Learned Process Research Articles

Generalised Diffusion Probabilistic Scale-Spaces

Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from physics, they apply image perturbations such as noise and blur in a forward process that results in a tractable probability distribution. A corresponding learned reverse process generates images and can be conditioned on side information, which leads to a wide variety of practical applications. Most of the research focus currently lies on practice-oriented extensions. In contrast, the theoretical background remains largely unexplored, in particular the relations to drift-diffusion. In order to shed light on these connections to classical image filtering, we propose a generalised scale-space theory for diffusion probabilistic models. Moreover, we show conceptual and empirical connections to diffusion and osmosis filters.

Using Role Play Technique to Improve Students Speaking Skill at the Eighth Grade of SMPN 7 Bukittinggi

The purpose of this research was to to know whether using the role play technique can improve the students’ speaking skill. The researcher had done in SMPN 7 Bukittinggi at VIII.7 class. The problems in this research was caused by several factors. The first problem, most of the students were still awkard in speaking, the only spoke when the teacher asked them. The second problem, the lack of opportunities for students to speak when learning English in the class. The third problem, the students were less excited or less encouraged enough to participated in the speaking learned process. To obtain the research data the writer conducted the observation and test. It was found that there was a significant effect of using role play technique toward students speaking skill. It is proven that role play techniques can improve students' speaking skills. Where student scores increased further from cycle 1 to cycle 2. From the prelimenery research it can be seen that the average student score is 60,00. After carrying out actions in cycle 1 the average value increased, namely 72.25 and in cycle 2 it increased again to 91,00. It can be concluded that all the students enjoy with the role play technique. Moreover, they admitted that their speaking skill improve.

Landau model for illustrating the learning and unlearning process of nociplastic pain.

Recent advancements in understanding the consolidation of nociplastic pain point to a complex, non-conscious learned process of threat perception. Neurobiological pain education is emerging as a promising approach to unlearn nociplastic pain, supported by biopsychosocial tools such as exposure to movement, mindfulness, and group sharing formats. However, this approach is still not well-known among clinicians and the society at large, creating a communication problem that unfortunately perpetuates the suffering of patients. Herein, we propose a Landau model to describe the learning and unlearning process of nociplastic pain, aiming to clarify this complex situation and facilitate communication across different sectors of the society. Nociplastic pain corresponds to a first-order transition, with attention more likely in the alert-protection state than in the trust-explore state. Two appealing results of the model are that the perception of the critical context depends on personal history regarding the symptom and that biopsychosocial loops are formed when there is alarming learned historical information about the symptom, along with confused and contradictory expert information, as seen in nocebo messages. Learning and unlearning in the model correspond to a chang in control parametrs that can weigh more on the alert-protection state, trust-explore state, uncertain state or neutral state. This description clarifies why neurobiological education is the foundational therapy from which others must be built to embody the accessible, clear, and trustworthy information.

Developing prospective teachers' science-task knowledge and practice through lesson study

Science learned requires constructivist experiences so that students could learnt to discover and conclude. However, today's learned challenges were increasing along with the diversity of students and interests. The aimed of this researched was to increase the knowledge and practiced of prospective science teachers in didactic practiced through lesson studied activities. Didactic knowledge and practiced includes aspects of knowledge in preparing student assignments and analyzing student learned difficulties. Apart from that, updating student competency achievements as a curriculum requirement was also a challenged for prospective teachers. This researched was a qualitative studied that conducted researched on the lesson studied group of prospective teachers consisting of two groups totaling eleven prospective teachers. Data collection was carried out through direct group observation, semi-structured interviews, and participant observation. The results of the implementation of the lesson studied project revealed the stages of science learned through the preparation of various learned ideas, the preparation of structured learned paths by thinking about the future, thinking about the process of student collaboration and the learned process. Constructed science concepts to determine objective learned outcomes. The results of this researched revealed how teams of prospective teachers collaborate in lesson studied learned and did things that built and support the team in achieving common goals.

