High-level Tasks Research Articles

Model Predictive Control Used in Passenger Vehicles: An Overview

The following article presents a high-level overview of how Model Predictive Control (MPC) is leveraged in passenger vehicles and their subsystems for improved performance. This overview presents the fundamental concepts of MPC algorithms and their common variants. After building some understanding of MPC methods, the paper discusses state-of-the-art examples of how MPC methods are leveraged to perform low- to high-level tasks within a typical passenger vehicle. This review also aims to provide the reader with intuition in formulating MPC systems based on the strengths and weaknesses of the different formulations of MPC. The paper also highlights active areas of research and development.

EXPRESS: Advancing an account of hierarchical dual-task control: A focused review on abstract higher-level task representations in dual-task situations.

Dual tasks are a common phenomenon in everyday life. In dual-task contexts, we perform two component tasks in temporal overlap, which usually results in impaired performance in one or both of these component tasks relative to single-task contexts. Numerous studies have examined dual-task interference at the level of response selection, but only few studies addressed the cognitive representation of a dual task and the cognitive mechanisms controlling these representations. The present review outlines recent empirical findings and theoretical developments concerning these two issues. In detail, the review focuses on different components of a cognitive dual-task representation, including the representation of component-task specific information (i.e., information about the goal and stimulus-response mapping of a component task), the representation of component-task order information (i.e., information about the order in which the component tasks have to executed), and the representation of dual-task identity information (i.e., information about which two component tasks have to be performed). A particular emphasis is placed on the cognitive representation of dual-task identity information, which is examined in a recent research line employing the task-pair switching logic as an empirical approach. By conceptualizing a dual-task representation as a hierarchical multi-component representation, the review integrates the research line on the cognitive representation of dual-task identity information with those on the representation of component-task specific information and of component-task order information. Based on this conceptualization, the review provides a new theoretical contribution to dual-task research and highlights an integrative perspective on the different components of cognitive dual-task representations.

Supporting High-Level Tasks and Talk Through Lesson Study

A cross-grade level team participates in lesson study to develop skills in engaging students in deep mathematical thinking and high-level mathematical discourse.

A survey on pragmatic processing techniques

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.

An examination of the cognitive demand levels posed by problems in the middle grades mathematics textbooks

This research investigated the cognitive demand levels of mathematics tasks within the middle school mathematics textbooks endorsed by the Ministry of National Education for the academic year of the 2023-2024. To achieve this, a qualitative research method was utilized, and document analysis was performed. In this direction, the textbooks were analyzed using descriptive analysis. The tasks within the textbooks were examined through the lens of Smith and Stein’s (1998) theoretical framework. The findings indicate that the majority of mathematics tasks in middle school mathematics textbooks exhibit a low level of cognitive demand. It was found that over 80% of the tasks fell into the categories of memorization and procedures without connections levels. It has been revealed that tasks at the levels of procedures with connections and doing mathematics, which are high cognitive demand levels, are uncommon. This shows that textbooks do not sufficiently support the potential of students to develop mathematical thinking and problem-solving skills. Based on these results, various suggestions have been made for textbooks. The most important of these suggestions include ensuring that students are cognitively exposed to higher level tasks and increasing the number of tasks that will improve their mathematical thinking skills. Thus, it will be possible for students to have a more effective learning process by deepening their mathematical understanding.

New color channel driven physical lighting model for low-light image enhancement

Outdoor imaging systems, affected by low-light conditions, generally produce low-quality images with poor visibility. Low-quality images can directly influence high-level tasks such as surveillance and autonomous navigation systems. Enhancing the images captured under inadequate lighting conditions aims to generate higher visual quality in these images. However, current low-light enhancement methods may result in color unnaturalness, information loss, and strange artifacts. We propose a new color channel-driven physical lighting model (NCC-PLM) to respond to these issues to improve image quality. More concretely, we first apply a gamma correction to the input image according to its darkness degree, which is determined by its average intensity value. Then, we introduce a new color channel prior to estimate the environmental light (EL) and light scattering attenuation rate (LSAR). Finally, the enhanced image is obtained through the estimations and physical lighting model. Experimental results on various datasets demonstrate the proposed method's effectiveness and superiority over the compared methods both visually and qualitatively. Specifically, we enhance the visual quality of low-light images by revealing intricate details and maintaining color consistency, leading to a natural appearance.

