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Over the years, the Internet of Things (IoT) devices have shown rapid proliferation and development in various domains. However, the widespread adoption of smart devices significantly ameliorates the possibility of several security challenges. To address these challenges, this research presents an advanced AI-enhanced trust framework for IoT Intrusion detection to safeguard IoT environments from any potential intrusion attempts. The proposed framework integrates cutting-edge AI techniques for intrusion detection which identifies the anomalies based on the device behavior and responds dynamically to emerging threats. Initially, a robust Intrusion Detection System (IDS) is developed based on an Isolation Forest (IF) algorithm and Autoencoders (AE) to promptly identify anomalies in real-time. Then, behavioral Modeling is performed by employing Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) for precise behavioral understanding of IoT devices. Additionally, the Bayesian Network is used to perform adaptive trust assessment and the Reinforcement Learning based Proximal Policy Optimization (PPO) for providing dynamic responses to the detected anomalies. The proposed framework is practically implemented and evaluated using IoTID20 and N-BaIoT datasets, and compared with baseline intrusion detection methods including, CNN-TSODE, cuLSTMGRU, ELETL-IDS, Fed-Inforce-Fusion, and Conv-LSTM. The results demonstrate that the proposed framework achieves high efficiency and outperformed other baseline methods by obtaining a detection accuracy of 98.25%, recall of 96.8%, and precision of 97.45%. Overall, the proposed AI-Enhanced Trust Framework offers a promising solution by identifying the intrusion endeavors effectively and contributing toward the attainment of secure and trustworthy IoT ecosystems. [ABSTRACT FROM AUTHOR]

Addressing the issue of information asymmetry in crowdfunding, this paper developed a new model for predicting crowdfunding performance, based on the decision utility rules for processing uncertain information in prospect theory and combining the analysis of crowdfunding project information disclosure with investor utility. To tackle the issue of excessive feature selection in practical applications, it introduced a sparsity-based feature selection method using neural networks, which could help crowdfunding platforms to focus on core features for better understanding and predicting investor behavior. Empirical analysis of over 150 000 projects on the Kickstarter platform shows that models considering investors perception of risk and prospect utility have better predictive and explanatory power for crowdfunding performance. The research results not only provide a new perspective for the prediction and evaluation of crowdfunding projects, but also offer powerful tools for crowdfunding platforms and fundraisers to establish models for analyzing backers backing behavior. [ABSTRACT FROM AUTHOR]

The paper presents a method for semantic data analysis by means of complex-valued matrix decomposition. The method is based on the quantum model of contextual decision-making, according to which observable probabilities are generated by qubit states, representing subjective meaning of the contexts relative to the basis decision. In the simplest three-context case, one of these qubits is decomposed to superposition of the remaining two, mathematically encoding semantic relations between the three contexts. For use in data analysis this model is translated to the matrix form, in which rows and columns correspond to the contexts and instances of experiment. The observable real-valued data then emerge from a complex-valued amplitude matrix, decomposed to a product of a real basis matrix and complex-valued matrix of superposition coefficients. This decomposition reveals stable process-semantic relations between the considered contexts, not captured by other methods of analysis. As a result, the data are approximated with higher precision and fewer parameters than singular and non-negative matrix decompositions, truncated to the same dimension. The model is experimentally approved in descriptive and prognostic regimes. The result opens prospects for development of nature-like computational architectures on novel logical grounds.

Abstract Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design. However, constructing reliable surrogate models faces several challenges, including the nonlinearity of circuit characteristics and the vast size of the parameter space, encompassing both dimensionality and design variable ranges. Additionally, ensuring the validity of the model across broad geometry/material parameter and frequency ranges is crucial for its utility in design. The purpose of this paper is to introduce an innovative approach to cost-effective and dependable behavioral modeling of microwave passives. Central to our method is a fast global sensitivity analysis (FGSA) procedure, which is devised to identify correlations between design parameters and quantify their impacts on circuit characteristics. The most significant directions identified through FGSA are utilized to establish a reduced-dimensionality domain. Within this domain, the model may be constructed using a limited amount of data samples while capturing a significant portion of the circuit response variability, rendering it suitable for design purposes. The outstanding predictive capability of the proposed model, its superiority over traditional techniques, and its readiness for design applications are demonstrated through the analysis of three microstrip circuits of diverse characteristics.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants’ behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants’ clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI. [ABSTRACT FROM AUTHOR]

