After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi‐layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the (...) coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free‐energy carriers, and precursors for biosynthesis and stress‐related compounds. Here we will review the regulation of multi‐tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components. (shrink)

In a simple economic decision problem with multi-tasking the dimensionality of the problem is neither a necessary nor a sufficient measure of complexity. Rather, dimension is good measure of complexity when there is an aggregate resource constraint that creates an interaction between the different activities, resulting in a problem with high algorithmic complexity.

The paper introduces the problem of unawareness into multi-dimensional Principal–Agent theory. We introduce two key parameters to describe the problem, the extent and the effect of unawareness, show under what conditions it is optimal for the Principal to propose an incomplete or a complete contract, and characterize the incentive power of optimal linear contracts. If Agents differ in their unawareness, optimal incentive schemes can be distorted for both aware and unaware Agents, because, different from standard contract theory, the single-crossing (...) property fails to hold. In this case, even aware Agents can be subject to inefficiently high or low incentives. (shrink)

I argue that it is not possible to accurately represent our task settings as close environments with a single well defined cost structure. Natural environments are places where many things are done, often at the same time, and often by many people. To appreciate the way such invariants of everyday life affect design I present a case study, a micro-analysis of espresso making at Starbucks to show the challenges facing a cost structure approach.

Educational literature has tended to focus, explicitly and implicitly, on two kinds of task orientation: the ability either to focus on a single task, or to multi-task. A third form of orientation characterises many highly successful people. This is the ability to combine several tasks into one: to ‘kill two birds with one stone’. This skill characterises people with initiative, who exercise judgment, deliberation and creative imagination in their personal organisation. The motivation to work in this way indicates personal (...) commitment rather than mere compliance. Focusing on its development educationally implies a shift from either/or, linear, methodical, mechanical and technicist thinking to encouraging individual and collective responsibility, initiative and risk-taking, resulting in unpredictable outcomes. The emphasis on judgment renders the issues as ethical rather than narrowly instrumental. Dominant ethical approaches are, therefore, considered in relation to this in terms of its application for teaching. (shrink)

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) is a nuclear protein that plays a crucial role in various biological functions, such as RNA splicing and chromatin organization. HNRNPU/scaffold attachment factor A (SAF‐A) activities are essential for regulating gene expression, DNA replication, genome integrity, and mitotic fidelity. These functions are critical to ensure the robustness of developmental processes, particularly those involved in shaping the human brain. As a result, HNRNPU is associated with various neurodevelopmental disorders (HNRNPU‐related neurodevelopmental disorder, HNRNPU‐NDD) characterized by developmental delay (...) and intellectual disability. Our research demonstrates that the loss of HNRNPU function results in the death of both neural progenitor cells and post‐mitotic neurons, with a higher sensitivity observed in the former. We reported that HNRNPU truncation leads to the dysregulation of gene expression and alternative splicing of genes that converge on several signaling pathways, some of which are likely to be involved in the pathology of HNRNPU‐related NDD. (shrink)

Cocreating meaning in collaboration is challenging. Success is often determined by people's abilities to coordinate their language to converge upon shared mental representations. Here we explore one set of low‐level linguistic behaviors, linguistic alignment, that both emerges from, and facilitates, outcomes of high‐level convergence. Linguistic alignment captures the ways people reuse, that is, “align to,” the lexical, syntactic, and semantic forms of others' utterances. Our focus is on the temporal change of multi‐level linguistic alignment, as well as how alignment (...) is related to communicative outcomes within a unique collaborative problem‐solving paradigm. The primary task, situated within a virtual educational video game, requires creative thinking between three people where the paths for possible solutions are highly variable. We find that over time interactions are marked by decreasing lexical and syntactic alignment, with a trade‐off of increasing semantic alignment. However, greater semantic alignment does not translate into better team performance. Overall, these findings provide greater clarity on the role of linguistic coordination within complex and dynamic collaborative problem‐solving tasks. (shrink)

Human instructors often refer to objects and actions involved in a task description using both linguistic and non-linguistic means of communication. Hence, for robots to engage in natural human-robot interactions, we need to better understand the various relevant aspects of human multi-modal task descriptions. We analyse reference resolution to objects in a data collection comprising two object manipulation tasks and find that 78.76% of all referring expressions to the objects relevant in Task 1 are verbally underspecified and 88.64% of (...) all referring expressions are verbally underspecified in Task 2. The data strongly suggests that a language processing module for robots must be genuinely multi-modal, allowing for seamless integration of information transmitted in the verbal and the visual channel, whereby tracking the speaker’s eye gaze and gestures as well as object recognition are necessary preconditions. (shrink)