Universal institutional mechanism for ensuring the right to development

The article describes the institutional mechanism for ensuring the right to development at the international universal level. The functions and mandate of the Office of the United Nations High Commissioner for Human Rights, the Working Group on the Right to Development, the High-level Task Force on the Implementation of the Right to Development, the Special Rapporteur on the Right to Development, the Expert Mechanism on the Right to Development, etc. are described. It is noted that the right to development is the only human right specifically mentioned in resolution 48/141 of the UN General Assembly, by which it established the position of High Commissioner and the Office of the High Commissioner for Human Rights. It is highlighted the functions of the Office of the High Commissioner for Human Rights in ensuring the right to development. It is described the mandate of the Working Group on the Right to Development, which includes: monitoring and analysis of progress made in the promotion and implementation of the right to development at the national and international levels, making recommendations and further analysis of obstacles to its full implementation; review of reports and any other information submitted by States, UN agencies, other relevant international organisations and non-governmental organisations on the relationship between their activities and the right to development, etc. It is noted that the High-level Task Force on the Implementation of the Right to Development was established within the framework of the Working Group on the Right to Development to assist it in fulfilling its mandate. It is stated that the Special Rapporteur on the Right to Development contributes to the realisation of the right to development by developing practical guidance to implement the right and to integrate it within development policies and programmes at local, national, regional and international levels. The functions and mandate of the Special Rapporteur are highlighted. It is described the activities of the Expert Mechanism on the Right to Development, which provides the Human Rights Council with thematic expertise on the right to development in searching for, identifying and sharing best practices with Member States and promotes the implementation of the right to development worldwide.

Enhanced “learning to learn” through a hierarchical dual-learning system: the case of action video game players

In contrast to conventional cognitive training paradigms, where learning effects are specific to trained parameters, playing action video games has been shown to produce broad enhancements in many cognitive functions. These remarkable generalizations challenge the conventional theory of generalization that learned knowledge can be immediately applied to novel situations (i.e., immediate generalization). Instead, a new “learning to learn” theory has recently been proposed, suggesting that these broad generalizations are attained because action video game players (AVGPs) can quickly acquire the statistical regularities of novel tasks in order to increase the learning rate and ultimately achieve better performance. Although enhanced learning rate has been found for several tasks, it remains unclear whether AVGPs efficiently learn task statistics and use learned task knowledge to guide learning. To address this question, we tested 34 AVGPs and 36 non-video game players (NVGPs) on a cue-response associative learning task. Importantly, unlike conventional cognitive tasks with fixed task statistics, in this task, cue-response associations either remain stable or change rapidly (i.e., are volatile) in different blocks. To complete the task, participants should not only learn the lower-level cue-response associations through explicit feedback but also actively estimate the high-level task statistics (i.e., volatility) to dynamically guide lower-level learning. Such a dual learning system is modelled using a hierarchical Bayesian learning framework, and we found that AVGPs indeed quickly extract the volatility information and use the estimated higher volatility to accelerate learning of the cue-response associations. These results provide strong evidence for the “learning to learn” theory of generalization in AVGPs. Taken together, our work highlights enhanced hierarchical learning of both task statistics and cognitive abilities as a mechanism underlying the broad enhancements associated with action video game play.

Physical effort modulates perceptual awareness judgment independent of level of processing

Recent studies have emphasized the association between action and perceptual awareness, suggesting that action-related information can contribute to perceptual awareness. Given that the Level of Processing (LoP) hypothesis proposes that the emergence of awareness depends on the level of stimulus processing, the current study examines whether action impacts perceptual awareness across different processing levels. In Experiment 1, participants identified target stimuli’s color (low-level task) or category (high-level task) via mouse clicks, followed by visual awareness ratings. Experiment 2 replicated the tasks using hand-grip dynamometers. Results from Experiment 1 support the LoP theory, showing a more gradual emergence of awareness for low-level features and a more dichotomous emergence for high-level features. In Experiment 2, higher reported visual awareness ratings were observed at greater physical effort, regardless of task type. These results suggest that action-related information influences reported awareness of stimuli in the same way at low- and high-level stimulus processing.