A novel frequency domain behavioral modeling method for gallium‐nitride (GaN) devices is presented in this article. By utilizing a multi‐dimensional polynomial function, the proposed technique interpolates DC‐bias voltage and temperature based on the Canonical section‐wise piecewise linear (CSWPL) model framework. A detailed description of the model's theory is provided. With data from 10‐W GaN devices, the model was implemented in commercial software and validated through both DC and radio frequency (RF) tests. A robust predictive ability is demonstrated by the obtained results, thus proving the accuracy of the developed modeling method. This model is superior to the standard CSWPL model in that it is capable of predicting transistor behavior at different bias voltages and temperatures using a single set of parameters, thereby greatly reducing the complexity of the model and the time required for extraction. [ABSTRACT FROM AUTHOR]

In this work, a formulation of alternating direction method of multipliers (ADMM) for addressing power amplifiers (PA) modeling and linearization problems is presented. The proposal consists on leveraging the implicit redundancy of the equations in order to achieve a distributed architecture. A detailed theoretical formulation of the method is provided in order to get a better comprehension of its advantages and the approach to integrating the technique in the standard direct learning architecture scheme for digital predistortion. In addition, a discussion on how the implicit regularization helps to deal with numerical problems is presented. It is proven that the implicit regression for the modeling and linearization of PAs can be carried out in a distributed fashion with similar accuracy, enabling the use of resource-constrained devices for digital predistortion. Furthermore, a computational assessment is presented for measuring in terms of arithmetic operations how simpler the devices that implement the proposed ADMM can be, compared with the classical least squares (LS) data-centralized approach. A proof of concept with three scenarios is included: the first two scenarios feature a commercial PA driven by 5G-NR signals with a bandwidth of 30 MHz, and the third scenario involves a commercial Doherty PA with a 100-MHz signal. In a first scenario, experimental results show that an ADMM implementation of a digital predistorter can achieve the same performance as the classical LS solution. The benefits of the proposed regularization are examined by assessing ADMM in a second scenario characterized by significant numerical instabilities. Finally, the robustness of the technique is illustrated through the linearization of the Doherty PA with a 100-MHz OFDM signal.

With the deepening application of artificial intelligence in the field of education, teaching quality evaluation has become the key to improving educational effectiveness. In order to quantify the effectiveness of knowledge transmission and mastery in digital media teaching, this paper proposes an improved Bayesian knowledge tracking model (BF-BKT), which has been specifically optimized for teaching quality evaluation. The motivation stems from the shortcomings of existing technology in evaluating teaching quality, especially the lack of effective integration of student learning behavior and forgetting patterns. This article describes the development process of the BF-BKT model, which innovatively combines learning behavior data and forgetting patterns by introducing behavioral forgetting features to more accurately predict students’ learning status and teaching effectiveness. The BF-BKT model not only considers whether students have mastered a certain knowledge point, but also further analyzes their forgetting situation at different time points, providing a more comprehensive perspective for evaluating teaching quality. To validate the performance of the BF-BKT model, we compared it with the traditional BKT model and two diverse BKT variants. We conducted systematic experiments and analysis by selecting public datasets as simulation scenarios. The experimental results show that the BF-BKT model performs well in predicting students’ knowledge mastery and evaluating teaching quality, and its performance is superior to other comparative methods, proving the effectiveness and superiority of the model.

This paper proposes a novel methodology based on the Genetic Programming (GP) to derive behavioral models describing the transient evolution of the terminal voltage of a battery. These models analytically relate the battery voltage to its state of charge, charge/discharge rate, and temperature. Compared to the popular equivalent circuit-based models, one of the main advantages is the significant reduction of the effort to produce the experimental dataset required to identify the model parameters. The GP generates a family of optimal “candidate” analytical models, each associated with suitable metrics that quantify performance indicators like simplicity and accuracy. The methodology is applied to describe the transient discharge phase of a Lithium Iron Phosphate (LiFePO4 or LFP) battery under realistic operating conditions, considering the state-of-charge between 20% and 80%, discharge rates comprised between 0.25C and 1C, and temperature ranging from 5°C to 35°C. The GP provides different solutions that can be chosen by imposing the desired trade-off between accuracy and simplicity. Two models are selected and validated against experimental results. The chosen models guarantee a quite low level of the relative root mean square error (maximum 0.31% and 0.22%, respectively) over the range of analysis.

