Implicit System Research Articles

An implicit memory of errors limits human sensorimotor adaptation.

During extended motor adaptation, learning appears to saturate despite persistence of residual errors. This adaptation limit is not fixed but varies with perturbation variance; when variance is high, residual errors become larger. These changes in total adaptation could relate to either implicit or explicit learning systems. Here, we found that when adaptation relied solely on the explicit system, residual errors disappeared and learning was unaltered by perturbation variability. In contrast, when learning depended entirely, or in part, on implicit learning, residual errors reappeared. Total implicit adaptation decreased in the high-variance environment due to changes in error sensitivity, not in forgetting. These observations suggest a model in which the implicit system becomes more sensitive to errors when they occur in a consistent direction. Thus, residual errors in motor adaptation are at least in part caused by an implicit learning system that modulates its error sensitivity in response to the consistency of past errors.

From species sorting to mass effects: spatial network structure mediates the shift between metacommunity archetypes

Local assemblages are embedded in networks of communities connected by dispersal, and understanding the processes that mediate this local–regional interaction is central to understanding biodiversity patterns. In this network (i.e. metacommunity), the strength of dispersal relative to the intensity of environmental selection typically determines whether local communities are comprised of species well‐adapted to the local environment (i.e. species sorting) or are dominated by regionally successful species that may not be locally adapted (i.e. mass effects), which by extension determines the capacity of the landscape to sustain diversity. Despite the fundamentally spatial nature of these dispersal‐mediated processes, much of our theoretical understanding comes from spatially implicit systems, a special case of spatial structure in which patches are all connected to each other equally. In many real systems, both the connections among patches (i.e. network topology) and the distributions of environments across patches (i.e. spatial autocorrelation) are not arranged uniformly. Here, we use a metacommunity model to investigate how spatial heterogeneities may change the balance between species sorting versus mass effects and diversity outcomes. Our simulations show that, in general, the spatially implicit model generates an outlier in biodiversity patterns compared to other networks, and most likely amplifies mass effects relative to species sorting. Network topology has a strong effect on metacommunity outcome, with topologies of sparse connections and few loops promoting sorting of species into suitable patches. Spatial autocorrelation is another key factor; by interacting with spatial topology, intermediate‐scale clusters of similar patches can emerge, leading to a reduction of regional competition, and hence maintenance of gamma diversity. These results provide a better understanding of the role that complex spatial landscape structure plays in metacommunity processes, a necessary step to understanding how metacommunity processes relate to biodiversity conservation.

Generalised Anxiety Disorder and Depression on Implicit and Explicit Trust Tendencies Toward Automated Systems

This paper explores whether generalised anxiety disorder (GAD) and depression have any effect on an individual’s explicit general propensity to trust automated systems (trust that is unspecific to any one automated system) and whether those that do have these disorders have an implicit bias towards automated systems over other humans. The human-automated system literature to date has discovered that individual differences in humans, such as self-confidence, mood, and personality types, can influence the human-automated system relationship through human trust and reliance attitudes and behaviour. However, whether suffering from a mental disorder influences an individual’s attitudes towards automated systems generally is yet to be explored. In this study, 184 UK university students responded to online experiments between December 2019 – January 2020 and were subjected to the cultural trust instrument survey and the implicit association test in a between-subjects design to measure their general propensity to trust and implicit association towards automated systems respectively. A two-way ANOVA was performed to evaluate GAD $\times $ depression interaction effects on the dependent variables. Results suggest there was a significant interaction between GAD and depression regarding propensity to trust automated systems but they have little to no influential effect on mean implicit association test scores. Furthermore, those without depression showed a significantly higher trust score when they also had GAD. It can be concluded that GAD and depression have potential critical influence over human-automated system trust, thus creating potential issues with misuse and disuse and must be accounted for in automated system design.

