Task Model Research Articles

Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics

Intelligent logistics empowered by artificial intelligence (AI) has become an inevitable trend in the development of modern logistics, thus a convenient and efficient logistics system has attracted widespread attention. However, how to use AI to execute computation-intensive applications on resource-constrained logistics vehicles still faces enormous challenges. For the dependent applications in intelligent logistics, this paper investigates a task dynamic offloading strategy for logistics vehicles-edge collaboration with multiple dependent tasks, considering the inter-task dependency, to guarantee the quality of service (QoS) requirements of logistics vehicles. Firstly, the dependent application ARCore is modeled and transformed into a model with a linear execution sequence. Then, based on this task model, the joint task offloading and resource allocation problem is formulated. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the delay tolerance and computing resource constraints of the tasks. Furthermore, we propose a federated logistics vehicles-edge collaborative computation (FECC) offloading framework to solve the optimization problem, which only requires each agent to share its model parameters without sharing local training data, thereby reducing the computation complexity and signaling overhead of the multi-agent training process. Numerical results show that the proposed strategy has significant advantages in terms of total system cost compared to the baseline strategy.

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Heterogeneous multi-core platforms, such as ARM big.LITTLE are widely used to execute embedded applications under multiple and contradictory constraints, such as energy consumption and real-time execution. To fulfill these constraints and optimize system performance, application tasks should be efficiently mapped on multi-core platforms. Embedded applications are usually tolerant to approximated results but acceptable Quality-of-Service (QoS). Modeling embedded applications by using the elastic task model, namely, Imprecise Computation (IC) task model, can balance system QoS, energy consumption, and real-time performance during task deployment. However, state-of-the-art approaches seldom consider the problem of IC task deployment on heterogeneous multi-core platforms. They typically neglect task migration, which can improve the solutions due to its flexibility during the task deployment process. This paper proposes a novel QoS-aware task deployment method to maximize system QoS under energy and real-time constraints, where frequency assignment, task allocation, scheduling, and migration are optimized simultaneously. The task deployment problem is formulated as mixed-integer non-linear programming. Then, it is linearized to mixed-integer linear programming to find the optimal solution. Furthermore, based on problem structure and problem decomposition, we propose a novel heuristic with low computational complexity. The sub-problems regarding frequency assignment, task allocation, scheduling, and adjustment are considered and solved in sequence. Finally, the simulation results show that the proposed task deployment method improves the system QoS by 31.2% on average (up to 112.8%) compared to the state-of-the-art methods and the designed heuristic achieves about 53.9% (on average) performance of the optimal solution with a negligible computing time.

Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: Research and implementation

Information technologies, particularly artificial intelligence methods, involve more and more deeply into all spheres of human activity: science, technology, art, and education. Ukraine also has sufficient potential and needs to develop educational support, which is the subject of this paper. The work aims to demonstrate the components of information technology for the creation of Intelligent Tutoring Systems (ITS), which are involved in studying various engineering disciplines. The work includes methods of system analysis, mathematical and simulation modeling, technical diagnostics, and artificial intelligence. The proposed models and methods are implemented in ITS prototypes for teaching mathematics, programming, and the automatic control theory. The Intelligent Tutoring Systems were implemented in the educational process of KhAI University and other institutions in Ukraine, Great Britain, Austria, and China. Experimental studies have shown increased student learning success rates using ITS compared to traditional methods. Improved and adapted for computer training methods of technical diagnostics, Bayesian networks, and developed models of algorithmic tasks, the learning process and the learner are valuable from a scientific point of view. In a practical sense, the obtained results can be used to create new specialized ITSs and build an expandable common learning platform integrating the basic disciplines of a specific educational field.