Postprocessing of Ensemble Weather Forecasts Using Permutation-Invariant Neural Networks

Abstract Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previous approaches, which often operate on ensemble summary statistics and dismiss details of the ensemble distribution, we propose networks that treat forecast ensembles as a set of unordered member forecasts and learn link functions that are by design invariant to permutations of the member ordering. We evaluate the quality of the obtained forecast distributions in terms of calibration and sharpness and compare the models against classical and neural network–based benchmark methods. In case studies addressing the postprocessing of surface temperature and wind gust forecasts, we demonstrate state-of-the-art prediction quality. To deepen the understanding of the learned inference process, we further propose a permutation-based importance analysis for ensemble-valued predictors, which highlights specific aspects of the ensemble forecast that are considered important by the trained postprocessing models. Our results suggest that most of the relevant information is contained in a few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems.

Data-Driven Soft Sensor Based on Sparse Relational Graph Attention Network for Hot Strip Mill Process

Due to technological or financial constraints, it is always challenging to measure the mechanical properties (MPs) of the hot strip mill process (HSMP) online. A feasible solution that offers a consistent and trustworthy online estimation of MPs based on available process variables is to develop an Effective soft sensor. To achieve this, we propose a novel method for MP modeling based on a sparse relational graph attention network (SRGAT). In SRGAT, a sparse relationship learning (SRL) module is first utilized to learn a graph structure that can describe sparse interactive correlations among process variables. Then, both the learned graph structure and process variables are fed into the graph attention network (GAT) to highlight useful information for MP prediction of the steel strip. The proposed SRGAT soft sensor surpasses state-of-the-art approaches in terms of root mean square error (RMSE), mean absolute error (MAE), and r-square (R2), according to experimental results on the HSMP data collected from a real Iron & Steel Co., Ltd, China.

PoseScript: Linking 3D Human Poses and Natural Language.

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we have introduced the PoseScript dataset. This dataset pairs more than six thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. Additionally, to increase the size of the dataset to a scale that is compatible with data-hungry learning algorithms, we have proposed an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information, known as "posecodes", using a set of simple but generic rules on the 3D keypoints. These posecodes are then combined into higher level textual descriptions using syntactic rules. With automatic annotations, the amount of available data significantly scales up (100k), making it possible to effectively pretrain deep models for finetuning on human captions. To showcase the potential of annotated poses, we present three multi-modal learning tasks that utilize the PoseScript dataset. Firstly, we develop a pipeline that maps 3D poses and textual descriptions into a joint embedding space, allowing for cross-modal retrieval of relevant poses from large-scale datasets. Secondly, we establish a baseline for a text-conditioned model generating 3D poses. Thirdly, we present a learned process for generating pose descriptions. These applications demonstrate the versatility and usefulness of annotated poses in various tasks and pave the way for future research in the field. The dataset is available at https://europe.naverlabs.com/research/computer-vision/posescript/.

Interplay between endocannabinoid and endovanilloid mechanisms in fear conditioning.

The transient receptor potential cation channel, subfamily V (vanilloid), member 1 (TRPV1) mediates pain perception to thermal and chemical stimuli in peripheral neurons. The cannabinoid receptor type 1 (CB1), on the other hand, promotes analgesia in both the periphery and the brain. TRPV1 and CB1 have also been implicated in learned fear, which involves the association of a previously neutral stimulus with an aversive event. In this review, we elaborate on the interplay between CB1 receptors and TRPV1 channels in learned fear processing. We conducted a PubMed search for a narrative review on endocannabinoid and endovanilloid mechanisms on fear conditioning. TRPV1 and CB1 receptors are activated by a common endogenous agonist, arachidonoyl ethanolamide (anandamide), Moreover, they are expressed in common neuroanatomical structures and recruit converging cellular pathways, acting in concert to modulate fear learning. However, evidence suggests that TRPV1 exerts a facilitatory role, whereas CB1 restrains fear responses. TRPV1 and CB1 seem to mediate protective and aversive roles of anandamide, respectively. However, more research is needed to achieve a better understanding of how these receptors interact to modulate fear learning.

