Amount Of Prior Knowledge Research Articles

Prior Knowledge Norms for Naming Country Outlines: An Open Stimulus Set.

Paired-associate stimuli are an important tool in learning and memory research. In cognitive psychology, many studies use materials of which the learners are expected to have little to no prior knowledge. Despite their theoretical usefulness, conclusions from these studies are difficult to generalize to real-world learning contexts, where learners can be expected to have varying degrees of prior knowledge. Here, we present an ecologically valid stimulus set with 112 country outline-name pairs, and report response times and prior knowledge for these items in 285 largely Western European participants. Prior knowledge per item ranged from very high (94.4%) to zero (0.3%), thereby allowing researchers to select materials of which participants can be expected to have any given amount of prior knowledge. As such, this database provides a useful tool for research on real-world learning. The database can be accessed at: https://osf.io/q25rd/.

Does prior knowledge affect interaction dynamics and learning achievement in digital problem-based learning? A pilot study.

Previous research on problem-based learning (PBL) describes that videotaped observations develop meaningful insights into cognitive processes in tutorial groups. Analysis regarding the amount of prior knowledge on learning achievement has not been investigated in medical education so far, although both are key factors of PBL success. Thus, we intended to analyse videos of digital problem-based learning (dPBL) sessions, focusing on knowledge acquisition and interaction dynamics among groups with different levels of prior knowledge to reveal any distinctions. This study employed a pilot design by dividing 60 dental students into twelve subgroups with less or more prior knowledge, determined by a pre-semester multiple choice test (MCQ). The groups engaged in videotaped dPBL cases, which were examined regarding group interactions and tutor effectiveness. The learning achievement was assessed through a post-semester MCQ, an oral and practical exam. The video analysis showed that dPBL groups with less prior knowledge achieved significantly higher tutor effectiveness and group interaction utterances, but that the percentage of time in which utterances occurred was similar in both groups. Related to the MCQ results, the students with less prior knowledge learned four times more than those with profound previous abilities, but no significant difference was found in the results of the oral exam and practical exam. The interaction dynamics in dPBL depend on the group's amount of prior knowledge. Especially groups including participants with less prior knowledge seemed to benefit from dPBL in comparison to groups with more prior knowledge. The dPBL groups acquired knowledge in different ways during the courses but, finally, all students arrived at a similar level of knowledge.

Lessons Learned from Implementing Unconscious Bias Training at an Academic Medical Center.

IntroductionIn 2018, our Midwestern university medical center began offering unconscious bias training. Each session concluded with a standard evaluation. We analyzed two years of data that focused on three areas: 1) whether demographic differences or amount of prior knowledge on the topic influenced the training experience; 2) what participants liked best about the training; and 3) whether participants’ stated intentions to apply their learning at the end of the training aligned with institutional goals of the training.MethodsParticipants attended sessions open to the campus community pre-scheduled by the Office for Diversity, Equity, and Inclusion and posted on its website. Chi-square tests were utilized to test associations between outcomes and questionnaire responses. Outcome measures included race/ethnicity, prior knowledge level, and overall rating of the training. Thematic analysis was used to code comments and establish themes from two open-ended survey questions.ResultsSignificant differences were found by race and ethnicity for all questionnaire responses; each were p < 0.001. Those who reported they had advanced/expert knowledge on the topic were less likely to report the training increased their knowledge, and those who reported their race as White/Caucasian tended to give the training the highest overall rating, as did heterosexuals. Through thematic analysis, participants valued the interactive nature of the training sessions, the use of storytelling, and the safety of the learning environment. Participants’ intention to apply their learning indicated they had gained general awareness of bias and settings where it might influence their work.ConclusionsIn an effort to foster a better working and learning environment for those who are underrepresented in the health professions, training was provide that may not have met the expectations of all participants. At the same time, participants who identified as White clearly increased their awareness of bias. Therefore, it is recommended to move away from one-size-fits-all unconscious bias training and develop a robust training continuum to provide ongoing advancement for diverse audiences.

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination.

Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.