Performance Evaluation of Environmental Sound Classification: A Machine Learning Stacking and Multi-Criteria Metrics Based Approach

This study proposes an Environment Sound Classification Task (ESC) model that includes numerous element channels given as a contribution to Machine learning with an Attention instrument. ESC is a significant testing issue. The interest in the paper lies in utilizing different part channels involving the MFCCs-Mel Frequency Cepstral Coefficients a mutual module in speaker detection and artificial speech systems. LPCs-Linear Prediction Coefficients and Linear Prediction Cepstral Coefficients were the most commonly used types in ASR- Automated speech recognition. The paper also discusses some basic features of MFCCs and how to put them into practice. The techniques used for this project are Background Gaussian Noise and Time Shifting, by observing that every technique is implemented with a provided probability, however, when at the time of generation of a new sample the same spectrogram input may have several combinations. We go through the blend information expansion method to additional lift execution. Our model can accomplish cutting-edge execution on three benchmark climate sound characterization datasets, for example, the UrbanSound8K.

Suicide prevention and ketamine: insights from computational modeling.

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

A multi-AGV fast path planning method based on improved CBS algorithm in workshops

Automatic guided vehicles (AGVs) are an important component of workshop logistics distribution systems. With the continuous increase in human cost, unmanned workshops are becoming increasingly popular, and an increasing number of AGVs are being applied to workshop logistics distribution systems. However, with the increasing number of AGVs in the workshop, planning a path quickly for each AGV is challenging, and the response time of multi-AGV fast path planning cannot meet actual requirements. To solve this problem, a multi-AGV fast path planning method based on an improved conflict-based search (ICBS) algorithm is proposed. First, the path planning problem is analyzed, and the environment expression, task, objective and constraint models are established. Subsequently, a two-layer ICBS algorithm is proposed to achieve fast path planning. At the bottom layer, an improved A* algorithm is adopted, and the selection strategy of the nodes is changed when multiple minimum values are equal. In the upper layer, the conflict number, conflict objective and conflict constraint sets are established. Three priority rules including Most Conflicts First (MCF) rule, Earliest Conflict First (ECF) rule, Single Search for Conflict Point (SSCP) rule are used. And a conflict elimination strategy is proposed. Finally, the superiority of the algorithm-solving is verified based on benchmark and actual workshop maps.

Compositional verification of embedded real-time systems

In an embedded real-time system (ERTS), real-time tasks (software) are typically executed on a multicore shared-memory platform (hardware). The number of cores is usually small, contrasted with a larger number of complex tasks that share data to collaborate. Since most ERTSs are safety-critical, it is crucial to rigorously verify their software against various real-time requirements under the actual hardware constraints (concurrent access to data, number of cores). Both the real-time systems and the formal methods communities provide elegant techniques to realize such verification, which nevertheless face major challenges. For instance, model checking (formal methods) suffers from the state-space explosion problem, whereas schedulability analysis (real-time systems) is pessimistic and restricted to simple task models and schedulability properties. In this paper, we propose a scalable and generic approach to formally verify ERTSs. The core contribution is enabling, through joining the forces of both communities, compositional verification to tame the state-space size. To that end, we formalize a realistic ERTS model where tasks are complex with an arbitrary number of jobs and job segments, then show that compositional verification of such model is possible, using a hybrid approach (from both communities), under the state-of-the-art partitioned fixed-priority (P-FP) with limited preemption scheduling algorithm. The approach consists of the following steps, given the above ERTS model and scheduling algorithm. First, we compute fine-grained data sharing overheads for each job segment that reads or writes some data from the shared memory. Second, we generalize an algorithm that, aware of the data sharing overheads, computes an affinity (task-core allocation) guaranteeing the schedulability of hard-real-time (HRT) tasks. Third, we devise a timed automata (TA) model of the ERTS, that takes into account the affinity, the data sharing overheads and the scheduling algorithm, on which we demonstrate that various properties can be verified compositionally, i.e., on a subset of cores instead of the whole ERTS, therefore reducing the state-space size. In particular, we enable the scalable computation of tight worst-case response times (WCRTs) and other tight bounds separating events on different cores, thus overcoming the pessimism of schedulability analysis techniques. We fully automate our approach and show its benefits on three real-world complex ERTSs, namely two autonomous robots and an automotive case study from the WATERS 2017 industrial challenge.

KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction

Machine Learning is often challenged by insufficient labeled data. Previous methods employing implicit commonsense knowledge of pre-trained language models (PLMs) or pattern-based symbolic knowledge have achieved great success in mitigating manual annotation efforts. In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. Specifically, starting with limited labeled data as seeds, KICE first builds a Rule Generator by prompt-tuning to stimulate the rich knowledge distributed in PLMs, generate seed rules, and initialize the rules set. Afterwards, based on the rule-labeled data, the task model is trained in a self-training pipeline where the knowledge in rules set is consolidated with self-learned high-confidence rules. Finally, for the low-confidence rules, KICE solicits human-enlightened understanding and expands the knowledge coverage for better task model training. Our framework is verified on relation extraction (RE) task, and the experiments on TACRED show that the model performance (F1) grows from 33.24% to 79.84% with the enrichment of knowledge, outperforming all the baselines including other knowledgeable methods.

Translanguaging in the academic writing process: Exploring Chinese bilingual postgraduate students’ practices at a British university

Written translanguaging within educational settings has drawn increasing attention in recent years, but research which explores translanguaging practice in detail in the postgraduate dissertation writing process is still limited. This study focuses on five Chinese postgraduate students who studied at a British university. It investigates their translanguaging practice in the outlining, note-taking and drafting phases of their master's dissertation writing process with reference to Plakans' reading-to-write test task model. We collected and analysed the texts from our participants and conducted interviews to introduce emic perspectives to our analysis. The findings reveal six translanguaging practices in the academic dissertation writing process, showing that translanguaging supports students' development of self-regulation and serves as an efficient self-regulation tool for them to control the recursive and extensive dissertation writing process, to reach their immediate and global writing goals. The paper problematizes and challenges the orthodox academic discourse and practice and highlights the need for EAP tutors and dissertation supervisors to encourage translanguaging in students' writing practice in response to the potential issues raised by the monolingual norms in the academic communities.

Task scheduling in real-time industrial scenarios

Task scheduling for microservice-oriented industrial software is a complex process. It is a real-time process where multiple task attributes should be considered and different tasks should be processed in parallel. To address this problem, a scheduling model including a task model and resource model applicable to industrial scenarios is established. Then a task importance function for evaluating the importance of tasks is designed. After that, a Dynamic Importance-aware Online Scheduling Algorithm (DIOS) is proposed, which ranks and schedules all the tasks in decreasing order of importance. To further improve the efficiency and adaptability of the DIOS algorithm, resource reservation, preemptive scheduling and online adaptive tuning methods are established. Finally, simulation results in two typical industrial software scenarios show that the proposed algorithm performs significantly better than the widely used algorithms such as the First Come First Serve Algorithm (FCFS), the Earliest Deadline First Algorithm (EDF), the Least Laxity First Algorithm (LLF) and the Fixed Priority Scheduling Algorithm (FPS).

ФИНАНСОВЫЙ МЕНЕДЖМЕНТ: ПЛАНИРОВАНИЕ, ПРОЕКТИРОВАНИЕ И УПРАВЛЕНИЕ

Planning and project management is one of the main tools of financial management and budgeting. Organizational actions and practical steps to analyze financing models in the financial management system are strategic tasks for the development of the project and, in general, the business of an economic entity. Effective forecasting and management of investments and cash flows in the financial management system is the key to successful project co-financing. One of the main areas of planning in the financial management system is financial planning and modeling. Project management at the planning stage in the financial management system is time-consuming and significant. Methods of financial management in project management are aimed at studying the development of the project using its own funds and through co-financing (attracted investments). Financial management at the stage of project planning and development requires financial modeling of current tasks. Budgeting in the financial management system carries the tasks of budget management. Budgetary management is a detailed study of costs and investments, managing them, using a set of traditional methods and management procedures. It is concluded that in the system of financial management - planning, design and business management are vital for the implementation of both current activities and the development of new projects.

A High-Efficient Reinforcement Learning Approach for Dexterous Manipulation.

Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and relatively clumsy motions of current robotic end effectors. This paper proposed a dynamic model based on generative adversarial architecture to learn the state mode of the dexterous hand, reducing the model's prediction error in long spans. An adaptive trajectory planning kernel was also developed to generate High-Value Area Trajectory (HVAT) data according to the control task and dynamic model, with adaptive trajectory adjustment achieved by changing the Levenberg-Marquardt (LM) coefficient and the linear searching coefficient. Furthermore, an improved Soft Actor-Critic (SAC) algorithm is designed by combining maximum entropy value iteration and HVAT value iteration. An experimental platform and simulation program were built to verify the proposed method with two manipulating tasks. The experimental results indicate that the proposed dexterous hand reinforcement learning algorithm has better training efficiency and requires fewer training samples to achieve quite satisfactory learning and control performance.

