Implicit Parameters Research Articles

Analysis of multi-term arbitrary order implicit differential equations with variable type delay

Due to their capacity to simulate intricate dynamic systems containing memory effects and non-local interactions, fractional differential equations have attracted a great deal of attention lately. This study examines multi-term fractional differential equations with variable type delay with the goal of illuminating their complex dynamics and analytical characteristics. The introduction to fractional calculus and the justification for its use in many scientific and technical domains sets the stage for the remainder of the essay. It describes the importance of including variable type delay in differential equations and then applying it to model more sophisticated and realistic behaviours of real-world phenomena. The research study then presents the mathematical formulation of variable type delay and multi-term fractional differential equations. The system’s novelty stems from its unique combination of variable delay, generalized multi terms fractional differential operators (n and m), and integral implicit parameters, and studying the stability of the the newly formulated system as compared to the work in the existing literature. While the variable type delay is introduced as a function of time to describe instances where the delay is not constant, the fractional order derivatives are generated using the Caputo approach. The existence, uniqueness, and stability of solutions are the main topics of the theoretical analysis of the suggested differential equations. In order to establish important mathematical features, the inquiry makes use of spectral techniques, and fixed-point theorems. The study finishes by summarizing the major discoveries and outlining potential future research avenues in this developing field. It highlights the potential contribution of multi-term fractional differential equations with variable type delay to improving the control and design of complex systems. Overall, this study adds to the growing body of knowledge in the field of fractional calculus and provides insightful information about the investigation of multi-term fractional differential equations with variable type delay, making it pertinent for academics and practitioners from a variety of fields.

Estimation of economic losses due to leptospirosis in dairy cattle

There are few studies that effectively quantify the economic losses resulting from problems caused by leptospirosis in naturally infected dairy cattle. Given this gap, the objective of this study was to propose and apply a method to quantify the economic losses resulting from productive and reproductive problems in a commercial dairy herd naturally infected by Leptospira spp. For this study, the zootechnical and economic indicators at a property with Jersey cattle were analyzed during the period from 2014 to 2017. The leptospirosis outbreak occurred in 2014, and the therapeutic approach was carried out between 2015 and 2017, with the latter considered the year of control of the outbreak. The adopted integrated control strategy consisted of dividing the herd according to the serological results obtained through the microscopic agglutination test, the treatment of reagents with streptomycin, and vaccination against leptospirosis of non-reagent heifers and cows. The method used to evaluate the economic indicators of the property was the calculation of the gross margin by taking into account the implicit and explicit cost parameters associated with the manifestation of leptospirosis. The prevalence rate of leptospirosis decreased from 49.4 % in 2015 to 21.6 % in 2017. There was a reduction in the abortion rate (from 40.00 % in 2014 to 9.00 % in 2017), in the stillborn rate (from 2.63 % in 2014 to 1.69 % in 2017) and an increase in the calving rate (from 65.00 % in 2014 to 86.00 % in 2017). In addition, there were increases in the number of lactating cows (from 38 in 2014–57 in 2017) and the mean times of lactation duration, which increased from 275 days in 2014–295 days in 2017. As a result, the average annual production of milk increased from 164,655 liters in 2014–248,521 liters in 2017. In 2014, when treatment hadn’t yet started, the gross margin per liter of milk sold, considering implicit and explicit costs, was US$0.00. In 2015 and 2016, US$0.27 and US$0.30 were obtained, respectively, for this variable. In 2017, with the disease under control on the property, the gross margin per liter of milk reached US$0.36. The gross margin per liter of milk sold was higher in the period when the disease was controlled, showing losses of up to 84 % of the gross margin during the outbreak. Immediate treatment of positive cows and preventive measures had a significant impact on improving the productive and economic efficiency of the property.

A gradient-based calibration method for the Heston model

The Heston model is a well-known two-dimensional financial model. Because the Heston model contains implicit parameters that cannot be determined directly from real market data, calibrating the parameters to real market data is challenging. In addition, some of the parameters in the model are non-linear, which makes it difficult to find the global minimum of the optimization problem within the calibration. In this paper, we present a first step towards a novel space mapping approach for parameter calibration of the Heston model. Since the space mapping approach requires an optimization algorithm, we focus on deriving a gradient descent algorithm. To this end, we determine the formal adjoint of the Heston PDE, which is then used to update the Heston parameters. Since the methods are similar, we consider a variation of constant and time-dependent parameter sets. Numerical results show that our calibration of the Heston PDE works well for the various challenges in the calibration process and meets the requirements for later incorporation into the space mapping approach. Since the model and the algorithm are well known, this work is formulated as a proof of concept.