Clinical Reasoning in Voice Pedagogy: A Systematic Foundation for Developing Intuition in Novice Voice Teachers

Voice pedagogy literature often draws a parallel between a doctor’s ability to diagnose illness and prescribe treatment and a voice teacher’s ability to identify vocal obstacles and design solutions. The core of these parallel procedures is an ability to utilize critical reasoning skills within highly specific contexts. Medical literature describes this as “clinical reasoning,” and students of medicine study it as a learned and systematic process. In contrast, voice pedagogy literature has traditionally described it as an innate process that is developed through studio experience. This article broadly examines the ways in which medical literature has broken down clinical reasoning into specific cognitive processes and proposes a model through which these processes can be adapted to the needs of developing voice teachers. An emphasis is given to the development of intuition and empathy from teacher to singer, as well as a reexamination of how voice teachers interact with voice literature and research.

Effectiveness of the Lessons Learned Process in ESA OPS Directorate

The ESA Directorate of Operations (D/OPS) has the need to continuously challenge for improvement the working practices, the deployed resources, the delivered products and services. Since 2007 D/OPS has put in place the process of Lessons Learned to address proposed changes coming from the performed experience. The positive outcome of this process over the years is visible in the implemented organisational improvements and in the general learning-based culture accumulated. As part of EN9100-based QMS, it is essential to have evidence that the process is effective, i.e. whether it is carried out according to expectations of the process owner. The paper will present the introduction of Key Effectiveness Indicators (KEI) on the Lessons Learned process, their measurement and the analysis of the collected data for identification of possible improvement actions. Some pre-conditions for definition and implementation of KEIs (for any process) need to be satisfied. Then it is possible to identify few strategic points in the process where expectation on the discipline of the organisation can be translated in a quantitative target. A typical measurement of effectiveness is the duration in performing a specific step of the Lessons Learned process (i.e. in terms of timeliness). The paper will present:
 
 The identified KEIs for the Lessons Learned process
 The identified homogeneous environments where the instances of the process are running
 The (current) targets set by the process owner
 The time scope of effectiveness measurement.
 
 The analysis of collected data has demonstrated weaknesses in the process to be corrected but also highlighted the current strengths. This initiative confirmed that the continual improvement of the organisation relies also on the effectiveness of the Lessons Learned process itself, and not only on the contents to be learned (i.e. the performance indicators on the organisation itself).

Developing english supplementary book in teaching speaking skill used visio media for 7 grade at MTs Syekh Subakir


 
 
 
 Abstract: The purpose of this study was to determine the results of validator validation and student responses to the product that the researcher had developed in the form of a supplementary book with a combination of Visio media to test 16 7B grade students. MTs Syekh Subakir 1. This study uses Research and Development (R&D) used Borg and Gall theory (2003). The material presented in the media is descriptive text and this additional product focuses on speaking skill. Data collection technique were carried out used unstructured interviews and surveys. The instruments used are interview guidelines and questionnaires used a Likert scale. Based on the results of the validation carried out by material and media experts, from the results of student responses, the percentage of results obtained were 82,3%, 83,3%, and 79.8% with the criteria "Good or Valid or useful". so, it can be concluded that additional descriptive text books as learned media are very well used in the English learned process.
 
 
 

Impacts of visual neurorehabilitation on autistic children

IntroductionChildren with Autism Spectrum Disorder (ASD) have a qualitative deficit in social interaction that can be manifested by two of the following characteristics: a) deficits in the use of non-verbal behaviors, such as eye contact, facial expressions, body postures and gestures used to regulate the social interaction; b) inability to develop peer relationships in an adequate manner compatible with their level of development; c) absence of the spontaneous tendency to share emotions, interests and objects; d) lack of social and emotional reciprocity. Among these characteristics, we can mention the difficulty in establishing and maintaining eye contact as one of the points that make it more difficult to develop important skills for learning in general.ObjectivesThe present study aims to investigate the impacts of visual neurorehabilitation on autistic children.MethodsThe online questionnaire based on the emotional and functional development scale (FEDQ) of the DIR/Floortime model was distributed to parents and professionals as of October 15, 2022. To date, the Visual Contact Protocol has been applied to 34 children.ResultsThe protocol has been applied to 34 children so far.The collection suggests that when we favor the visual contact of the autistic, the motor, cognitive, linguistic, emotional and especially social learning prove to be facilitatedConclusionsDiscussionVision is the master of all the senses. When the child is born, his visual ability is very limited. As she grows functional visual skills (HVF) develop. When we talk about the autistic child, these abilities may not be well defined.The collection suggests that when we favor the visual contact of the autistic, the motor, cognitive, linguistic, emotional and especially social learning prove to be facilitated. When observing the children in speech therapy, a better engagement was verified during the execution of the activities suggested by the therapist. Vision is a learned process that adds meaning to what is seen. There is still a lot of research to be done, but by providing the possibility of visual screening to the autistic child, we give them the opportunity to explore the world, get to know and recognize the environments that surround them and consequently improve their learning.Keywords: autism, visual neurorehabilitation.Disclosure of InterestNone Declared

