

The development of selective chemosensor for the detection of detrimental analytes in the environment is crucial due to their catastrophic effects. In this study, a novel Schiff base derived from naphthalimide and salicylaldehyde was designed and prepared for the detection of Cu2+, F-, and CN- ions in CH3CN as medium. The chemosensor exhibits a red shift in the presence of Cu2+, F-, and CN- ions accompanied by naked eye color change in the presence of F- and CN- ions. On the other hand, a “turn-off” response was observed in the presence of ion Cu2+, F-, and CN- ions in the fluorescent spectrum. The good anti-interference characteristics, strong binding, and low limit of detection (LOD) of the chemosensor revealed its sensitivity against Cu2+, F-, and CN- ions i.e. 1.25 × 10-5 M, 1.07 × 10-6 M, and 0.98 × 10-6 M, respectively. The binding ratio (1:1) was determined by Job's plot between the chemosensor and ions. In addition, the binding of ions with the chemosensor was proposed which was further supported by time-dependent density functional theory (TD-DFT) and IR spectrum data.

Effect of Energy Utilization on Pakistan’s Economic Development: A Time Series Analysis

The present research has been conducted to study the causal relation among GDP, electricity utilization, exports, real capital and labor force for Pakistan. Time dependent data for the mentioned parameters have been used for the time period of 1980 to 2022. Inter-relations among the above-mentioned variables have been studied in this work by the method of cointegration using bounds test. The results illustrate that there is an existence of long run relation among the parameters where GDP has been taken into consideration as the dependent variable. Granger causation analysis has also been performed for the variables. Results show that Granger causality between GDP and electricity utilization runs in both directions. Moreover, the study discloses that Electricity utilization granger cause exports and per capita real capital. Exports granger cause per capita real capital. Per capita real capital granger cause GDP. Labor force granger cause GDP and exports. The long run relation equation of GDP, Electricity utilization, exports, real capital and labor force has also been examined for parameter stability. The parameters are found to be stable with the significance level of 5%. The research also suggests some significant strategy recommendations.

Investigation of coverage dependence of the stretching frequency of CO adsorbed on Pd surfaces at low coverage limits

The stretching frequency of the CO bond is a sensitive probe of the local environment of a surface-bound CO molecule, including the adorption site and density, i.e. surface coverage. In this work, we extend our analysis beyond the frequency shift due to differences in adsorption configurations. Using density functional theory (DFT) calculations, we directly explore the correlations between surface coverage and the stretching frequency of adsorbed CO on Pd surfaces. We also perform constant pressure infrared reflection absorption measurements of CO on Pd(111) and use existing relations between pressure and coverage to derive coverage dependency. Both results are compared to previously reported experimental data. Our derived correlations of peak frequency and area with surface coverage can help interpret experimental IR spectra in real time and extract time-dependent concentration data from transient kinetic experiments.

Exploring physical and digital architectures in magnetic nanoring array reservoir computers

Physical reservoir computing (RC) is a machine learning technique that is ideal for processing of time dependent data series. It is also uniquely well-aligned to in materio computing realisations that allow the inherent memory and non-linear responses of functional materials to be directly exploited for computation. We have previously shown that square arrays of interconnected magnetic nanorings are attractive candidates for in materio RC, and experimentally demonstrated their strong performance in a range of benchmark tasks (Dawidek et al 2021 Adv. Funct. Mater. 31 2008389, Vidamour et al 2022 Nanotechnology 33 485203, Vidamour et al 2023 Commun. Phys. 6 230). Here, we extend these studies to other lattice arrangements of rings, including trigonal and Kagome grids, to explore how these affect both the magnetic behaviours of the arrays, and their computational properties. We show that while lattice geometry substantially affects the microstate behaviour of the arrays, these differences manifest less profoundly when averaging magnetic behaviour across the arrays. Consequently the computational properties (as measured using task agnostic metrics) of devices with a single electrical readout are found to be only subtly different, with the approach used to time-multiplex data into and out of the arrays having a stronger effect on properties than the lattice geometry. However, we also find that hybrid reservoirs that combine the outputs from arrays with different lattice geometries show enhanced computational properties compared to any single array.