In my discussion of the articles in this special issue of Techné I will relate the multiple perspectives on the phenomenon of driving-celling to the core debate, which concerns how this dual activity may be related to the need to have a concentrated focus, on the one hand, or to the possibility of a form of multitasking, on the other. The contributors show multiple perspectives on this phenomenon and draw from a range of authors on the roles of attention, embodiment (...) and perception. (shrink)

Do multi-level selection explanations of the evolution of social traits deepen the understanding provided by single-level explanations? Central to the former is a mathematical theorem, the multi-level Price decomposition. I build a framework through which to understand the explanatory role of such non-empirical decompositions in scientific practice. Applying this general framework to the present case places two tasks on the agenda. The first task is to distinguish the various ways of suppressing within-collective variation in fitness, and moreover to (...) evaluate their biological interest. I distinguish four such ways: increasing retaliatory capacity, homogenising assortment, and collapsing either fitness structure or character distribution to a mean value. The second task is to discover whether the third term of the Price decomposition measures the effect of any of these hypothetical interventions. On this basis I argue that the multi-level Price decomposition has explanatory value primarily when the sharing-out of collective resources is `subtractable'. Thus its value is more circumscribed than its champions Sober and Wilson (1998) suppose. (shrink)

The purpose of this paper is to investigate the effect of intra-organizational networking on individual task performance, via employability. Moreover, this study also examines whether this relationship differs for younger versus older employees. A self-report questionnaire was distributed among a sample of employees working in a range of different types of organizations. We conclude that employability fully mediates the relationship between intra-organizational networking and individual task performance. However, this mediation effect did not vary between younger and older employees. This study (...) extends past research by applying a human capital perspective and life-span development frameworks for explaining employability and task performance enhancement across one’s working life. It provides useful insights for stimulating career development and individual performance growth, by means of social capital, herewith increasing the individual employee’s chance to survive in nowadays’ labor markets. (shrink)

The development of artificial intelligence technology has brought changes to various industries. Under the concept of green sustainable development, how to use the progress of science and technology to implement low-carbon strategies is a problem that every enterprise should consider. Aiming at the problem of picking up goods in logistics industry, this paper proposed a secondary task assignment theory for multiple unmanned aerial vehicle based on green scheduling. The theory greatly improves the utilization rate of unmanned aerial vehicle and reduces (...) the energy consumption. We analyzed the advantages and disadvantages of local optimal algorithm and global optimal algorithm in time and energy consumption. Through repeated experiments in different ranges, we have well verified the high efficiency and general applicability of this theory, which can provide theoretical and practical implications for logistics enterprises using UAV to achieve low-carbon sustainable development in the future. (shrink)

This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in reinforcement learning (...) tasks, di erent ways of partitioning a task and using agents selectively based on partitioning. Based on the analysis, some heuristic methods are described and experimentally tested. We nd that some o -line heuristic methods performed the best, signi cantly better than single-agent models. (shrink)

In the real-world complex product design process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing (...) executing time strategy is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency. (shrink)

Multi-voxel pattern analysis (MVPA) is a popular analytical technique in neuroscience that involves identifying patterns in fMRI BOLD signal data that are predictive of task conditions. But the technique is also frequently used to make inferences about the regions of the brain that are most important to the tasks in question, and our analysis shows that this is a mistake. MVPA does not provide a reliable guide to what information is being used by the brain during cognitive tasks, nor (...) where that information is. This is due in part to inherent run to run variability in the decision space generated by the classifier, but there are also several other issues, discussed below, that make inference from the characteristics of the learned models to relevant brain activity deeply problematic. These issues have significant implications both for many papers already published, and for how the field uses this technique in the future. (shrink)

Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and (...) cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries. (shrink)

In a Mental Imagery Brain-Computer Interface the user has to perform a specific mental task that generates electroencephalography components, which can be translated in commands to control a BCI system. The development of a high-performance MI-BCI requires a long training, lasting several weeks or months, in order to improve the ability of the user to manage his/her mental tasks. This works aims to present the design of a MI-BCI combining mental imaginary and cognitive tasks for a severely motor impaired user, (...) involved in the BCI race of the Cybathlon event, a competition of people with severe motor disability. In the BCI-race, the user becomes a pilot in a virtual race game against up to three other pilots, in which each pilot has to control his/her virtual car by his/her mental tasks. We present all the procedures followed to realize an effective MI-BCI, from the user's first contact with a BCI technology to actually controlling a video-game through her EEG. We defined a multi-stage user-centered training protocol in order to successfully control a BCI, even in a stressful situation, such as that of a competition. We put a specific focus on the human aspects that influenced the long training phase of the system and the participation to the competition. (shrink)

As an important component to promote the development of affective brain–computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the (...) joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task. (shrink)