The contamination of valsartan and other sartans, Part 2: Untargeted screening reveals contamination with amides additionally to known nitrosamine impurities

The recent incidences of contaminated valsartan drug products gave rise to review the suitability of current impurity profiling workflows implemented at authorities and pharmaceutical companies. The major drawback of targeted impurity profiling, where a considerable amount of prior knowledge about possible contaminants is necessary, is the fact that unexpected impurities are overlooked easily. Here, a generic untargeted approach was applied on sartan containing drug products. The untargeted workflow allowed for the discrimination of different batches, different production sites, and differences after changes in the production process. The presented universal workflow makes use of LC-HRMS/MS and multivariate analysis for the interpretation of the data. Sartan samples contaminated with N-nitrosodimethylamine could be very well discriminated from N-nitrosodimethylamine-free samples using the procedure. Furthermore, untargeted approaches revealed two new impurities in various sartans drug products: valeramide and N,N-dimethylvaleramide.

An Exploratory Study of Predictors of Relationship Commitment for Cisgender Female Partners of Transgender Individuals.

The current exploratory study investigates which variables predict commitment among cisgender women currently in a relationship with a transgender-identified partner. This study includes variables specific to gender diverse couples such as years in the relationship, amount of prior knowledge about transgender identity, and gender role beliefs. Also included are the general relationship factors of satisfaction, quality of alternatives, investment size, and the outcome variable of commitment, as described in the Investment Model of Commitment. Using convenience sampling from online support groups, the sample was n=138 cisgender female partners. The findings of the regression analyses indicate one's personal resilience and the number of years in the relationship prior to transition are significantly related to partner commitment, with satisfaction mediating both associations. The longer a participant was in the relationship at the time of a gender transition of a partner, the less satisfaction they report, thus reducing commitment to the relationship. Additionally the more personal resilience a partner endorses, the higher the relationship satisfaction, and in turn, more commitment is reported. The results offer information about the important factors related to staying in relationships during and after a gender transition.

Image Super Resolution Reconstruction Based MCA and PCA Dimension Reduction

Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.

Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

Reasoning and fuzzy comprehensive assessment methods based CAD system for boiler intelligent design

Boilers are key industrial equipment in the field of modern manufacturing. In this study, rule-based reasoning (RBR) and case-based reasoning (CBR) are applied into the boiler intelligent design. RBR is adopted to perform like a “sieve” for the acquisition of candidate structure modules. Then, fuzzy comprehensive assessment method is used to select an optimal structure module among the candidates. The optimal structure module is subjected to the CBR process, which includes modification and case retention. It is a minor adjustment process that needs a great amount of prior knowledge. The case retention process gives the system a self-enrich function, which will improve the design ability of the system with continuous use of the system. A case study is presented to validate the correctness and efficiency of proposed method.

The influence of prior knowledge and viewing repertoire on learning from video

Video is increasingly used as an instructional tool. It is therefore becoming more important to improve learning of students from video. We investigated whether student learning effects are influenced through an instruction about other viewing behaviours, and whether these learning effects depend on their prior knowledge. In a controlled environment, 115 students watched a number of instructional videos about the technical equipment needed in a course on digital photography. Every second student was instructed about other possible viewing behaviours. A pre-post-retention test was carried out to calculate learning effects. The differences with respect to the learning effects of students who received an awareness instruction on an alternative viewing strategy were not significantly different. The differences as observed in our earlier experiment however could not be reproduced. Students with a broad viewing repertoire showed higher learning effects than students with a narrow repertoire. Furthermore, students with a strategic viewing approach also showed higher learning effects. Certain conditions have to be met: the technical and didactical quality of the video must be good, the integration in a learning task must be apparent, students must be aware of their viewing behaviour, and teachers must be aware of their students’ viewing behaviour in order to enrich the viewing repertoire of students when they have at least some basic knowledge e.g. after several lessons on the topics at hand. In future research, this study should be replicated using more complex video episodes than the instruction videos we used in our experiments that were only on the factual knowledge level of the taxonomy of Bloom. Moreover, replication of this study with a larger sample size could yield a significant improvement in learning effects. This is plausible because students need an amount of prior knowledge beyond a certain threshold value in order to be able to expand their knowledge network in their long term memory. Finally, additional media player functionality, facilitating effective student learning from video, can be described based on the results of this study.

Bayesian Estimation of Clean Speech Spectral Coefficients Given a Priori Knowledge of the Phase

While most short-time discrete Fourier transform-based single-channel speech enhancement algorithms only modify the noisy spectral amplitude, in recent years the interest in phase processing has increased in the field. The goal of this paper is twofold. First, we derive Bayesian probability density functions and estimators for the clean speech phase when different amounts of prior knowledge about the speech and noise amplitudes is given. Second, we derive a joint Bayesian estimator of the clean speech amplitudes and phases, when uncertain a priori knowledge on the phase is available. Instrumental measures predict that by incorporating uncertain prior information of the phase, the quality and intelligibility of processed speech can be improved both over traditional phase insensitive approaches, and approaches that treat prior information on the phase as deterministic.