It remains important to make abnormity detection from largescale behavioral data of Internet. Existing related approaches mostly failed to employ high-dimensional characteristics of Internet data, which limits the detection effect. To deal with this issue, we introduce graph convolution network (GCN) to generate fine-grained feature representation towards largescale behavioral data. And a deep GCN-based abnormity detection model for largescale behavioral data is proposed in this paper. Firstly, GCN is used to extract global co-occurrence information from largescale behavior data. Then, global embedding is applied to the encoder to obtain local features, which are fused into advanced features to better capture the relationships among nodes in social network. Finally, based on the idea of support vector domain description, a new objective function is optimized to determine whether abnormal behavior occurs in behavior data. Empirically, we have also carried out some experiments to make performance evaluation. The research results indicate that the proposal has higher Precision and robustness compared to traditional methods.

Every engineering process, especially software, involves two complementary aspects: structural and behavioral. Behavior is, in essence, transforming the structure associated with the system. As a language for the object-oriented paradigm, Unified Modeling Language (UML) offers constructs for both aspects, for example, class diagrams for the structural aspect and activity diagrams for the behavioral aspect. However, without obtaining directly executable models, in glass-box terms, or reasoning support, on the other hand, when software engineering is approached with ontologies, only constructs for structural aspects are provided to develop a directly executable model, thanks to their reasoning capability. However, there are no constructs or approaches for this paradigm’s specification or definition of behavior. This lack appears mainly in the early stages of the software engineering process, where there are no constructs similar to, e.g., the activity diagram in the object-oriented domain. Object Management Group (OMG) already addressed the transformation between the two paradigms in structural terms throughout Ontology Definition Metamodel (ODM) from UML to Resource Description Framework (RDF) and Web Ontology Language (OWL). However, there is no transformation of the object-oriented behavioral constructs into ontologies because they are not defined in the ontological paradigm. This paper addresses the definition of behavior in the ontology paradigm and the transformation of behavioral constructs between the two paradigms. The foundation of behavior specification is the flow concept, and the basis of this is the transformation of the structural model in an evaluative sense. Therefore, once the behavior has been defined in the ontology domain, the artifacts obtained throughout the life cycle are directly executable, and their validation and testing are automatic. With this approach, the life cycle is reduced to a modeling process. Thus, the resulting software engineering process improves features such as agility, simplicity, productivity, and formalism. The target audience for this work is the software engineering community, especially in the Model-Driven Engineering (MDE) paradigm approached from object-oriented and ontology perspectives. The evaluation of the proposed approach has been performed in the electric utilities, solving the problem of the validation flow for the interoperability process specified by the Common Grid Model Exchange Standard (CGMES) standard.

With the growing use of multicarrier technology in wireless communication, wideband receivers are facing increasingly severe nonlinear distortion issues. This paper focuses on the behavioral modeling of direct-conversion receivers (DCRs) for nonlinear distortion mitigation. This model accounts for arbitrary-order RF and baseband nonlinearities as well as the I/Q imbalance and can reproduce nonlinear distortion of the wideband DCR with multiple simultaneous input signals. Meanwhile, a method of model parameters estimation is put forward, which involves the inverse function method, singular value decomposition approach, and least square estimation. The experiment is performed by using a real RX chain and different types of input signals to evaluate the proposed model’s accuracy in reproducing nonlinear distortions. The experimental and simulated results suggest that the proposed model can accurately reproduce signal distortions and intermodulation products (IMDs) of DCRs with several input signals of different modulations and power levels.

Memristors are devices built on the basis of fourth passive electrical elements in nanosystems. Because of the multitude of technologies used for memristor implementation, it is not always possible to obtain analytical models of memristors. This difficulty can be overcome using behavioral modeling, which is when mathematical models are constructed according to the input–output relationships on the input and output signals. For memristor modeling, piecewise neural and polynomial models with split signals are proposed. At harmonic input signals of memristors, this study suggests that split signals should be formed using a delay line. This method produces the minimum number of split signals and, as a result, simplifies behavioral models. Simplicity helps reduce the dimension of the nonlinear approximation problem solved in behavioral modeling. Based on the proposed method, the piecewise neural and polynomial models with harmonic input signals were constructed to approximate the transfer characteristic of the memristor, in which the current dynamics are described using the Bernoulli differential equation. It is shown that the piecewise neural model based on the feedforward network ensures higher modeling accuracy at almost the same complexity as the piecewise polynomial model. [ABSTRACT FROM AUTHOR]