The paper presents a new model of the structure of basic arithmetical representa-tions encoded in minds which enable them to solve simple story-tasks. According to the dominating paradigm the process of acquiring basic counting abilities culminates in encoding the exact number line in mind. This linear number representation enables the mind to solve simple story-tasks which do not require any mathematical mastery knowledge comprising laws, definitions and theorems. Some researchers try to show that the process of encoding the exact number (...) line stems from transformations of the approximate number line (the mental number line) whereas others model this process as being dependent on the linguistic and logical resources of mind. (shrink)

Spatial objects may not only be perceived visually but also by touch. We report recent experiments investigating to what extent prior object knowledge acquired in either the haptic or visual sensory modality transfers to a subsequent visual learning task. Results indicate that even mental object representations learnt in one sensory modality may attain a multi-modal quality. These findings seem incompatible with picture-based reasoning schemas but leave open the possibility of modality-specific reasoning mechanisms.

Self-efficacy is an important predictor of learning and achievement. By definition, self-efficacy requires a task-specific assessment, in which students are asked to evaluate whether they can solve concrete tasks. An underlying assumption in previous research into such assessments was that self-efficacy is a one-dimensional construct. However, empirical evidence for this assumption is lacking, and research on students’ performance suggests that it depends on various task characteristics. The present study explores the potential multi-dimensionality of self-efficacy in the topic of linear (...) functions. More specifically, we investigate how three task characteristics – the representational format, embedding in a real-life context, or the required operation – are related to students’ self-efficacy. We asked 8th and 9th graders to evaluate their self-efficacy on specific linear function tasks which systematically varied along the three dimensions of task characteristics. Using confirmatory factor analysis, we found that a two-dimensional model which includes the task characteristic of real-life context fitted the data better than other two-dimensional models or a one-dimensional model. These results suggest that self-efficacy with linear functions is empirically separable with respect to tasks with vs. without a real-life context. This means that in their self-evaluation of linear function tasks students particularly rely on whether or not the linear function task is embedded in a real-life context. This study highlights the fact that even within a specific content domain students’ self-efficacy can be considered a multi-dimensional construct. (shrink)

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, (...) a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches. (shrink)

Being a player with an F. A. Premier football academy is very prestigious for young players, but it can also be very stressful too. Coping with stress is particularly important given that one of the undesirable consequences linked to chronic stress is athlete burnout, which may also negatively impact psychological well-being. Understanding the most effective ways to cope with stress, therefore, is important for optimizing academy athlete education. Consequently, the aim of the present study was to examine whether coping predicted (...) changes in athlete burnout, and whether athlete burnout predicted changes in well-being across 14 weeks of the competitive season. A sample of 26, under-18 and under-23, male F.A. Premier academy athletes completed weekly assessments of coping, athlete burnout, and psychological well-being on 14 separate occasions. The results of within-person analyses revealed that task-oriented coping predicted decreases in athlete burnout, which in turn predicted decreased well-being. Teaching high-level academy athletes task-oriented coping strategies may be useful in reducing athlete burnout, which may additionally protect athletes' well-being. (shrink)

Recent advances in automation technology have increased the opportunity for collaboration between humans and multiple autonomous systems such as robots and self-driving cars. In research on autonomous system collaboration, the trust users have in autonomous systems is an important topic. Previous research suggests that the trust built by observing a task can be transferred to other tasks. However, such research did not focus on trust in multiple different devices but in one device or several of the same devices. Thus, we (...) do not know how trust changes in an environment involving the operation of multiple different devices such as a construction site. We investigated whether trust can be transferred among multiple different devices, and investigated the effect of two factors: the similarity among multiple devices and the agency attributed to each device, on trust transfer among multiple devices. We found that the trust a user has in a device can be transferred to other devices and that attributing different agencies to each device can clarify the distinction among devices, preventing trust from transferring. (shrink)

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse (...) planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration. (shrink)

Here we present a systematic plan to the experimental study of test–retest reliability in the multitasking domain, adopting the multitrait-multimethod approach to evaluate the psychometric properties of performance in Düker-type speeded multiple-act mental arithmetic. These form of tasks capacitate the experimental analysis of integrated multi-step processing by combining multiple mental operations in flexible ways in the service of the overarching goal of completing the task. A particular focus was on scoring methodology, particularly measures of response speed variability. To this (...) end, we present data of two experiments with regard to test–retest reliability, between-measures correlational structure, and stability. Finally, we compared participants with high versus low performance variability to assess ability-related differences in measurement precision, which is especially relevant in the applied fields of clinical neuropsychology. The participants performed two classic integrated multi-act arithmetic tasks, combining addition and verification and addition and comparison. The results revealed excellent test–retest reliability for the standard and the variability measures. The analysis of between-measures correlational structure revealed the typical pattern of convergent and discriminant relationships, and also, that absolute response speed variability was highly correlated with average speed, indicating that these measures mainly deliver redundant information. In contrast, speed-adjusted variability revealed discriminant validity being correlated to a much lesser degree with average speed, indicating that this measure delivers additional information not already provided by the speed measure. Furthermore, speed-adjusted variability was virtually unaffected by test–retest practice, which makes this measure interesting in situations with repeated testing. (shrink)