Attachment-Based Therapy for Addiction and Trauma in Children and Adolescents

This article is about why children and adolescents with traumatic experiences are particularly at risk of becoming severely addicted. It addresses the question of why it is so difficult for them to feel safe in therapeutic relationships. It will be examined to what extent the epidemiology of addiction, trauma, and attachment disorder provides evidence as to whether there is a significant relationship among the disorders that contributes to the complicated and lengthy nature of therapy for adolescents who have become severely addicted. It will describe how the reward system, attachment system, and anxiety-stress system (limbic system) interact at the neurobiological level. As a result of the study, it is shown that the role of the reward system in understanding the development of dependence cannot be understood neurophysiologically without the role of attachment and anxiety-stress. One conclusion from this is that this relationship should be taken into account therapeutically in order to be able to expand the spectrum of therapeutic intervention options and to be able to make the therapy of severe dependence disorders in adolescence more successful. Methodologically, two levels of description are compared here: On the first level, the neurobiological research findings are presented as the connection between addiction, trauma and attachment; on the second level, the significance of the research findings for therapeutic practice is concretely described and empirically demonstrated on the basis of a case report. In conclusion, it is shown that understanding the neurobiological function of the implicit memory systems of reward, attachment, and anxiety/stress has a central importance for the further development of addiction therapy for dependent adolescents.

On implicit coupled systems of fuzzy fractional delay differential equations with triangular fuzzy functions

<abstract> In this paper, we introduce and study an implicit coupled system of fuzzy fractional delay differential equations involving fuzzy initial values and fuzzy source functions of triangular type. We assume that these initial values and source functions are triangular fuzzy functions and define the solution of the implicit coupled system as a triangular fuzzy function matrix consisting of real functional matrices. The method of triangular fuzzy function, fractional steps and fuzzy terms separation are used to solve the implicit coupled systems. Further, we prove existence and uniqueness of solution for the considered systems, and also construct a solution algorithm. Finally, an example is given to illustrate our main results and some further work are presented. </abstract>

A Deep Learning Approach to Subject Identification Based on Walking Patterns

For the time being, smartphone devices rely on direct interaction from the users for unlocking and authentication purposes through implicit authentication systems such as PINs, facial recognition or fingerprint scanning. While different passive two-factor authentication systems based on machine learning were explored in recent work, all require an implicit authentication system. In this study, the focus is to develop and introduce a passive authentication system based on walking patterns. In this scenario, the authentication system continuously authenticates the user in the background, without any further action. To the best of our knowledge, this is the first study in which the data sets are processed with the aim to generate better performing gait-based motion signals. Compared to previous studied work, we employ a processing stage in which we extract tiny frames of data from the motion signals. Our contribution of processing gait data, allows for more robust learning of the subject movement and lowers the number of samples required to classify a user thereafter. Hence, our approach is more robust compared to using raw gait signals. Further, we transform them into gray-scale images for deep neural network training and feature extraction. Conducting the experiments, the empirical results demonstrate that subjects can be identified with a very high accuracy through walking patterns employing the presented techniques. Empirical results outline that a system based on gait data can be utilized as a passive authentication system. Therefore, it is concluded that deep neural networks employing the technique described in this work for gait-based feature representation are well suited for continuous and unobtrusive authentication systems.

Caputo type modification of the Erdélyi-Kober coupled implicit fractional differential systems with retardation and anticipation

Caputo type modification of the Erdélyi-Kober coupled implicit fractional differential systems with retardation and anticipation

Explicit and implicit Valuation-Based System methods for the risk assessment of systems subject to common-cause failures under uncertainty

Multiple components within a system may fail or malfunction simultaneously due to a shared cause or common cause (CC). This kind of failure is referred to as a common-cause failure (CCF), and it contributes greatly to the system failure. Due to the insufficiency of relative data and system complexities, different kinds of uncertainties inevitably exist in real-world system modeling. This paper proposes explicit and implicit Valuation-Based System (VBS) methods to model systems subject to CCFs, and to evaluate the failure probability of such systems considering the parametric uncertainty related to state probabilities of components and the model uncertainty related to the system structure. Both methods are suitable to model systems subject to CCFs under different kinds of uncertainties, and allow the relationship among multiple CCs being s-independent, s-dependent or mutually exclusive. Finally, the proposed methods are applied to model hazardous material transportation accidents and to evaluate the occurrence probability of accidents using statistical data.

The Dual Process of Intuitive Responsivity and Reflective Self-supervision: About the Therapist in Family Therapy Practice.