Recommendation systems serve as a pivotal solution to address the increasing issue of information overload. While traditional recommendation algorithms have been grounded primarily on user-item interactions, the significance of a user's contextual information influencing decision-making has often been overlooked. Such neglect becomes more evident in the realm of systems integrating contextual mechanisms, which encounter pronounced data sparsity challenges. Existing studies in contextual recommendation systems tend to treat all contextual features uniformly as influencers of user decisions. Yet, a prevalent dilemma is the frequent absence of contextual data, leading to potential misallocations of contextual features. To mitigate these challenges, a novel deep learning-based recommendation system, termed the CAW-NeuMF Model, has been designed. Accompanying this model, a Context-aware Weighted high-order Tensor Factorization algorithm (CAWTF) has been introduced. This algorithm facilitates the calculation of correlations between user ratings in varied contexts, relying on the said context. Additionally, it ascertains the weight of context features grounded on the user ratings correlation. Such a process aids in isolating the most influential contextual features, thereby amplifying the efficiency of personalized recommendations. Empirical evaluations using the LDOS CoMoDa dataset revealed that the proposed model substantially enhances prediction score accuracy. Comparative analyses against alternative recommendation models further affirmed the superior efficacy of the introduced approach. [ABSTRACT FROM AUTHOR]

This paper devises the issue of machine learning rule-based methodology for uncovering the behavior-based rules of respective smart-phone users for purpose of providing context wise individualized notification services. Nowadays, the number of notifications arrived at an inappropriate moment of time or carried unrelated material, which can cause disruption. Previously Rule-based Classifier and Association Rule Mining (ARM) Techniques have been used to solve those problems. However, the classifier approach processes accuracy and reliability problems because of small data instances. ARM creates a vast number of redundant rules, which are pointless for creating context-aware decisions. Redundant rules can make the approach non-efficient and also make the dataset unnecessarily large, making decision-based problems more complicated. For those problems solution in this article, we propose a new Behavioral Adversarial Traversal Tree approach for extracting user behavioral rules with respect to different contexts. A real-world dataset is collected to make this approach more relevant. The Proposed approach effectively identifies and removes the redundant rules with individual behavior-oriented time slots, which are used in the proposed approach to make it more exact and efficient. Our experiments and comparisons on each individual contextual dataset exhibit that the following rule discovery approach is more adequate and more exact for context-aware notification services. [ABSTRACT FROM AUTHOR]

Patient‐reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment decisions. For example, athletes may underreport symptoms to expedite return‐to‐play (RTP) from sports‐related concussion (SRC). Thus, clinicians must implement treatment policies that mitigate the Price of Naïveté, that is, the reduction in health outcomes due to naïvely believing strategically reported symptoms. In this study, we analyze dynamic treatment cessation decisions with strategic patients. Specifically, we formulate the Behavior‐Aware Partially Observable Markov Decision Process (BA‐POMDP), which optimizes the timing of treatment cessation decisions while accounting for known symptom‐reporting behaviors. We then analytically characterize the BA‐POMDP's optimal policy, leading to several practical insights. Next, we formulate the Behavior‐Learning Partially Observable Markov Decision Process (BL‐POMDP), which extends the BA‐POMDP by learning a patient's symptom‐reporting behavior over time. We show that the BL‐POMDP is decomposable into several BA‐POMDPs, allowing us to leverage the BA‐POMDP's structural properties for solving the BL‐POMDP. Then, we apply the BL‐POMDP to RTP from SRC using data from 29 institutions across the United States. We estimate the Price of Naïveté by comparing the BL‐POMDP to naïve benchmark policies. Accordingly, the BL‐POMDP reduces premature RTP by over 44% and provides up to 3.63 additional health‐adjusted athletic exposures per athlete compared to current practice. Overall, changing the interpretation of reported symptoms can better reduce the Price of Naïveté over adjusting treatment cessation thresholds. Therefore, to improve patients' health outcomes, clinicians must understand how strategic behavior manifests in PROs. [ABSTRACT FROM AUTHOR]

Expectations play important roles in consumers' purchase decisions. Among many types of expectations, consumers often form expectations on future market conditions when purchasing goods or services. This study develops a multiple-selves intertemporal choice model for such expectation-based purchase decisions, incorporating behavioral factors such as present-biased preferences into the model. An analysis based on the model shows that consumers adopt a threshold perception-perfect strategy when making purchase decisions and the threshold depends on values of model parameters that capture expectations on key market conditions. Different consumers often have different parameter values, leading to heterogeneous behavior. The study further applies the model to explain observations from medical service consumption data during the COVID-19 pandemic, and shows that the expectation-based purchase model provides a sound explanation for the observed heterogeneous purchase decisions across individuals with different incomes and health insurance status. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