Implicit Posteriori Parameter Distribution Optimization in Reinforcement Learning.

Efficient and intelligent exploration remains a major challenge in the field of deep reinforcement learning (DRL). Bayesian inference with a distributional representation is usually an effective way to improve the exploration ability of the RL agent. However, when optimizing Bayesian neural networks (BNNs), most algorithms need to specify an explicit parameter distribution such as a multivariate Gaussian distribution. This may reduce the flexibility of model representation and affect the algorithm performance. Therefore, to improve sample efficiency and exploration based on Bayesian methods, we propose a novel implicit posteriori parameter distribution optimization (IPPDO) algorithm. First, we adopt a distributional perspective on the parameter and model it with an implicit distribution, which is approximated by generative models. Each model corresponds to a learned latent space, providing structured stochasticity for each layer in the network. Next, to make it possible to optimize an implicit posteriori parameter distribution, we build an energy-based model (EBM) with value function to represent the implicit distribution which is not constrained by any analytic density function. Then, we design a training algorithm based on amortized Stein variational gradient descent (SVGD) to improve the model learning efficiency. We compare IPPDO with other prevailing DRL algorithms on the OpenAI Gym, MuJoCo, and Box2D platforms. Experiments on various tasks demonstrate that the proposed algorithm can represent the parameter uncertainty implicitly for a learned policy and can consistently outperform competing approaches.

High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm

Overparameterization often leads to benign overfitting, where deep neural networks can be trained to overfit the training data but still generalize well on unseen data. However, it lacks a generalized asymptotic framework for nonlinear regressions and connections to conventional complexity notions. In this paper, we propose a generalized high-dimensional analysis for nonlinear regression models, including various nonlinear feature mapping methods and subsampling. Specifically, we first provide an implicit regularization parameter and asymptotic equivalents related to a classical complexity notion, i.e., effective dimension. We then present a high-dimensional analysis for nonlinear ridge regression and extend it to ridgeless regression in the under-parameterized and over-parameterized regimes, respectively. We find that the limiting risks decrease with the effective dimension. Motivated by these theoretical findings, we propose an algorithm, namely RFRed, to improve generalization ability. Finally, we validate our theoretical findings and the proposed algorithm through several experiments.

The application of tennis teaching in computer network environment

Abstract In this paper, students' implicit learning characteristics parameters are first transformed using BP neural network. Then the parameter weights are corrected using the activation function and normalized to obtain explicit data characteristics. Finally, a tennis online teaching platform is constructed by measuring the correlation between learning behavior data through the K-mean clustering algorithm and extracting the learning behavior regularity coefficients accurately using time series. The findings revealed that the platform had an average pass rate of 78.6% for students, and the average learning time was 6.5 hours. The teaching quality of tennis can be effectively improved in the computer network environment.

A fast-response-generation method for single-layer reticulated shells based on implicit parameter model of generative adversarial networks

A fast-response-generation method for single-layer reticulated shells based on implicit parameter model of generative adversarial networks

An Interpretable Method for Operational Modal Analysis in Time-Frequency Representation and Its Applications to Railway Sleepers

Operational modal analysis (OMA) enables the identification of modal characteristics under operational loads and conditions. Traditional frequency-domain methods cannot directly capture modal changes over time, while existing time-frequency representations are not sufficiently interpretable due to spurious modes and implicit parameter design. This paper develops a new OMA method in time-frequency representation based on frequency-domain decomposition (FDD). Short-time FDD and a convolution-based strategy are proposed to obtain singular values and local mode shape similarity, respectively, which are further fused into mode indicators by a fuzzy-based strategy mimicking the modal assurance criterion. The method provides not only a global view of the modal characteristics over time and frequency but also estimates of the modal parameters. It is applicable to strongly nonstationary responses under time-varying loads and conditions. All the parameters explicitly affect the time-frequency representation, and the interpretability is enhanced by including physical information from the user’s prior knowledge in selecting parameters and peak bands. The proposed method is validated based on a study of railway sleepers under train passage. The rigid-body motions and bending modes are identified at frequencies up to 6,500 Hz in laboratory tests and 4,500 Hz in field tests at speeds up to 200 km/h. The identified natural frequencies and mode shapes agree with experimental modal analysis (EMA). The proposed method outperforms EMA in terms of broad frequency range and low measurement cost and can be potentially applied to structural health monitoring under operational conditions.