Psychotherapy research shows that-in individual therapy as in family therapy-some therapists are more effective than others. This highlights the crucial role the therapist plays in a client's improvement. Furthermore, it seems that training may make a difference, as deliberate practice can improve the therapist's effectiveness. In the context of the current renewed awareness of the importance of the person of the therapist in the psychotherapy field, this paper's focus is specifically on the therapist in family therapy practice. There is a long tradition of reflecting on the person of the therapist in the family therapy field, often inspired by the metaphor of the wounded healer. In contrast, focusing on the person op the therapist in the present moment of the therapy session is fairly new. In this paper, we use of the dual process models from cognitive psychology as a frame to reflect on the person of the therapist. We review these dual process models and propose that the intuitive responsivity of the therapist relies on the fast, implicit cognitive system (system 1) and the therapist's self-reflection on the slow, deliberate system (system 2). The therapist's actions in therapy practice then emerge moment-by-moment as an echo of the way these two cognitive systems balance each other. It is optimal if the therapist, attuned to the family's rhythm, can flexibly oscillate between the two systems. In the concluding comments of this paper, reflective questions are posed about what this perspective may mean for family therapy practice, training, and supervision.

A non-linear implicit approach for modelling the dynamics of porous tensile structures interacting with fluids

A new model for the simulation of large motions of porous tensile structures and their interaction with the surrounding fluid is developed in this paper. The discrete structure is represented by several non-linear elastic bars and knots connecting up to four bars. An implicit system of equations is derived from the fundamental relations of dynamics, kinematics and material and solved using an improved Newton’s method. The Navier–Stokes equations are solved in a numerical domain to account for the interaction with the fluid. The presence of the porous structure is respected in these equations through an additional forcing term based on a modified Lagrangian–Eulerian coupling algorithm. Here, the forces on the structure are distributed on multiple Lagrangian points embedded in the fluid domain. Integration over a suitable Kernel function is applied to distribute these forces on the surrounding fluid. The derived numerical model is suitable for simulating the interaction of porous tensile structures of arbitrary geometry, non-linear material and under large motion with fluids including complex free surfaces. This is in contrast to existing models which either neglect important non-linearities, the physical interaction with the fluid or rely on explicit time integration. The validation process shows excellent agreement between the numerical simulations and existing experimental data and demonstrates the applicability of the new methodology for a wide range of applications.

What is the Role and Nature of Language and Language Development?

In this paper, the role and nature of language and language development will be discussed. Research and theory in second language acquisition has demonstrated that (i) language is an abstract, implicit and complex system. Input (ii) plays a key role in language development; despite the fact that some knowledge of language is innate (iii). Overall, language development (iv) is ordered and stage-like and instruction (v) has a limited role. Theoretical and pedagogical implications will be highlighted.

Structured singular value of implicit systems

Implicit systems provide a general framework in which many important properties of dynamic systems can be studied. Implicit systems are especially relevant to behavioural systems theory, the analysis and synthesis of complex interconnected systems, systems identification and robust control. By incorporating algebraic constraints, implicit models provide additional versatility relative to the standard input–output framework. Problems of robust stability in implicit systems lead in a natural way to non‐standard structured singular value (μ) formulations. In this note, it is shown that for a class of uncertainty structures involving repeated scalar parameters, these problems reduce to a standard μ problem which is well studied and for the solution of which several numerical algorithms are available. Our results are based on a matrix dilation technique and the redefinition of the uncertainty structure of the transformed problem. The main results of the paper are illustrated with a numerical example.

Solvability of the Cauchy problem for a pseudohyperbolic system

We consider the Cauchy problem for an implicit system of linear partial differential equations. It belongs to the class of pseudohyperbolic systems. We prove the unique solvability of the Cauchy problem in weighted Sobolev spaces and obtain energy estimates of the solution.

Tracking in the tracks in the Italian public schooling: Inequality patterns in an urban context

We study whether, alongside with an explicit tracking system separating students in general versus vocational curricula typical in European countries, the Italian highly centralised public schooling is also characterised by another implicit tracking system, typical of the US, separating students mostly by ability and income within the same track. We pursue this aim by considering the municipality of Turin, a post-industrialised urban context in Northern Italy. We proxy students' ability and skills with the score obtained at the standardised admission test at the School of Economics and Business of the local university. We find evidence of heterogeneity across tracks and schools within the same track, which suggests that the inequality patterns common in the Italian schooling system are affected by both types of tracking. We then discuss the potential sources of this US-style tracking, namely self-selection of better students in better schools, observed and unobserved school characteristics and income stratification. As for the role of income, we find limited evidence of residential segregation, but students from better socio-economic backgrounds travel more, exploiting information on school quality.