BackgroundMulti-centre studies generally cost more than single-centre studies because of larger sample sizes and the need for multiple ethical approvals. Multi-centre studies include clinical trials, clinical quality registries, observational studies and implementation studies. We examined the costs of two large Australian multi-centre studies in obtaining ethical and site-specific approvals.MethodsWe collected data on staff time spent on approvals and expressed the overall cost as a percent of the total budget.ResultsThe total costs of gaining approval were 38 % of the (...) budget for a study of 50 centres (mean cost AUD $6960 per site) and 2 % for a study of 11 centres (mean cost AUD $2300 per site). Seventy-five and 90 % of time was spent on repeated tasks, respectively, and many time-consuming tasks, such as reformatting documents, did nothing to improve the study design or participant safety.ConclusionsImprovements have been made to the ethical approval application system, but more gains could be made without increasing risks of harm to research participants. We propose that ethical review bodies and individual sites publish statistics on how long they take to process approvals which could then be nationally benchmarked. (shrink)

This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple agents, without a priori domain knowledge regarding task structures. Partitioning a state/input space into multiple regions helps to exploit the di erential characteristics of regions and di erential characteristics of agents, thus facilitating learning and reducing the complexity of agents especially when function approximators are used. We develop a method for optimizing the partitioning of the space through experience without the use of a priori domain knowledge. The method (...) is experimentally tested and compared to a number of other algorithms. As expected, we found that the multi-agent method with automatic partitioning outperformed single-agent learning. (shrink)

In this paper, a novel experimental task is developed for testing the highly influential, but experimentally underexplored, possible worlds account of conditionals (Stalnaker, 1968; Lewis, 1973). In Experiment 1, this new task is used to test both indicative and subjunctive conditionals. For indicative conditionals, five competing truth tables are compared, including the previously untested, multi-dimensional possible worlds semantics of Bradley (2012). In Experiment 2, these results are replicated and it is shown that they cannot be accounted for by an (...) alternative hypothesis proposed by our reviewers. In Experiment 3, individual variation in truth assignments of indicative conditionals is investigated via Bayesian mixture models that classify participants as following one of several competing models. As a novelty of this study, it is found that a possible worlds semantics of Lewis and Stalnaker is capable of accounting for participants’ aggregate truth value assignments in this task. Applied to indicative conditionals, we show across three experiments, that the theory both captures participants’ truth values at the aggregate level (Experiments 1 and 2) and that it makes up the largest subgroup in the analysis of individual variation in our experimental paradigm (Experiment 3). (shrink)

Those who would enquire into therelationship between health conceptions and health care consequences are faced with a formidable task. In order to make this challenge manageable it is necessary to define the scope of the task as precisely as possible. Are we, for instance, faced with a purely theoretical challenge; a task for applied philosophy, or must we employ multi-disciplinary methods?This paper argues that while philosophy has a central clarifying role, inquiry into the relationship between health conceptions and health (...) care organisation can be done properly only through the combined efforts of several disciplines. Unless we are to be concerned only with abstract models it is essential to take account of the reality of health care situations. Given this it is suggested that the study of health conceptions is only a part of a greater task (unless all conceptions are to count as health conceptions). What is needed is understanding of the possible and actual purposes of health care, and detailed study of their practical implications. (shrink)

Aspect-level sentiment classification is an interesting and challenging research task to identify the sentiment polarities of aspect words in sentences. Previous attention-based methods rarely consider the position information of aspect and contextual words. For an aspect word in a sentence, its adjacent words should be given more attention than the long distant words. Based on this consideration, this article designs a position influence vector to represent the position information between an aspect word and the context. By combining the position influence (...) vector, multi-head self-attention mechanism and bidirectional gated recurrent unit, a position-enhanced multi-head self-attention network based BiGRU model is proposed. To verify the effectiveness of the proposed model, this article makes a large number of experiments on SemEval2014 restaurant, SemEval2014 laptop, SemEval2015 restaurant, and SemEval2016 restaurant data sets. The experiment results show that the performance of the proposed PMHSAT-BiGRU model is obviously better than the baselines. Specially, compared with the original LSTM model, the Accuracy values of the proposed PMHSAT-BiGRU model on the four data sets are improved by 5.72, 6.06, 4.52, and 3.15%, respectively. (shrink)

Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). There has been little research on user preference towards HRI interfaces. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of (...) the participants, including their attentional activities and conversational activities, the temporal characteristics of these social behaviours, as well as questionnaires. It has been revealed that social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life. (shrink)