Text and Context in Multimedia Translation

Multimedia translation refers to the process of translating multimedia content, such as videos, audio, and images, from one language to another. This type of translation is becoming increasingly important as more and more content is being produced and distributed globally. Multimedia translation is a complex field that requires in-depth technical and linguistic expertise and a thorough knowledge of the culture and communication norms of the target language. Plurisemiotic and micro-textual analyses of translations can help to develop an effective translation grid. The aim of this article would be, on one side, to restore certain implicit and explicit parameters of a silent source text so that a multimedia translation faithfully communicates the original and multidimensional meaning of a source text. On the other side, this article will try to demonstrate the usefulness of applying some concepts from a branch of socio-semiotics concerned with the production of multimodal texts that can be easily incorporated into existing methods in translation studies. As technology continues to evolve, we can expect multimedia translation to become even more sophisticated and effective. We will try to show in this article that the real context occupies a large place in which the work of translating audiovisual programs is carried out while presenting the various challenges of audiovisual translation, which is part of an extremely dynamic field that combines theory and practical. The aim of this article is to attempt to provide answers to these questions due to the increasing complexity of textual concepts and text production today. Translation and text production sometimes become almost indistinguishable, which will increase the emphasis on text analysis and different forms of intertextuality rather than on translation in the strictest sense.

Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

Wald, QLR, and score tests when parameters are subject to linear inequality constraints

This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter space is characterized by a finite number of linear equality and inequality constraints. We show that the asymptotic null distributions of the Wald and QLR statistics are discontinuous in an implicit nuisance parameter and propose an algorithm to identify it. In contrast, the asymptotic null distribution of the score statistic is not discontinuous in any model parameter but depends on a polytope projection. We present an algorithm based on the Fourier–Motzkin elimination to compute such a projection. We study the consistency and local power properties of the three tests. Finally, we present numerical results of our tests’ finite sample performance from a Monte Carlo study and conduct an empirical illustration of a Mincer earnings regression.

A Study in Reproducibility: The Congruent Matching Cells Algorithm and cmcR Package

Scientific research is driven by our ability to use methods, procedures, and materials from previous studies and further research by adding to it. As the need for computationally-intensive methods to analyze large amounts of data grows, the criteria needed to achieve reproducibility, specifically computational reproducibility, have become more sophisticated. In general, prosaic descriptions of algorithms are not detailed or precise enough to ensure complete reproducibility of a method. Results may be sensitive to conditions not commonly specified in written-word descriptions such as implicit parameter settings or the programming language used. To achieve true computational reproducibility, it is necessary to provide all intermediate data and code used to produce published results. In this paper, we consider a class of algorithms developed to perform firearm evidence identification on cartridge case evidence known as the *Congruent Matching Cells* (CMC) methods. To date, these algorithms have been published as textual descriptions only. We introduce the first open-source implementation of the Congruent Matching Cells methods in the R package cmcR. We have structured the cmcR package as a set of sequential, modularized functions intended to ease the process of parameter experimentation. We use cmcR and a novel variance ratio statistic to explore the CMC methodology and demonstrate how to fill in the gaps when provided with computationally ambiguous descriptions of algorithms.