This research encompasses both Process-Voltage-Temperature (PVT) considerations and behavioral modeling of two proposed Power Amplifier (PA) designs for wireless communication systems. Process variations, supply voltage changes, and temperature changes provide difficulties for the design and optimization of power amplifiers for wireless communication systems and may have a substantial influence on their effectiveness. PVT analysis and behavioral modeling have been conducted to address the aforementioned challenges and characterize the behavior and performance of the power amplifiers under real-world operating conditions. It investigates the impact of process variations, supply voltage variations, and temperature variations on the performance of the PAs. The insights gained from this analysis contribute to a deeper understanding of the power amplifiers' performance, efficiency, and suitability for specific wireless communication standards, laying the foundation for future advancements in RF power amplifier design. The first design focuses on a Doherty PA (DPA) tailored for lowpower short-range applications complying with the IEEE Wireless Personal Area Network (WPAN) standard. The second design explores an Envelope Tracking (ET) supply bias control for low-power long-range applications conforming to the IEEE Wireless Local Area Network (WLAN) standard. By examining these factors, the research ensures that the PAs exhibit reliable and optimal performance under real-world operating conditions. The PVT corners show a change in gain of only 0.4 dB for DPA and 0.9 dB for ET PA. Furthermore, behavioral modeling is employed to characterize the power efficiency of the envelope tracking PA, along with the current efficiency and quotient current on the load current scale. These models provide valuable insights into the behavior and performance of the PAs at a higher level of abstraction. The results and findings contribute to a deeper understanding of the PA’s performance, efficiency, and suitability for specific wireless communication standards, laying the foundation for future advancements in RF power amplifier design. [ABSTRACT FROM AUTHOR]

The fourth fundamental circuit element, the memristor, has become a promising candidate to substantially improve the energy and area efficiencies of circuits as traditional complementary metal-oxide-semiconductor (CMOS) technology is approaching its physical limit. However, a mathematical representation of the experimentally obtained current-voltage characteristic of the memristor is necessary to develop and test memristor-based circuitry in electrical design simulators. Here we have developed a behavioral model for the I-V trace of a Pt/TiO2/Pt memristor that can relate the fitting equations with the physical processes associated with the device in response to applied electrical excitation. Multiple conduction mechanisms are involved in memristor that depend upon its latest state. Therefore, the I-V has distinct segments that altogether form a hysteresis loop pinched at the center. In accordance with the predominant conduction mechanisms at each segment, our model defines the form of the equations. The behavioral model can adequately represent the experimental I-V retrieved from existing work. [ABSTRACT FROM AUTHOR]

Animal behavior provides context for understanding disease models and physiology. However, that behavior is often characterized subjectively, creating opportunity for misinterpretation and misunderstanding. For example, spatial alternation tasks are treated as paradigmatic tools for examining memory; however, that link is actually an assumption. To test this assumption, we simulated a reinforcement learning (RL) agent equipped with a perfect memory process. We found that it learns a simple spatial alternation task more slowly and makes different errors than a group of male rats, illustrating that memory alone may not be sufficient to capture the behavior. We demonstrate that incorporating spatial biases permits rapid learning and enables the model to fit rodent behavior accurately. Our results suggest that even simple spatial alternation behaviors reflect multiple cognitive processes that need to be taken into account when studying animal behavior.SIGNIFICANCE STATEMENT Memory is a critical function for cognition whose impairment has significant clinical consequences. Experimental systems aimed at testing various sorts of memory are therefore also central. However, experimental designs to test memory are typically based on intuition about the underlying processes. We tested this using a popular behavioral paradigm: a spatial alternation task. Using behavioral modeling, we show that the straightforward intuition that these tasks just probe spatial memory fails to account for the speed at which rats learn or the types of errors they make. Only when memory-independent dynamic spatial preferences are added can the model learn like the rats. This highlights the importance of respecting the complexity of animal behavior to interpret neural function and validate disease models.