ENHANCED NETWORK SECURITY: A DATA MINING APPROACH TO INTRUSION DETECTION

In the ever-evolving landscape of network security, the role of Intrusion Detection Systems (IDSs) is critical. These systems serve as the guardians of digital networks, defending against a relentless tide of cyber threats. However, the efficacy of IDSs in accurately detecting and preventing these threats remains a critical concern. The emergence of new attack vectors and the growing sophistication of cyberattacks underscore the need for innovative approaches to intrusion detection. Nonetheless, there is an information vacuum about the efficacy of different data mining approaches in intrusion detection. This study investigates the deployment of seven data mining models, which include Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and two types of Recurrent Neural Networks (RNN) on five (5) IDS benchmark datasets. Based on the undertaken experiments, it is evident that the RNN is the best classifier as it obtained 100% accuracy for all datasets. Due to its strength in modelling time-dependent and sequential data problems, RNN has become powerful in predicting future attacks. As our digital world evolves, so must our cyber defences; hence, this study strives to equip network security professionals with the knowledge and tools needed to fortify their networks, ensuring resilience against the ever-adaptive landscape of cyber threats.

Time series analysis and applications in data analysis, forecasting and prediction

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.

Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali

Article Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali Boesjes Freek 1, Jahani Alireza 2, Ooink Bas 3, and Derek Groen 2,* 1 Utrecht University, Heidelberglaan 8, Utrecht, Utrecht 3584 CS, the Netherlands 2 Computer Science Department, Brunel University London, Wilfred Brown Building, Kingston Lane, Uxbridge UB8 3PH, UK 3 Blue Team Intelligence, Jan Aertshof 3, Hoevelaken 3871 WH, the Netherlands * Correspondence: Derek.Groen@brunel.ac.uk Received: 7 September 2023 Accepted: 30 January 2024 Published: 26 March 2024 Abstract: As of 2023, the world has approximately 100 million refugees, many of whom have been displaced by violent conflicts. Accurately predicting where these people may go can help non-government organisations (NGOs) and other support organisations to more effectively help these refugees. In this paper, we extend the existing flee migration forecasting model which models migration using intelligent agents with a dynamic network that represents the physical environment. In doing so, we integrate time-dependent data into four different characteristics from three public data sources. We obtain data from aspects such as the slope, drainage, soil and infrastructure, and use these aspects to systematically modify the movement preferences of forcibly displaced agents in the flee model. We showcase our approach by applying it to the 2012 northern Mali conflict. We find that numerous routes previously deemed traversable are actually inaccessible for prolonged periods according to sensor data, and a range of off-road routes are instead traversable for vehicles. We also perform a validation comparison with the original modelling approach, and find that our revised representation of travel routes leads to a reduction of 4.5% in the averaged relative difference. Our approach can be reused in other flee conflict contexts, of which five are present in the EU-funded ITFLOWS project alone. Our work provides the ability to represent a dynamic physical environment and potentially improves the simulation accuracy in a range of flee conflict situations.

Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named Temporal MultiPersistence (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Abstract. Ocean models must be regularly updated through the assimilation of observations (data assimilation) in order to correctly represent the timing and locations of eddies. Since initial conditions play an important role in the quality of short-term ocean forecasts, an effective data assimilation scheme to produce accurate state estimates is key to improving prediction. Western boundary current regions, such as the East Australia Current system, are highly variable regions, making them particularly challenging to model and predict. This study assesses the performance of two ocean data assimilation systems in the East Australian Current system over a 2-year period. We compare the time-dependent 4-dimensional variational (4D-Var) data assimilation system with the more computationally efficient, time-independent ensemble optimal interpolation (EnOI) system, across a common modelling and observational framework. Both systems assimilate the same observations: satellite-derived sea surface height, sea surface temperature, vertical profiles of temperature and salinity (from Argo floats), and temperature profiles from expendable bathythermographs. We analyse both systems' performance against independent data that are withheld, allowing a thorough analysis of system performance. The 4D-Var system is 25 times more expensive but outperforms the EnOI system against both assimilated and independent observations at the surface and subsurface. For forecast horizons of 5 d, root-mean-squared forecast errors are 20 %–60 % higher for the EnOI system compared to the 4D-Var system. The 4D-Var system, which assimilates observations over 5 d windows, provides a smoother transition from the end of the forecast to the subsequent analysis field. The EnOI system displays elevated low-frequency (>1 d) surface-intensified variability in temperature and elevated kinetic energy at length scales less than 100 km at the beginning of the forecast windows. The 4D-Var system displays elevated energy in the near-inertial range throughout the water column, with the wavenumber kinetic energy spectra remaining unchanged upon assimilation. Overall, this comparison shows quantitatively that the 4D-Var system results in improved predictability as the analysis provides a smoother and more dynamically balanced fit between the observations and the model's time-evolving flow. This advocates the use of advanced, time-dependent data assimilation methods, particularly for highly variable oceanic regions, and motivates future work into further improving data assimilation schemes.