Application of Functional Resonance Analysis and fuzzy TOPSIS to identify and prioritize factors affecting newly emerging risks

Conventional safety analyses in complex systems like air separation units (ASUs) often attributed accidents to linear, deterministic causes, such as operator error. However, acknowledging the intricate interdependence of process components necessitates a shift towards recognizing the complexity of incident causation. This study proposes a novel model that integrates Function Resonance Analysis Method (FRAM) and fuzzy logic analysis to address this growing need. The model facilitates the identification of emerging risks and assesses the impact of influential factors within a mixed qualitative and quantitative framework. The FRAM method is initially employed to identify emerging risks within the ASU. Subsequently, fuzzy multi-criteria decision-making methods are utilized to establish the relationships and weightage of influential factors. Data collection encompasses semi-structured interviews, direct observation, process workflow analysis, and the involvement of a panel of engineers and operators from the investigated ASU. Utilizing FMV software for FRAM analysis, functions associated with air compression, distribution, and storage exhibit high resonance. This signifies substantial variability and a heightened potential for incidents or deviations in these functions and higher-level tasks. Furthermore, Fuzzy TOPSIS analysis reveals that education and experience emerge as the most impactful factors governing newly emerging risk. This model demonstrates significant merit for risk assessment and incident investigation. Its non-linear and dynamic nature empowers the proactive identification and examination of processes, incidents, and emerging risks before deviations or accidents occur.

Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.

To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview. 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1. The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support. Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data.

Reinforcement learning with predefined and inferred reward machines in stochastic games

This paper focuses on Multi-Agent Reinforcement Learning (MARL) in non-cooperative stochastic games, particularly addressing the challenge of task completion characterized by non-Markovian reward functions. We employ Reward Machines (RMs) to incorporate high-level task knowledge. Firstly, we introduce Q-learning with Reward Machines for Stochastic Games (QRM-SG), where RMs are predefined and available to agents. QRM-SG learns each agent’s best-response policy at Nash equilibrium by defining the Q-function in augmented state space that integrates the stochastic game and RM states. The Lemke-Howson method is utilized to compute the best-response policies for the stage game defined by the current Q-functions at each time step. Subsequently, we explore a more challenging scenario where RMs are unavailable and propose Multi-Agent Reinforcement learning with Concurrent High-level knowledge inference (MARCH). MARCH uses automata learning to learn RMs iteratively and combines this process with QRM-SG for learning the best-response policies. The RL episodes where the obtained rewards are inconsistent with the rewards from the current RMs trigger the inference of new RMs. We prove QRM-SG and MARCH converge to the best-response policies under certain conditions. Two scenarios are conducted to demonstrate the superior performance of QRM-SG and MARCH compared to baseline methods.

LEARNING TO WORK WITH AI IN DIGITAL MEDIA COPYWRITING (DMC): A CASE STUDY ON INTRODUCING CHATGPT TO CORPORATE CONTENT CREATION

The introduction of ChatGPT in late 2022 resulted in a paradigm shift within digital media copywriting as a growing form of contemporary media, whereby artificial intelligence (AI) has become an indispensable tool in content creation. This case study investigates the pro- cess of introducing ChatGPT, a large language model, to corporate content creation and exam- ines its impact on human copywriters and their changing scope of work by means of measuring the time logged for content creation and by a qualitative investigation of edits of the content draft prepared by ChatGPT, as well as by means of interviewing the editors and authors. The study was conducted in a corporate environment, with clearly defined voice and tone guidelines, where ChatGPT was used to generate social media posts and blogs. The results of this case study show that ChatGPT significantly reduced the time and effort required for copywriting, allowing copy- writers to focus on higher-level tasks such as fine-tuning the message, setting the proper voice and tone, and coming up with better hooks. Additionally, ChatGPT generated content that was found to be, on the whole, of similar or better quality to that produced by human copywriters, demonstrating the potential of AI to augment human capabilities in content creation. As the use of AI in content creation continues to expand, this study can serve as a baseline for further investigations. Finally, it includes possible directions for copywriters’ and students’ up-skilling efforts that enable them to work effectively with ChatGPT and similar AI tools.