Animal behavior provides context for understanding disease models and physiology. However, that behavior is often characterized subjectively, creating opportunity for misinterpretation and misunderstanding. For example, spatial alternation tasks are treated as paradigmatic tools for examining memory; however, that link is actually an assumption. To test this assumption, we simulated a reinforcement learning (RL) agent equipped with a perfect memory process. We found that it learns a simple spatial alternation task more slowly and makes different errors than a group of male rats, illustrating that memory alone may not be sufficient to capture the behavior. We demonstrate that incorporating spatial biases permits rapid learning and enables the model to fit rodent behavior accurately. Our results suggest that even simple spatial alternation behaviors reflect multiple cognitive processes that need to be taken into account when studying animal behavior.SIGNIFICANCE STATEMENT Memory is a critical function for cognition whose impairment has significant clinical consequences. Experimental systems aimed at testing various sorts of memory are therefore also central. However, experimental designs to test memory are typically based on intuition about the underlying processes. We tested this using a popular behavioral paradigm: a spatial alternation task. Using behavioral modeling, we show that the straightforward intuition that these tasks just probe spatial memory fails to account for the speed at which rats learn or the types of errors they make. Only when memory-independent dynamic spatial preferences are added can the model learn like the rats. This highlights the importance of respecting the complexity of animal behavior to interpret neural function and validate disease models.

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker's strategy and support solutions to achieve this goal.

This paper presents a novel nonlinear behavioral modeling methodology based on long-short-term memory (LSTM) networks for gallium nitride (GaN) high-electron-mobility transistors (HEMTs). There are both theoretical foundations and practical implementations of the modeling procedure provided in this paper. To determine the most appropriate optimizer algorithm for the model presented in this work, four different optimization algorithms are examined. The results of both simulation and experimental validation are provided based on a 10-W GaN HEMT device. According to the developed investigation, the model is capable of extrapolating and interpolating over multiple input power levels and frequencies, including linear, weakly nonlinear, and strongly nonlinear areas. The analysis of the simulated and measured results shows that the developed model has superior performance also when considering the DC drain current (Ids.). Compared with the existing support vector regression (SVR) based model and the Bayesian based model, the proposed approach shows a significantly improved extrapolation capability.

This paper presents the behavioral modeling of three circuit architectures, which are suitable for heartbeat detection in wireless healthcare applications. The developed front-end models implement an analog system that detects the heartbeats for lower power consumption and higher detection accuracy without digital signal processing. Therefore, the circuit blocks and their critical specifications specifically regarding the noise and bandwidth are evaluated. The architectures are implemented in Verilog-A and their behavior simulations use real electrocardiogram (ECG) signals with different characteristics and conditions. All architectures have amplification, band-pass and notch response blocks as their first three stages. Then, the first circuit architecture detects the heartbeat using an adaptive threshold based on a pulse width approach. The second heartbeat detection scheme finds the maximum and minimum values of each beat using a sample-and-hold-based peak detector. The third model uses a similar peak detection scheme, but delays the analog signal for better precision. The simulation results show that all models can detect heartbeats when the input noise is less than $20 \mu V$ , the third having the highest noise tolerance of around $50 \mu V$ . For the ECG signal to maintain its characteristics with respect to its positive and negative peak, the bandpass response should present a low cutoff frequency below 10 Hz.

Summary: A novel dissipativity characterization is proposed for linear time‐invariant circuits and systems whose dynamic behavior depends on one external parameter. The proposed formulation extends to the parameterized case standard Hamiltonian‐based algebraic dissipativity characterizations and is structured as an underdetermined multiparameter eigenvalue problem. Resulting from a polynomial parameterization, the proposed characterization leads to an effective set of algorithms that are able to determine the regions of local dissipativity and local activity in the frequency‐parameter plane, which in turn can be exploited by dissipativity enforcement algorithms to produce uniformly passive models. The reference application of proposed technique is behavioral modeling of complex circuits or systems, whose dynamic behavior can be compressed into a low‐order system through dedicated model order‐reduction processes. Various examples ranging from integrated components to microstrip filters and networks are used to illustrate the proposed characterization. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing, analysis and modeling often cannot be entirely automated. Even machine- or deep learning approaches often depend on a priori expert knowledge and labelling. In this paper, an information-based data preprocessing approach is proposed. By applying statistical methods including variance and correlation analysis, an approximation of the sampling rate in event-based systems and the utilization of spectral analysis, knowledge about the underlying manufacturing processes can be gained prior to modeling. The paper presents, how statistical analysis enables the pruning of a dataset's least important features and how the sampling rate approximation approach sets the base for further data analysis and modeling. The data's underlying periodicity, originating from the cyclic nature of an automated manufacturing process, will be detected by utilizing the fast Fourier transform. This information-based preprocessing method will then be validated for process time series data of cyber-physical systems' programmable logic controllers (PLC). [ABSTRACT FROM AUTHOR]