Engineering Rehabilitation: Blending Two Tool-supported Approaches to Close the Loop from Tasks-based Rehabilitation to Exercises and Back Again

Post-stroke or post-brain injuries rehabilitation is a long-term process defined by therapists and tuned to the specific damages and their consequences for each patient. This process requires the definition of well-defined multiple exercises to be performed multiple times by each patient over a long period of time. One of the key findings in the rehabilitation domain is that exercises should be tasks and goals oriented to increase functional independence, enhance development and prevent disability [24]. Patients performing the exercises must be carefully monitored to identify progress, possible regression and prevent damages due to the inadequate performance of the exercises. The SIVIRE tool [20] provides interactive graphical feedback to patients about the performance of their exercises and deviations from nominal, planed practice but fails at providing high-level descriptions of exercises and at connecting them to high-level rehabilitation goals (e.g. gain autonomy in cleaning a room). The HAMSTERS-XLE tool [16] provides a tuneable editor and a simulator to describe goals and task models including low-level tasks covering perception of the various senses, cognition and motoric actions. This paper presents the integration of these two tools and the benefits it brings both to the patients and to the therapists. The resulting tool and its engineering are presented in detail highlighting some generic integration mechanisms. On a case study, we show how exercises can be defined in HAMSTERS-XLE, transferred to SIVRE for fine tuning, monitored by SIVRE during the multiple practices of the patient and transferred back again to HAMSTERS-XLE to monitor progress, identify issues with the exercises and provide therapists with goal and task-based data about patients' evolutions

Task-oriented Energy Scheduling in Wireless Rechargeable Sensor Networks

In recent years, the flourishing of Wireless Power Transfer (WPT) technology brings Wireless Sensor Networks (WSNs) a renewable, reliable, and controllable energy source. To achieve high efficiency, WPT and sensors both adopt directional antennas. Most previous works about directional charging optimize overall charging efficiency, but neglect energy scheduling on nodes for optimizing tasks. This disadvantage may cause a serious imbalance between the energy supply and task loads. This article takes both aspects into consideration. To achieve an overall performance improvement, we jointly schedule rotatable chargers and allocate tasks to nodes in a WSN powered by directional chargers. As far as we know, this is the first work to propose the Task-oriented Energy Scheduling (TOES) problem, i.e., given a set of rotatable directional chargers and some wireless rechargeable sensor nodes, scheduling chargers and nodes to maximize the total utility gained from a set of tasks. We prove the NP-Hardness of TOES. Then we design a 4-approximation algorithm for TOES by jointly solving two subproblems of the original one. We further extend this problem to the windowed task model and propose a \(\frac{4}{1-\gamma }\) approximation algorithm for it, where γ is the proportion of the rotating period. Finally, extensive simulation results show that our algorithms outperform baselines by at least 40.3%.

Exploration of Identification and Prognostic Analysis of a Novel Immune-Related lncRNA Pair Signature and Immune Landscape in Esophageal Adenocarcinoma: A New Method Based on “Continuous Learning” Model