Albatrosses develop attraction to fishing vessels during immaturity but avoid them at old age.

Animals have to develop novel behaviours to adapt to anthropogenic activities or environmental changes. Fishing vessels constitute a recent feature that attracts albatrosses in large numbers. While they provide a valuable food source through offal and bait, they cause mortalities through bycatch, such that selection on vessel attraction will depend on the cost-benefit balance. We examine whether attraction to fishing and other vessels changes through the lifetime of great albatrosses, and show that attraction differed between age classes, sexes and personality. Juveniles encountered fewer vessels than adults, but also showed a lower attraction to vessels when encountered. Attraction rates, especially for fishing vessels, increased through immaturity to peak during adulthood, decreasing with old age. Shy females had lower attraction to vessels and shy males remained at vessels longer, suggesting that bolder individuals may outcompete shyer ones, with positive consequences for mass gain. These results suggest that attraction to vessels is a learned process, leading to an increase with age, and is not the result of preferential attraction to new objects by juveniles. Overall, our findings have important conservation implications as a result of potential strong differential selection on the risk of bycatch for age classes, personality types, populations and species.

Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles

We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.

Learning Safety Filters for Unknown Discrete-Time Linear Systems

A learning-based safety filter is developed for discrete-time linear time-invariant systems with unknown models subject to Gaussian noises with unknown covariance. Safety is characterized using polytopic constraints on the states and control inputs. The empirically learned model and process noise covariance with their confidence bounds are used to construct a robust optimization problem for minimally modifying nominal control actions to ensure safety with high probability. The optimization problem relies on tightening the original safety constraints. The magnitude of the tightening is larger at the beginning since there is little information to construct reliable models, but shrinks with time as more data becomes available.

Executive function improvement in response to meta-cognitive training in chronic mTBI / PTSD.

We tested Goal Management Training (GMT), which has been recommended as an executive training protocol that may improve the deficits in the complex tasks inherent in life role participation experienced by those with chronic mild traumatic brain injury and post-traumatic stress disease (mTBI/PTSD). We assessed, not only cognitive function, but also life role participation (quality of life). We enrolled and treated 14 individuals and administered 10 GMT sessions in-person and provided the use of the Veterans Task Manager (VTM), a Smartphone App, which was designed to serve as a "practice-buddy" device to ensure translation of in-person learning to independent home and community practice of complex tasks. Pre-/post-treatment primary measure was the NIH Examiner, Unstructured Task. Secondary measures were as follows: Tower of London time to complete (cTOL), Community Reintegration of Service Members (CRIS) three subdomains [Extent of Participation; Limitations; Satisfaction of Life Role Participation (Satisfaction)]. We analyzed pre-post-treatment, t-test models to explore change, and generated descriptive statistics to inspect given individual patterns of change across measures. There was statistically significant improvement for the NIH EXAMINER Unstructured Task (p < .02; effect size = .67) and cTOL (p < .01; effect size = .52. There was a statistically significant improvement for two CRIS subdomains: Extent of Participation (p < .01; effect size = .75; Limitations (p < .05; effect size = .59). Individuals varied in their treatment response, across measures. In Veterans with mTBI/PTSD in response to GMT and the VTM learning support buddy, there was significant improvement in executive cognition processes, sufficiently robust to produce significant improvement in community life role participation. The individual variations support need for precision neurorehabilitation. The positive results occurred in response to treatment advantages afforded by the content of the combined GMT and the employment of the VTM learning support buddy, with advantages including the following: manualized content of the GMT; incremental complex task difficulty; GMT structure and flexibility to incorporate individualized functional goals; and the VTM capability of ensuring translation of in-person instruction to home and community practice, solidifying newly learned executive cognitive processes. Study results support future study, including a potential randomized controlled trial, the manualized GMT and availability of the VTM to ensure future clinical deployment of treatment, as warranted.

Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed using deep neural networks (DNNs) to represent the state transition and observation generation, both of which constitute a stochastic nonlinear state-space model. A new bidirectional recurrent neural network (RNN), creating a connection of the hidden layer between a forward RNN and a backward RNN, is proposed to generate the filtering estimation and the smoothing estimation of process states which further generate observations with DNN-based process models. The smoothing estimator and the process model are first learned offline with all collected samples. Then the filtering estimator is fine-tuned by the learned smoother and process models to achieve real-time monitoring since the filter state is estimated based on the past and the current observations. Two indices are designed based on the learned model for monitoring the process anomaly. The proposed process monitoring model can deal with complex nonlinearities, process dynamics, and process uncertainties, all of which can be very challenging for the existing methods, such as kernel mapping and stacked auto-encoder. Two case studies validate that the effectiveness of the proposed method outperforms the other comparative methods by at least 10% when using the averaged fault detection rate in the industrial experimental data.

Neurobiological mechanisms of mood disorders: Stress vulnerability and resilience.

Stress is an important factor in the development of several human pathologies. The response of rodents and humans to stress depends on many factors; some people and rodents develop stress-related mood disorders, such as depression and anxiety in humans, depression-like and anxiety-like behavior in mice and rats, while others report no new psychological symptoms in response to chronic or acute stress, and are considered susceptible and resilient to stress, respectively. Resilience is defined as the ability to thrive in the face of adversity and is a learned process that can help protect against occupational stressors and mental illnesses. There is growing interest in the underlying mechanisms involved in resilience and vulnerability to depression caused by stress, and some studies have demonstrated that individual variability in the way animals and humans respond to stress depends on several mechanisms, such as oxidative stress, neuronal plasticity, immunology and genetic factors, among others not discussed in this review, this review provides a general overview about this mechanism.

Career Education Of Military Leaders in the Area of the Lessons Learned

The paper is focused on the issue how to develop and strengthen the competencies of military leaders essential for management of the Lessons Learned process in the Czech Armed Forces. The analysis shows that the issue of lessons learned is sufficiently addressed in relevant military documents which provides a foundation for definition of relevant competencies. Questionnaire survey proves that most respondents apply identified competencies related primarily to analysis phase and the verification of lessons identified. The article further presents a study curriculum proposal that can be used as a comprehensive framework for study programs within career education currently provided by the Center for Security and Military Strategic Studies of the University of Defence and the Training Command - Military Academy.

Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis

A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the process execution. Conformance analysis techniques examine the synchronisation between the recorded logs and the learned process models, but the exploitation of event logs for abnormality detection is a relatively under-explored area in process mining. In this paper, we proposed a novel technique for the identification of abnormalities in business process execution through the extension of available conformance analysis techniques. Non-traditional conformance analysis techniques are used to find correlations and discrepancies between simulated and observed behaviour in process logs. Initially, the raw event log is filtered into two variants, successful and failed, based upon the outcome of the instances. Successfully executed instances refer to an ideal conduct of process and are utilised to discover an optimal process model. Later, the process model is used as a behavioural benchmark to classify the abnormality in the failed instances. Abnormal behaviour is compiled grounded on three dimensions of conformance, control flow-based alignment, trace-level alignment and event-level alignment. For early predictions, we introduced the notion of conformance lifeline presenting the impact of varying fitness scores during process execution. We applied the proposed methodology to a real-world event log and presented several process-specific improvement measures in the discussion section.