Multistep Ahead Prediction of Vehicle Lateral Dynamics Based on Echo State Model

Lateral dynamics are critical for high-level autonomous driving, and multistep forecasting can significantly improve vehicle safety. However, the dynamics prediction method is rarely announced due to the difficulty of accurate prediction. In contrast to conventional time-series prediction, this article proposes a novel method for predicting a dynamical system with implicit parameters. Specifically, we construct an echo state model (ESM) incorporating a rational data organization and an efficient neural network structure. We observe the fading information property (FIP) of dynamic vehicle parameters and use temporal data to construct equivalent states for implicit parameters. Finally, a gated recurrent unit (GRU) neural network is designed and trained to accomplish the complex mapping of a vehicle dynamical system. Its performance is compared to that of several benchmark networks. The proposed method is capable of performing four-step-ahead prediction with high accuracy in both dry and slippery road conditions, implying the increased potential for some safe and comfortable driving applications of autonomous vehicles.

Probabilistic performance assessment of eccentric braced frames using artificial neural networks combined with correlation latin hypercube sampling

Hysteretic behavior of shear links has a vital role in seismic responses of Eccentric Braced Frames (EBFs). So, their behavioral parameters have been suggested recently in order to predict their hysteresis behavior. For shear links, modeling variables have been defined through seven implicit parameters all of which having specific Coefficient of Variation (COV), and variation in these parameters may affect the structural responses. However, the effect of uncertainties in these parameters on the structural responses has not been yet addressed. In this paper, variations of seismic structural responses due to the variations in the behavioral parameters of shear links have been investigated while trying to maintain a balance between the accuracy of predicted responses and the required computational effort. For this purpose, a 4-story EBF system has been modeled and studied. Incremental Dynamic Analysis (IDA) has been employed in order to attain the seismic responses while the spectral acceleration corresponding to different performance levels (Sa(T1,5%)) is used as the Intensity Measure (IM). A sensitivity analysis is carried out to rank the modeling parameters by their effect on seismic responses of the system in two performance levels. Then, Correlation Latin Hypercube Sampling (CLHS) method has been used to propagate the uncertainties of important modeling parameters into the structural responses of the frame. In the next step, Function Approximation (FA) with Artificial Neural Networks (ANNs) is used as a practical method to approximate the structural responses with lower computational effort. Screening design has been employed to consider the most optimum input data to train the network. ANNs in conjunction with CLHS provides a very efficient tool to address the effect of uncertainties in structural responses requiring lower computational effort. It has been found that web thickness and yielding strength of shear link play a key role in variation of seismic structural responses for the model studied. Moreover, incorporating modeling uncertainties may shift the median value of the probability distribution for structural responses.

Deconstruction and reconstruction of heat driven absorption refrigeration and absorption power cycle with 3-D methods

Advanced absorption cycles, such as the generator-absorber heat exchange (GAX) absorption refrigeration cycle and Honigmann power cycle, are not easy to be intuitively expressed in traditional T-s or P-h diagram, due to their complex heat and mass coupling relationship. With 3-dimensions (3D) methods, additional information can be intuitively expressed which can motivate researchers to propose creative and novel advanced absorption cycles with complex internal heat and mass coupling. In this study, a classic high-performance branched GAX (b-GAX) was graphicly analyzed by introducing 3D methods. The b-GAX cycle was deconstructed in 3D space and reconstructed by projecting the cycle to traditional P-T and P-X plates. Implicit parameters like intermediate concentration and deflation ratio were exhibited intuitively. The performance of the system has a better linear correlation with these new parameters than operation parameters. The Honigmann progress was also graphicly expressed with the 3D methods. By extracting implicit parameters, the new method is expected to play a role in helping to understand complex thermodynamic cycles and propose advanced high-performance cycles.

Stiffness and coherence length measurements of ultra-thin superconductors, and implications for layered superconductors

Based on the London equation, we use a rotor-free vector potential A and current measurements by a SQUID, to determine the superconducting (SC) Pearl length Λ and coherence length ξ 0, of ultra-thin, ring shaped, MoSi films, as a function of thickness d and temperature T. We find that T c and ξ 0 are functions of d and both jump at nm. At base temperature the SC stiffness, defined by , is an increasing function of T c . Similar behavior, known as the Uemura plot, exists in bulk layered superconductors, but with doping as an implicit parameter. We also provide the critical exponents of .