A multi-prediction implicit scheme for steady state solutions of gas flow in all flow regimes

An implicit multiscale method with multiple macroscopic prediction for steady state solutions of gas flow in all flow regimes is presented. The method is based on the finite volume discrete velocity method (DVM) framework. At the cell interface, a numerical flux with construction similar to discrete unified gas-kinetic scheme (DUGKS) is applied to ensure multiscale property and make the scheme free from the influence of cell Knudsen number. The idea of the macroscopic variable prediction is adopted and further developed to form an efficient implicit method, where a second order prediction scheme based on the Navier-Stokes (NS) equation is constructed to predict macroscopic variables from macroscopic residuals for the microscopic implicit system, and then a multiple prediction structure is formed. The accurate prediction procedure endows the implicit numerical system with high convergence speed in all flow regimes, especially in the continuum flow regime where NS solution is directly predicted and the convergence is accelerated greatly. Test cases show that the present method is accurate and efficient, and is one order of magnitude faster than the previous prediction-based implicit multiscale scheme in the continuum flow regime.

Interrupting Microaggressions in Health Care Settings: A Guide for Teaching Medical Students.

IntroductionMicroaggressions are connected to broader conceptualizations of the impact of implicit bias and systems of inequity. The body of evidence supporting the need for more-open discussions in medical education about race, racism, and their impact on health disparities continues to grow. Some have advocated for the importance of bringing anti-racist pedagogy into medical education curricula, which involves explicitly attempting to move beyond people's comfort zones and acknowledging that discomfort can be a catalyst for growth. To discuss the intent and impact of microaggressions in health care settings and how we might go about responding to them, we developed a workshop for third-year undergraduate medical students within a longitudinal undergraduate medical education diversity and inclusion curriculum.MethodsThis workshop occurred during a regularly scheduled clerkship intersession during the 2016–2017 academic year for third-year undergraduate medical students (N = 154). Prior to the workshop, the students were asked to anonymously submit critical incident reports on any microaggressions experienced or witnessed to develop case studies for problem-based learning. Teaching modalities included lecture, problem-based learning with case studies, pair and share, and facilitated small- and large-group debriefs.ResultsThe session was evaluated using a 4-point Likert scale to assess students' comfort in learning about the information presented. Ninety-eight percent felt confident in identifying microaggressions, and 85% felt confident in interrupting microaggressions when they occur.DiscussionThis personalized workshop exposes students to microaggressions personally experienced by colleagues with an attempt to interrupt them using empathy, awareness, and communication techniques.

Dirac structures and variational formulation of port-Dirac systems in nonequilibrium thermodynamics

Abstract The notion of implicit port-Lagrangian systems for nonholonomic mechanics was proposed in Yoshimura &amp; Marsden (2006a, J. Geom. Phys., 57, 133–156; 2006b, J. Geom. Phys., 57, 209–250; 2006c, Proc. of the 17th International Symposium on Mathematical Theory of Networks and Systems, Kyoto) as a Lagrangian analogue of implicit port-Hamiltonian systems. Such port-systems have an interconnection structure with ports through which power is exchanged with the exterior and which can be modeled by Dirac structures. In this paper, we present the notions of implicit port-Lagrangian systems and port-Dirac dynamical systems in nonequilibrium thermodynamics by generalizing the Dirac formulation to the case allowing irreversible processes, both for closed and open systems. Port-Dirac systems in nonequilibrium thermodynamics can be also deduced from a variational formulation of nonequilibrium thermodynamics for closed and open systems introduced in Gay-Balmaz &amp; Yoshimura (2017a, J. Geom. Phys., 111, 169–193; 2018a, Entropy, 163, 1–26). This is a type of Lagrange–d’Alembert principle for the specific class of nonholonomic systems with nonlinear constraints of thermodynamic type, which are associated to the entropy production equation of the system. We illustrate our theory with some examples such as a cylinder-piston with ideal gas, an electric circuit with entropy production due to a resistor and an open piston with heat and matter exchange with the exterior.