Memristors are devices built on the basis of fourth passive electrical elements in nanosystems. Because of the multitude of technologies used for memristor implementation, it is not always possible to obtain analytical models of memristors. This difficulty can be overcome using behavioral modeling, which is when mathematical models are constructed according to the input–output relationships on the input and output signals. For memristor modeling, piecewise neural and polynomial models with split signals are proposed. At harmonic input signals of memristors, this study suggests that split signals should be formed using a delay line. This method produces the minimum number of split signals and, as a result, simplifies behavioral models. Simplicity helps reduce the dimension of the nonlinear approximation problem solved in behavioral modeling. Based on the proposed method, the piecewise neural and polynomial models with harmonic input signals were constructed to approximate the transfer characteristic of the memristor, in which the current dynamics are described using the Bernoulli differential equation. It is shown that the piecewise neural model based on the feedforward network ensures higher modeling accuracy at almost the same complexity as the piecewise polynomial model.

To understand the neural mechanisms that support decision making, it is critical to characterize the timescale of evidence evaluation. Recent work has shown that subjects can adaptively adjust the timescale of evidence evaluation across blocks of trials depending on context [1]. However, it's currently unknown if adjustments to evidence evaluation occur online during deliberations based on a single stream of evidence. To examine this question, we employed a change-detection task in which subjects report their level of confidence in judging whether there has been a change in a stochastic auditory stimulus. Using a combination of psychophysical reverse correlation analyses and single-trial behavioral modeling, we compared the time period over which sensory information has leverage on detection report choices versus confidence. We demonstrate that the length of this period differs on separate sets of trials based on what's being reported. Surprisingly, confidence judgments on trials with no detection report are influenced by evidence occurring earlier than the time period of influence for detection reports. Our findings call into question models of decision formation involving static parameters that yield a singular timescale of evidence evaluation and instead suggest that the brain represents and utilizes multiple timescales of evidence evaluation during deliberation.

Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (n = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an "induction" task which provided feedback with a low (p = 0.58), a medium (p = 0.75), or a high probability of reward (p = 0.92), while the impact of ARR on reinforcement learning was tested by a second "reference" task with a constant reward probability (p = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction. [ABSTRACT FROM AUTHOR]

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R 2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved. [ABSTRACT FROM AUTHOR]

In this article, we present a novel decomposed vector rotation (DVR)-based recurrent neural network behavioral model for digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs) in wideband scenarios. By representing memory terms of DVR with recurrent states and redesigning the piecewise modeling, we propose a novel recurrent DVR scheme. To ensure stable operation and enhanced modeling accuracy, we integrate the recurrent DVR into the gated learning mechanism of the modified Just Another NETwork (JANET). Experimental results confirm that the proposed DVR-JANET model provides much improved linearization performance with significantly reduced model complexity compared with the recent existing models. [ABSTRACT FROM AUTHOR]

This work advances the application of neural ordinary differential equations (ODEs) to circuit modeling. Prior works primarily utilized the recurrent neural network (RNN), which is a specific type of neural ODE. In this work, the capability of neural ODEs to represent different types of circuits is studied. Stability conditions are presented, both for neural ODEs in a standalone configuration and for neural ODEs with feedback connections, and practical techniques to impose the stability constraints during training are demonstrated. Based on the theoretical and experimental results, this work provides guidance as to when and how an accurate and stable neural ODE circuit model can be generated. [ABSTRACT FROM AUTHOR]

In this letter, two basis function multiplexing-based behavioral modeling methods for digital predistortion (DPD) of RF power amplifiers (PAs) are proposed to reduce the running complexity of DPD. The proposed full basis-propagating selection (FBPS) model and reduced-complexity FBPS (RC-FBPS) model give two reasonable ways to multiplex even-order basis functions, extending the basis-propagating selection (BAPS) model which only uses basis function delay and odd-order basis functions. The experimental results confirm that both the proposed FBPS and RC-FBPS models can achieve a good tradeoff between running complexity and performance. [ABSTRACT FROM AUTHOR]