Learned self-regulation in top-level managers through neurobiofeedback training improves decision making under stress

Top-level management teams are particularly exposed to stress factors as they frequently have to make important decision under stress. While an existing body of research evidence suggests that stress negatively affects decision-making processes, very little is known about possible strategies to reduce these negative effects. The aim of the current work is to investigate the effect of training self-regulation ability through neurobiofeedback on managers’ intertemporal and risky decision making. Twenty-three managers were assigned to the experimental or the control condition. All participants performed, two decisional tasks, before and after a training phase. The tasks were administered through mouse tracker software, in order to measure participants’ delay discounting and risk taking propensity on both explicit and implicit choice parameters. During the training phase, the experimental condition received a training protocol based on stress assessment tests via neurobiofeedback signals (i.e., temperature and skin conductance), with the goal of improving self-regulation ability while the control condition was administered a control training. The main result of this study is to have conclusively demonstrated that NBF training increases an individual's ability to self-regulate stress-related psychophysiological phenomena. Consequently, the improved ability to manage one's own reaction to stress enables a reduction in instinctive behavior during a probabilistic choice task.

Periodic Hamiltonian systems in shape optimization problems with Neumann boundary conditions

The recent implicit parametrization theorem, based on simple Hamiltonian systems, allows the description of domains and their boundaries and, consequently, it provides a general fixed domain approximation method in shape optimization problems, using optimal control theory. Here, we discuss topology and shape optimization in the difficult case of Neumann boundary conditions, with a combined cost including both distributed and boundary observation. We give an unexpected general equivalence property with constrained optimal control problems, preserving differentiability. An important new ingredient in the arguments is the differentiability of the period for the Hamiltonian systems, with respect to functional variations. Due to the differentiability properties, we can use descent algorithms of gradient type. In the experiments, our approach can modify the topology both by closing holes or by creating new holes. We underline the applicability of this new methodology to large classes of shape optimization problems.

Methods for Solving Finite Element Mesh-Dependency Problems in Geotechnical Engineering—A Review

The instabilities of soil specimens in laboratory or soil made geotechnical structures in field are always numerically simulated by the classical continuum mechanics-based constitutive models with finite element method. However, finite element mesh dependency problems are inevitably encountered when the strain localized failure occurs especially in the post-bifurcation regime. In this paper, an attempt is made to summarize several main numerical regularization techniques used in alleviating the mesh dependency problems, i.e., viscosity theory, nonlocal theory, high-order gradient and micropolar theory. Their fundamentals as well as the advantages and limitations are presented, based on which the combinations of two or more regularization techniques are also suggested. For all the regularization techniques, at least one implicit or explicit parameter with length scale is necessary to preserve the ellipticity of the partial differential governing equations. It is worth noting that, however, the physical meanings and their relations between the length parameters in different regularization techniques are still an open question, and need to be further studied. Therefore, the micropolar theory or its combinations with other numerical methods are promising in the future.

Research artifacts and citations in computer systems papers.

Research in computer systems often involves the engineering, implementation, and measurement of complex systems software and data. The availability of these artifacts is critical to the reproducibility and replicability of the research results, because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research article itself. Artifact availability has also been previously associated with higher paper impact, as measured by citations counts. And yet, the sharing of research artifacts is still not as common as warranted by its importance. The primary goal of this study is to provide an exploratory statistical analysis of the artifact-sharing rates and associated factors in the research field of computer systems. To this end, we explore a cross-sectional dataset of papers from 56 contemporaneous systems conferences. In addition to extensive data on the conferences, papers, and authors, this analyze dataset includes data on the release, ongoing availability, badging, and locations of research artifacts. We combine this manually curated dataset with citation counts to evaluate the relationships between different artifact properties and citation metrics. Additionally, we revisit previous observations from other fields on the relationships between artifact properties and various other characteristics of papers, authors, and venue and apply them to this field. The overall rate of artifact sharing we find in this dataset is approximately 30%, although it varies significantly with paper, author, and conference factors, and it is closer to 43% for conferences that actively evaluated artifact sharing. Approximately 20% of all shared artifacts are no longer accessible four years after publications, predominately when hosted on personal and academic websites. Our main finding is that papers with shared artifacts averaged approximately 75% more citations than papers with none. Even after controlling for numerous confounding covariates, the release of an artifact appears to increase the citations of a systems paper by some 34%. This metric is further boosted by the open availability of the paper’s text.