Expertise reversal for different forms of instructional designs in dynamic visual representations

AbstractThe recent advances in software and computer technology have enabled the incorporation of dynamic representations into a multitude of educational and training environments. Cognitive load theory has been extensively used to enhance learning from complex dynamic representations by providing appropriate instructional designs to manage learner cognitive load. The available evidence, however, indicates that the suggested instructional designs that are effective for novice learners can reverse and become ineffective for learners with higher levels of prior knowledge. This phenomenon is called the expertise reversal effect. This paper reviews a series of recent experimental studies that have found interactions between levels of learners' organized knowledge structures (endogenous support) and effectiveness of different instructional designs (exogenous support), leading to the expertise reversal effect. It is argued that adapting instructional designs to learners with different amount of prior knowledge is a crucial part of effective learning.

Maximum-Likelihood Estimation of the Discrete Coefficient of Determination in Stochastic Boolean Systems

The discrete Coefficient of Determination (CoD) has become a key component of inference methods for stochastic Boolean models. We develop a parametric maximum-likelihood (ML) method for the inference of the discrete CoD for static Boolean systems and for dynamical Boolean systems in the steady state. Using analytical and numerical approaches, we compare the performance of the parametric ML approach against that of common nonparametric alternatives for CoD estimation, which show that the parametric approach has the least bias, variance, and root mean-square (RMS) error, provided that the system noise level is not too high. Next we consider the application of the proposed estimation approach to the problem of system identification, where only partial knowledge about the system is available. Inference procedures are proposed for both the static and dynamical cases, and their performance in logic gate and wiring identification is assessed through numerical experiments. The results indicate that identification rates converge to 100% as sample size increases, and that the convergence rate is much faster as more prior knowledge is available. For wiring identification, the parametric ML approach is compared to the nonparametric approaches, and it produced superior identification rates, though as the amount of prior knowledge is reduced, its performance approaches that of the nonparametric ML estimator, which was generally the best nonparametric approach in our experiments.

O Direito Dos Escravos: Lutas Jurídicas E Abolicionismo Na Província de São Paulo

This book is a must- read for historians of slave emancipation in Brazil. In revisiting the broad influence of juridical struggles for freedom during the first collective abolitionist mobilizations of the late 1860s, and then tracing the persisting force of such practices through the 1870s and early 1880s, Azevedo convincingly recasts the 20- year period of social struggle prior to the 1888 abolition law as significantly more fluid and interconnected than historians have previously allowed. In a word, Azevedo blurs the conventional periodization that separates the so- called legalistic or gradual phase (pre- 1880) of abolitionist activism from the better- known immediatist or radical stage (1880- 88). In one of the book’s best- developed sections, she shows that the politicization of freedom suits prior to the 1871 Free Womb Law had a similar galvanizing effect on popular activism as did the more familiar juridical struggles of the 1880s; both processes mobilized slaves, law students, associational groups, journalists, and scores of ordinary people who were called as witnesses.Focused on the not- unfamiliar story of abolition in São Paulo province, this work succeeds in illuminating new connections between the legal and public spaces of abolitionism. Similarly, it provides a refreshingly nuanced example of how to integrate analysis of slave agency within wider processes of social and political change. The broader argument for stretching the conventional periodization of abolitionism deserves careful attention and should well inspire new questions about the trajectories of abolitionist mobilizations.The multifaceted career of the Afro- Brazilian abolitionist Luiz Gama structures the chronological arc of O direito dos escravos. The book is informed, of course, by Azeve-do’s acclaimed 1999 biography of Luiz Gama, yet in its framing and conclusions, it is an original work. Gama’s experiences as a legal representative, police clerk, newspaper editor, and associational leader afford Azevedo an intriguing vantage point on the entangled worlds of the judiciary, the press, and political bodies. Developed in three substantive chapters, the focus is on the relationship between the legal challenges to slavery and the emergent calls for abolition.In chapter 2, Azevedo details how freedom suits made the issue of emancipation into a problem for both property holders and the state. She suggests that it was through the presentation of over 300 freedom suits in 1869 and 1870 that a “movement” to end slavery coalesced in São Paulo city (p. 109). Azevedo posits that cases focusing on the illegal importation of Africans after 1831 were instrumental to the production of an abolitionist discourse. She connects the presentation of these cases to a public conference in São Paulo city in 1869, where the young Rui Barbosa spoke openly about abolishing slavery since “an immense majority [of the enslaved] were brought illegally, in violation of the 1831 law” (p. 110). Furthermore, the politicization of court cases in the press helped to normalize the idea that a slave’s right to freedom was a “natural” right. This notion appeared especially dangerous when invoked in the context of crimes against owners and overseers. From these points, Azevedo revises the problematic characterization of legal and pre- 1880s abolitionism as moderate.In chapter 3, Azevedo returns to the specific context — Gama’s funeral, ironically — where a new narrative about a more radical vision of abolitionism emerged. The public outpourings not only on the day of Gama’s death but also on subsequent anniversaries were key to the consolidation of a militant identity. New leaders such as Antonio Bento, who famously stirred mass slave insurrections in the late 1880s, increasingly asserted themselves as part of a new wave of activists, and early histories of emancipation replicated this schema. As Azevedo reminds us, however, during the intense period of mobi-lization in the late 1860s, Bento was conspicuously absent. Azevedo suggests, albeit in less detail than elsewhere, that Bento’s continued work with legal abolitionism during the 1880s represents another form of continuity between the pre- and post- Gama eras.This is, in short, a rich work that artfully blends legal, social, and cultural approaches to emancipation. It is a book that, nevertheless, assumes readers to possess a considerable amount of prior knowledge on both the topic and the place; there is minimal attempt, for one, to establish a proper context for the provincial enslaved population, a phenomenon that we know was in flux because of the inter- and intraregional slave trades. For example, the introduction of some 40,000 enslaved people into the province during the 1870s may have influenced the intensity of the juridical struggles as well as the ways that antislavery sensibilities crystallized. This does not detract, however, from an important work that scholars of Brazilian slave emancipation will find much to reckon with among its larger and finer points.