To improve the modeling accuracy and digital predistortion compensation effect, this paper proposed a kind of polynomial based spline adaptive filter (SAF) power amplifier (PA) behavioral modeling method. The proposed model can solve the shortcomings of the traditional memory polynomial model (MP), such as higher nonlinear order, highly coefficient calculation difficulty, poor hardware stability, low modeling accuracy and poor digital predistortion compensation. Meanwhile, the proposed SAF based model can avoid the pseudo-inversion operation of the matrix when calculating the coefficients through the indirect learning structure, which can improve the compensation effect of digital predistortion. In order to compare the performance of the different models, the modeling processes of original filtering model, MP model and dynamic variation reduction (Dynamic Deviation Reduction, DDR) model based on SAF method (OSAF, MPSAF, DDRSAF) are presented respectively. The proposed models are verified by a class F PA, which are stimulated by a set of measured input and output data of an LTE signal with 20 MHz bandwidth. The measured results shows that compared with the conventional MP model, the proposed DDRSAF model presents the best model performance and improves the modeling accuracy by 6.6dB while maintaining the similar computational complexity, and the ACPR compensation effect increases by 19.58dB and 20.47dB respectively, which have the important significance for practical engineering application.

The design and optimization of coils for Inductive Power Transfer (IPT) systems is an iterative process conducted in Finite Element (FE) tools that takes a lot of time and computational resources. In order to overcome such limitations in the design process, new empirical equations for the evaluation of the self-inductance and mutual inductance values are proposed in this work. By means of a multi-objective genetic programming algorithm, the self-inductance, the mutual inductance and the coupling factor values obtained from FE simulations of IPT link are accounted by analytical equations, based on the geometric parameters defining the IPT link. The behavioral modeling results are compared with both FE-based and experimental results, showing a good accuracy.

We present a behavioral compact model and its implementation for low-voltage pentacene-based organic thin film transistors (OTFTs) using industry standard BSIM4 (Berkeley Short-channel IGFET Model) in LTspice platform. First, we simplify the number of BSIM4 model parameters according to the device structure. On the other hand, we adapt the BSIM4 mobility equation to OTFT and also involve the off-current using two behavioral current sources. Then, we perform metaheuristics-based optimization to extract the related BSIM4 model parameters together with additional four parameters, introduced in this work, fitting the modified model to the experimental data of an OTFT, using pentacene active layer. Finally, we implement the behavioral compact model in LTspice and validate its usage for low-voltage pentacene-based OTFTs comparing the experimental and modeled current–voltage curves. In this study, we also successfully simulate OTFT implementations of a Pseudo-E inverter, a Pseudo-E NAND gate, and a 1-bit full adder circuit based on NAND gates using our behavioral OTFT model and then investigate the static and dynamic performance parameters of these circuits. The supply voltage is 3 V and maximum frequency of the inputs is 500 Hz in the simulations.

Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (n = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an “induction” task which provided feedback with a low (p = 0.58), a medium (p = 0.75), or a high probability of reward (p = 0.92), while the impact of ARR on reinforcement learning was tested by a second “reference” task with a constant reward probability (p = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction.

A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities. [ABSTRACT FROM AUTHOR]

In this article, a sparse-Bayesian treatment is proposed to solve the crucial questions posed by power amplifier (PA) and digital predistorter (DPD) modeling. To learn a model, the advanced Bayesian framework includes a group of specific processes that maximize the likelihood of the measured data: regressor pursuit and identification, coefficient estimation, stopping criterion, and regressor deselection. The relevance vector machine (RVM) method is reformulated theoretically to be implemented in complex-valued linear regression. In essence, given an initial set of candidate regressors, the result of this sparse-Bayesian learning approach is the most likely model. Experimental results are provided for the linearization of class AB and class J PAs driven by a 30-MHz fifth-generation new radio signal for a fixed average power, where the evolution of the figures of merit versus the number of active coefficients is examined for the proposed sparse-Bayesian pursuit (SBP) algorithm in comparison to other greedy algorithms. The SBP presents a good performance in terms of linearization capabilities and computational cost. Furthermore, the proposed Bayesian framework enabled the design of a DPD model structure, deselect regressors, and readjust coefficients in a direct learning architecture, demonstrating the robustness to changes in the power level over a 10-dB range. [ABSTRACT FROM AUTHOR]

This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residual-fitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR). [ABSTRACT FROM AUTHOR]

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle. [ABSTRACT FROM AUTHOR]