Dynamically coupled kinetic chemistry in brown dwarf atmospheres – II. Cloud and chemistry connections in directly imaged sub-Jupiter exoplanets

ABSTRACT With JWST slated to gain high-fidelity time-dependent data on brown dwarf atmospheres, it is highly anticipated to do the same for directly imaged, sub-Jupiter exoplanets. With this new capability, the need for a full three-dimensional (3D) understanding to explain spectral features and their time dependence is becoming a vital aspect for consideration. To examine the atmospheric properties of directly imaged sub-Jupiter exoplanets, we use the 3D Exo-FMS general circulation model to simulate a metal-enhanced generic young sub-Jupiter object. We couple Exo-FMS to a kinetic chemistry scheme, a tracer-based cloud formation scheme and a spectral radiative-transfer model to take into account the chemical and cloud feedback on the atmospheric thermochemical and dynamical properties. Our results show a highly complex feedback between clouds and chemistry on to the 3D temperature structure of the atmosphere, bringing about latitudinal differences and inducing time-dependent stormy features at photospheric pressures. This suggests a strong connection and feedback between the spatial cloud coverage and chemical composition of the atmosphere, with the temperature changes and dynamical motions induced by cloud opacity and triggered convection feedback driving chemical species behaviour. In addition, we also produce synthetic latitude-dependent and time-dependent spectra of our model to investigate atmospheric variability and periodicity in commonly used photometric bands. Overall, our efforts put the included physics in 3D simulations of exoplanets on par with contemporary 1D radiative-convective equilibrium modelling.

Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees

Structural discovery among a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system can be represented through a structural equation model (SEM) that simultaneously captures the contemporaneous and temporal relationships amongst the variables, with the former encoded through a directed acyclic graph (DAG) for model identification. In many real applications, a partial ordering amongst the nodes of the DAG is available, which makes it either beneficial or imperative to incorporate it as a constraint in the problem formulation. This article develops an algorithm that can seamlessly incorporate a priori partial ordering information for solving a linear SEM (also known as Structural Vector Autoregression) under a high-dimensional setting. The proposed algorithm is provably convergent to a stationary point, and exhibits competitive performance on both synthetic and real datasets. Supplementary materials for this article are available online.

Dimension reduction, exact recovery, and error estimates for sparse reconstruction in phase space

An important theme in modern inverse problems is the reconstruction of time-dependent data from only finitely many measurements. To obtain satisfactory reconstruction results in this setting it is essential to strongly exploit temporal consistency between the different measurement times. The strongest consistency can be achieved by reconstructing data directly in phase space, the space of positions and velocities. However, this space is usually too high-dimensional for feasible computations. We introduce a novel dimension reduction technique, based on projections of phase space onto lower-dimensional subspaces, which provably circumvents this curse of dimensionality: Indeed, in the exemplary framework of superresolution we prove that known exact reconstruction results stay true after dimension reduction, and we additionally prove new error estimates of reconstructions from noisy data in optimal transport metrics which are of the same quality as one would obtain in the non-dimension-reduced case.

Optical deformations of azobenzene polymers: orientation approach vs. other concepts.

It has been 30 years since the discovery of surface restructuring in thin azopolymer films by two independent research groups. A wide variety of topographical structures have been created by the application of two-/four-beam interference patterns, space light modulators and even helical beams. There are a number of comprehensive reviews which describe in detail the advances in superficial photopatterning of azopolymer films and macroscopic deformations of azonetworks. The theoretical approaches are only briefly touched on in these reviews and often are accompanied by the remark that the phenomenon is far from being understood. In this review, we would like to present the polymer theoretist's point of view on this intriguing problem. We begin by describing a multitude of theoretical approaches and commenting on the pluses and drawbacks of each. Importantly, we show that in most cases the presence of an azopolymer matrix is either ignored or limited to a specific class of azopolymers (liquid-crystalline or elastomeric). We then move to early orientation approaches based on the hypothesis that reorientation of azo-chromophores by modulated polarized light is the sole cause of superficial patterning. At the end of the review a modern orientation approach, as proposed by our own group, is presented. This approach has high predictive power because it can explain a large pool of experimental data for different classes of azopolymers including glassy and liquid-crystalline materials. This is made possible by taking into account both the light-induced orientation process and the change of anisotropic interactions between the chromophores upon their isomerization. Last but not least, this is the only approach that provides an estimate of the light-induced stress large enough to cause plastic deformations of glassy azopolymers. Recent finite element modeling results show remarkable similarity to real patterns and even time-dependent data are well explained. With this, we claim that the puzzle is finally understood and the orientation approach is ready for its implementation for major azopolymer classes.