Cross‐Linking Nitroxide‐Mediated Radical Copolymerization from a Bayesian Experimental Design Angle

AbstractSummary: In an attempt to collect “meaningful” experimental data in cross‐linking nitroxide‐mediated radical copolymerization of stryene and divinyl benzene, a Bayesian design approach was used to derive optimal experimental sequences to be carried out in the laboratory. Since a considerable amount of prior knowledge about this polymerization system was already available from previous experimental and modeling efforts, application of the Bayesian design of experiments seemed like a perfect approach. In addition, the implementation of the Bayesian approach would, in principle, result in running fewer experiments, hence saving considerable time and resources.

Estimating effective model parameters for heterogeneous unsaturated flow using error models for bias correction

Estimates of effective parameters for unsaturated flow models are typically based on observations taken on length scales smaller than the modeling scale. This complicates parameter estimation for heterogeneous soil structures. In this paper we attempt to account for soil structure not present in the flow model by using so‐called external error models, which correct for bias in the likelihood function of a parameter estimation algorithm. The performance of external error models are investigated using data from three virtual reality experiments and one real world experiment. All experiments are multistep outflow and inflow experiments in columns packed with two sand types with different structures. First, effective parameters for equivalent homogeneous models for the different columns were estimated using soil moisture measurements taken at a few locations. This resulted in parameters that had a low predictive power for the averaged states of the soil moisture if the measurements did not adequately capture a representative elementary volume of the heterogeneous soil column. Second, parameter estimation was performed using error models that attempted to correct for bias introduced by soil structure not taken into account in the first estimation. Three different error models that required different amounts of prior knowledge about the heterogeneous structure were considered. The results showed that the introduction of an error model can help to obtain effective parameters with more predictive power with respect to the average soil water content in the system. This was especially true when the dynamic behavior of the flow process was analyzed.

Recovery of Sparsely Corrupted Signals

We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary. This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation, and recovery of signals that are impaired by, e.g., clipping, impulse noise, or narrowband interference. We present deterministic recovery guarantees based on a novel uncertainty relation for pairs of general dictionaries and we provide corresponding practicable recovery algorithms. The recovery guarantees we find depend on the signal and noise sparsity levels, on the coherence parameters of the involved dictionaries, and on the amount of prior knowledge about the signal and noise support sets.