From silicon to solutions: AI's impending impact on research and discovery

The social sciences have long relied on comparative work as the foundation upon which we understand the complexities of human behavior and society. However, as we go deeper into the era of artificial intelligence (AI), it becomes imperative to move beyond mere comparison (e.g., how AI compares to humans across a range of tasks) to establish a visionary agenda for AI as collaborative partners in the pursuit of knowledge and scientific inquiry. This paper articulates an agenda that envisions AI models as the preeminent scientific collaborators. We advocate for the profound notion that our thinking should evolve to anticipate, and include, AI models as one of the most impactful tools in the social scientist's toolbox, offering assistance and collaboration with low-level tasks (e.g., analysis and interpretation of research findings) and high-level tasks (e.g., the discovery of new academic frontiers) alike. This transformation requires us to imagine AI's possible/probable roles in the research process. We defend the inevitable benefits of AI as knowledge generators and research collaborators—agents who facilitate the scientific journey, aiming to make complex human issues more tractable and comprehensible. We foresee AI tools acting as co-researchers, contributing to research proposals and driving breakthrough discoveries. Ethical considerations are paramount, encompassing democratizing access to AI tools, fostering interdisciplinary collaborations, ensuring transparency, fairness, and privacy in AI-driven research, and addressing limitations and biases in large language models. Embracing AI as collaborative partners will revolutionize the landscape of social sciences, enabling innovative, inclusive, and ethically sound research practices.

How we perceive the width of grasped objects: Insights into the central processes that govern proprioceptive judgements.

Low-level proprioceptive judgements involve a single frame of reference, whereas high-level proprioceptive judgements are made across different frames of reference. The present study systematically compared low-level (grasp grasp) and high-level (vision grasp, grasp vision) proprioceptive tasks, and quantified the consistency of grasp vision and possible reciprocal nature of related high-level proprioceptive tasks. Experiment 1 (n = 30) compared performance across vision grasp, a grasp vision and a grasp grasp tasks. Experiment 2 (n = 30) compared performance on the grasp vision task between hands and over time. Participants were accurate (mean absolute error 0.27 cm [0.20 to 0.34]; mean [95% CI]) and precise ( = 0.95 [0.93 to 0.96]) for grasp grasp judgements, with a strong correlation between outcomes (r = -0.85 [-0.93 to -0.70]). Accuracy and precision decreased in the two high-level tasks ( = 0.86 and 0.89; mean absolute error = 1.34 and 1.41 cm), with most participants overestimating perceived width for the vision grasp task and underestimating it for grasp vision task. There was minimal correlation between accuracy and precision for these two tasks. Converging evidence indicated performance was largely reciprocal (inverse) between the vision grasp and grasp vision tasks. Performance on the grasp vision task was consistent between dominant and non-dominant hands, and across repeated sessions a day or week apart. Overall, there are fundamental differences between low- and high-level proprioceptive judgements that reflect fundamental differences in the cortical processes that underpin these perceptions. Moreover, the central transformations that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocaltasks. KEY POINTS: Low-level proprioceptive judgements involve a single frame of reference (e.g. indicating the width of a grasped object by selecting from a series of objects of different width), whereas high-level proprioceptive judgements are made across different frames of reference (e.g. indicating the width of a grasped object by selecting from a series of visible lines of different length). We highlight fundamental differences in the precision and accuracy of low- and high-level proprioceptive judgements. We provide converging evidence that the neural transformations between frames of reference that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. This stability is likely key to precise judgements and accurate predictions in high-level proprioception.

Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution

Abstract Large language models (LLMs) are a type of artificial intelligence (AI) that can perform various natural language processing tasks. The adoption of LLMs has become increasingly prominent in scientific writing and analyses because of the availability of free applications such as ChatGPT. This increased use of LLMs not only raises concerns about academic integrity but also presents opportunities for the research community. Here we focus on the opportunities for using LLMs for coding in ecology and evolution. We discuss how LLMs can be used to generate, explain, comment, translate, debug, optimise and test code. We also highlight the importance of writing effective prompts and carefully evaluating the outputs of LLMs. In addition, we draft a possible road map for using such models inclusively and with integrity. LLMs can accelerate the coding process, especially for unfamiliar tasks, and free up time for higher level tasks and creative thinking while increasing efficiency and creative output. LLMs also enhance inclusion by accommodating individuals without coding skills, with limited access to education in coding, or for whom English is not their primary written or spoken language. However, code generated by LLMs is of variable quality and has issues related to mathematics, logic, non‐reproducibility and intellectual property; it can also include mistakes and approximations, especially in novel methods. We highlight the benefits of using LLMs to teach and learn coding, and advocate for guiding students in the appropriate use of AI tools for coding. Despite the ability to assign many coding tasks to LLMs, we also reaffirm the continued importance of teaching coding skills for interpreting LLM‐generated code and to develop critical thinking skills. As editors of MEE, we support—to a limited extent—the transparent, accountable and acknowledged use of LLMs and other AI tools in publications. If LLMs or comparable AI tools (excluding commonly used aids like spell‐checkers, Grammarly and Writefull) are used to produce the work described in a manuscript, there must be a clear statement to that effect in its Methods section, and the corresponding or senior author must take responsibility for any code (or text) generated by the AI platform.

DANSEN: Database Acceleration on Native Computational Storage by Exploiting NDP

This paper introduces DANSEN , the hardware accelerator component for neoDBMS, a full-stack computational storage system designed to manage on-device execution of database queries/transactions as a Near-Data Processing (NDP)-operation. The proposed system enables Database Management Systems (DBMS) to offload NDP-operations to the storage while maintaining control over data through a native storage interface . DANSEN provides an NDP-engine that enables DBMS to perform both low-level database tasks, such as performing database administration, as well as high-level tasks like executing SQL, on the smart storage device while observing the DBMS concurrency control. Furthermore, DANSEN enables the incorporation of custom accelerators as an NDP-operation, e.g., to perform hardware-accelerated ML inference directly on the stored data. We built the DANSEN storage prototype and interface on an Ultrascale+HBM FPGA and fully integrated it with PostgreSQL 12. Experimental results demonstrate that the proposed NDP approach outperforms software-only PostgreSQL using a fast off-the-shelf NVMe drive, and significantly improves the end-to-end execution time of an aggregation operation (similar to Q6 from CH-benCHmark, 150 million records) by ≈ 10.6 ×. The versatility of the proposed approach is also validated by integrating a compute-intensive data analytics application with multi-row results, outperforming PostgreSQL by ≈ 1.5 ×.

Metacognitive evaluation of postdecisional perceptual representations.

Perceptual confidence is thought to arise from metacognitive processes that evaluate the underlying perceptual decision evidence. We investigated whether metacognitive access to perceptual evidence is constrained by the hierarchical organization of visual cortex, where high-level representations tend to be more readily available for explicit scrutiny. We found that the ability of human observers to evaluate their confidence did depend on whether they performed a high-level or low-level task on the same stimuli, but was also affected by manipulations that occurred long after the perceptual decision. Confidence in low-level perceptual decisions degraded with more time between the decision and the response cue, especially when backward masking was present. Confidence in high-level tasks was immune to backward masking and benefitted from additional time. These results can be explained by a model assuming confidence heavily relies on postdecisional internal representations of visual stimuli that degrade over time, where high-level representations are more persistent.

Reinforcement Learning with Task Decomposition and Task-Specific Reward System for Automation of High-Level Tasks.

This paper introduces a reinforcement learning method that leverages task decomposition and a task-specific reward system to address complex high-level tasks, such as door opening, block stacking, and nut assembly. These tasks are decomposed into various subtasks, with the grasping and putting tasks executed through single joint and gripper actions, while other tasks are trained using the SAC algorithm alongside the task-specific reward system. The task-specific reward system aims to increase the learning speed, enhance the success rate, and enable more efficient task execution. The experimental results demonstrate the efficacy of the proposed method, achieving success rates of 99.9% for door opening, 95.25% for block stacking, 80.8% for square-nut assembly, and 90.9% for round-nut assembly. Overall, this method presents a promising solution to address the challenges associated with complex tasks, offering improvements over the traditional end-to-end approach.

CutFreq: Cut-and-Swap Frequency Components for Low-Level Vision Augmentation

Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.