A benchmark study of the multiscale and homogenization methods for fully implicit multiphase flow simulations

Accurate simulation of multiphase flow in subsurface formations is challenging, as the formations span large length scales (km) with high-resolution heterogeneous properties. To deal with this challenge, different multiscale methods have been developed. Such methods construct coarse-scale systems, based on a given high-resolution fine-scale system. Furthermore, they are amenable to parallel computing and allow for a-posteriori error control. The multiscale methods differ from each other in the way the transition between the different scales is made. Multiscale (finite element and finite volume) methods compute local basis functions to map the solutions (e.g. pressure) between coarse and fine scales. Instead, homogenization methods solve local periodic problems to determine effective models and parameters (e.g. permeability) at a coarser scale. It is yet unknown how these two methods compare with each other, especially when applied to complex geological formations, with no clear scale separation in the property fields. This paper develops the first comparison benchmark study of these two methods and extends their applicability to fully implicit simulations using the algebraic dynamic multilevel (ADM) method. At each time step, on the given fine-scale mesh and based on an error analysis, the fully implicit system is solved on a dynamic multilevel grid. The entries of this system are obtained by using multiscale local basis functions (ADM-MS), and, respectively, by homogenization over local domains (ADM-HO). Both sets of local basis functions (ADM-MS) and local effective parameters (ADM-HO) are computed at the beginning of the simulation, with no further updates during the multiphase flow simulation. The two methods are extended and implemented in the same open-source DARSim2 simulator (https://gitlab.com/darsim2simulator), to provide fair quality comparisons. The results reveal insightful understanding of the two approaches, and qualitatively benchmark their performance. It is re-emphasized that the test cases considered here include permeability fields with no clear scale separation. The development of this paper sheds new lights on advanced multiscale methods for simulation of coupled processes in porous media.

Structure-preserving and efficient numerical methods for ion transport

Ion transport, often described by the Poisson–Nernst–Planck (PNP) equations, is ubiquitous in electrochemical devices and many biological processes of significance. In this work, we develop conservative, positivity-preserving, energy dissipating, and implicit finite difference schemes for solving the multi-dimensional PNP equations. A central-differencing discretization based on harmonic-mean approximations is employed for the Nernst–Planck (NP) equations. The backward Euler discretization in time is employed to derive a fully implicit nonlinear system, which is efficiently solved by a newly proposed Newton's method. The improved computational efficiency of the Newton's method originates from the usage of the electrostatic potential as the iteration variable, rather than the unknowns of the nonlinear system that involves both the potential and concentration of multiple ionic species. Numerical analysis proves that the numerical schemes respect three desired analytical properties (conservation, positivity preserving, and energy dissipation) fully discretely. Based on advantages brought by the harmonic-mean approximations, we are able to establish estimate on the upper bound of condition numbers of coefficient matrices in linear systems that are solved iteratively. The solvability and stability of the linearized problem in the Newton's method are rigorously established as well. Numerical tests are performed to confirm the anticipated numerical accuracy, computational efficiency, and structure-preserving properties of the developed schemes. Adaptive time stepping is implemented for further efficiency improvement. Finally, the proposed numerical approaches are applied to characterize ion transport subject to a sinusoidal applied potential.

A comparison between phonetic engine and GMM–UBM classifier for language identification tasks

In this paper, two automatic language identification (LID) systems are compared. One of the systems is the Hidden Markov Model (HMM) based phonetic engine (PE), and the other is the Gaussian Mixture Model based Universal Background Model (GMM–UBM) classifier. The PE belongs to the category of explicit LID systems while the GMM–UBM classifier falls into the category of implicit LID systems. Ideally, explicit LID requires a segmented and phonetically labelled speech corpus, while the implicit LID systems do not require any phonetic labelling of the data. Both systems are tested here in identifying a set of data belonging to three Indian languages, Manipuri, Assamese and Bengali. The selection of these languages is made due to their wide range of usages in North Eastern India, while at the same time; no proper identification task has been reported so far for a database containing these languages together. The purpose of this comparison is to check the LID efficiency of the relatively new concept of PE with a prevalent identification technique GMM–UBM. In the experiments, it is found that the identification rate (IDR) is more with the PE than that of GMM–UBM system. The average IDR reported with PE is 99% while for the GMM–UBM system it is found to be 96.94% with the same speech corpus being in use. However, the data preparation task is a little more cumbersome and expensive in PE than that of GMM–UBM system. Thus, the compensation for accuracy may be paid with the cost incurred in data preparation.