New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model

The online evolution and learning of fuzzy systems is highly important when dealing with changing environments over time. This capability is especially relevant in the field of control, due to the special characteristics of control problems. In this field, techniques capable of developing controllers with a minimum amount of prior knowledge about the plants to be controlled are desired. Furthermore, these controllers should provide a reduced number of interpretable rules. This paper presents a new Online Self-Evolving Neuro Fuzzy controller based on the Taylor Series Neuro Fuzzy (TaSe-NF) model. Under the assumption of no prior knowledge about the differential equations that define the plant to be controlled, this methodology is capable of incrementally evolving the structure of the controller and adapting its parameters online, while controlling the plant. The new methodology uses a scattered distribution of the fuzzy rules, thereby reducing the number of rules in the fuzzy controller. Moreover, the use of the TaSe-NF model to represent the antecedents of the rules enhances the interpretability of the obtained rules. The proposed evolving fuzzy controller is composed of two main blocks: On the one hand, the online local learning of the rule consequents tackles the task of providing a proper control at the present moment. On the other hand, a structure self-evolution method analyzes the error surface to determine which cluster/rule suffers the worst performance and therefore, needs to be further split. Simulation results are presented to illustrate the capabilities of this new online self-evolving controller.

Prehospital emergency care course: PhEC

The prehospital emergency care (PhEC) course is a three day course covering various aspects of prehospital care, from anaphylaxis to dysrhythmias to trauma management. It is run by the British Association for Immediate Care (BASICS) and is examined and accredited by the Royal College of Surgeons of Edinburgh (RCSEd). The first course was run in April 1993 and it has been developed and redesigned ever since, with the last major restructuring occurring in 2006. The PhEC course is also becoming an internationally respected course, with BASICS running courses in; Romania, Malta, Greece, Goa and Cyprus. To date, approximately 4000 people have successfully passed the PhEC course. However, not everyone is successful. In 2009, the PhEC courses attracted 201 candidates with 40 candidates not being successful; this represents a pass rate of 80%. This seems to be fairly typical, as the average pass rate is between 75%-85% with this being fairly consistent across the various professionals undertaking the course. The course is publicized as a Level 2 qualification, meaning it is aimed at the professional care provider who occasionally works in the prehospital environment. Although this may imply that by its very nature the course is aimed below the level of knowledge and skills already possessed by UK paramedics, this is certainly not the case in the prehospital environment (Box 1). The course requires an in-depth knowledge of the whole range of current paramedic practice, as well as a reasonable working knowledge of more advanced skills and interventions. However, despite this high level of required knowledge, a number of non-registered prehospital care providers have excelled at the PhEC course in the past, including first aiders, firefighters and combat medical technicians. The course The weekend course currently costs £735 for BASICS members and £800 for non-members, candidates might also want to consider paying for accommodation during the course and this can be booked online via the BASICS website. It may also be worthwhile for potential candidates to consider joining BASICS in order to benefit from this discount and to receive the various other benefits of membership to this leading organization. Currently, BASICS membership costs from £47 for student members (with no journal subscription) to £177 for full members, including a paper subscription to the Emergency Medicine Journal. As mentioned previously, the course material is delivered over three days. There is however a large syllabus to be accommodated within these three days and candidates can expect to begin lectures at 08:00–08:30 each day and not leaving before 18:00. It is also advisable not to expect to be ‘spoon-fed’ information during the course; as although there are excellent lectures and instructional practical sessions, a large amount of prior knowledge and preferably experience is certainly needed. Once you have registered for the PhEC course on the BASICS website, you will be sent further registration documents, recommended reading lists, the Manual of Core Skills produced by the RCSEd and a practice test paper (with answers). It is definitely advisable that all candidates The course requires an in-depth knowledge of the whole range of current paramedic practice ‘ Box 1. Hierachy of qualifications

Incremental State Aggregation for Value Function Estimation in Reinforcement Learning

In reinforcement learning, large state and action spaces make the estimation of value functions impractical, so a value function is often represented as a linear combination of basis functions whose linear coefficients constitute parameters to be estimated. However, preparing basis functions requires a certain amount of prior knowledge and is, in general, a difficult task. To overcome this difficulty, an adaptive basis function construction technique has been proposed by Keller recently, but it requires excessive computational cost. We propose an efficient approach to this difficulty, in which the problem of approximating the value function is decomposed into a number of subproblems, each of which can be solved with small computational cost. Computer experiments show that the CPU time needed by our method is much smaller than that by the existing method.