With the rapid development of information technology, many medical systems have emerged one after another with the support of continuous learning. A method of medical data privacy protection and resource utilization based on continuous learning is proposed to initialize the depth model of specific medical tasks. The depth model includes feature sampling model, data review model and task expression model, Finally, the depth model is trained according to the data from n institutions in turn. This method can overcome the obstacles of data sharing. The intelligent medical system of medical knowledge sharing will greatly improve the level of existing medical technology. An increasing body of evidence suggests that long non-coding RNAs (lncRNAs) participate in various physiological processes and pathological diseases. Esophageal adenocarcinoma develops rapidly with poor prognosis and high mortality in the near and long term. Immunotargeted therapy is a research hotspot. However, it is necessary to explore the immunomodulatory molecules of esophageal adenocarcinoma and analyze their relationship with clinicopathological characteristics and prognosis. We aimed to construct a robust immune-related lncRNA signature associated with survival outcomes in esophageal adenocarcinoma. We identified an immune-related lncRNA pairs signature with prognostic value from The Cancer Genome Atlas. Differentially expressed immune-related lncRNAs (DEirlncRNAs) were identified and paired, followed by prognostic assessment using univariate Cox regression analysis. We used least absolute shrinkage and selection operator penalized Cox analysis for constructing a risk score prognostic model and drew receiver operating characteristic (ROC) curves to predict overall survival. Then, we evaluated our signature in several settings: chemotherapy, tumor-infiltrating immune cells, and immune-mediated gene expression. In total, 339 DEirlncRNA pairs were identified, 11 of which were involved in the risk score prognostic signature. The area under ROC curves representing the predictive effect for 1-, 2-, and 3-year survival rates were 0.942, 0.987, and 0.977, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to clinicopathological characteristics. Correlation analyses showed disparities in drug sensitivity, tumor-infiltrating immune cells, and immune-related gene expression. We identified a novel prognostic immune-related lncRNA pair signature for esophageal adenocarcinoma. The risk score-based groups displayed different immune statuses, drug sensitivity, and immune-mediated gene expression. These findings may offer insights into the prognostic evaluation of esophageal adenocarcinoma and may provide a basis for creating personalized treatment plans.

A Meta-Learning Based Framework for Cell-Level Mobile Network Traffic Prediction

In this paper, we propose a meta-learning based cell-level network traffic prediction framework (ML-TP), which can provide the proper initial weight vector for the learning model of a new prediction task based on the prediction task’s meta-features. In the ML-TP, each prediction task forms a base-task, and a multi-layer long short-term memory (LSTM) network is constructed as its base-learner. Through fast Fourier transform (FFT) analyses of real-world network traffic data, we find that the five most dominating frequency components can capture the cell-level traffic variations, and hence can be used as a base-task’s meta-features. We prove that the well-trained weight vector of a previous base-task’s base-learner is likely to be a proper initial weight vector of a new base-task’s base-learner if the meta-features of the two base-tasks are close to each other in the Euclidean space. Accordingly, we propose a K-nearest neighbours (KNN) algorithm based meta-learner to deal with the meta-task in the ML-TP. Numerical tests show that the ML-TP can significantly increase the base-learners’ after-training prediction accuracy and learning efficiency in terms of the number of base-samples and the number of epochs needed in each base-learner’s fine-tuning progress.

Programming heterogeneous architectures using hierarchical tasks

SummaryTask‐based systems have become popular due to their ability to utilize the computational power of complex heterogeneous systems. A typical programming model used is the Sequential Task Flow (STF) model, which unfortunately only supports static task graphs. This can result in submission overhead and a static task graph that is not well‐suited for execution on heterogeneous systems. A common approach is to find a balance between the granularity needed for accelerator devices and the granularity required by CPU cores to achieve optimal performance. To address these issues, we have extended the STF model in the StarPU runtime system by introducing the concept of hierarchical tasks. This allows for a more dynamic task graph and, when combined with an automatic data manager, it is possible to adjust granularity at runtime to best match the targeted computing resource. That data manager makes it possible to switch between various data layout without programmer input and allows us to enforce the correctness of the DAG as hierarchical tasks alter it during runtime. Additionally, submission overhead is reduced by using large‐grain hierarchical tasks, as the submission process can now be done in parallel. We have shown that the hierarchical task model is correct and have conducted an early evaluation on shared memory heterogeneous systems using the Chameleon dense linear algebra library.

Optical character recognition quality affects subjective user perception of historical newspaper clippings

PurposeThis study aims to identify user perception of different qualities of optical character recognition (OCR) in texts. The purpose of this paper is to study the effect of different quality OCR on users' subjective perception through an interactive information retrieval task with a collection of one digitized historical Finnish newspaper.Design/methodology/approachThis study is based on the simulated work task model used in interactive information retrieval. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869–1918 which consists of ca. 1.45 million autosegmented articles. The article search database had two versions of each article with different quality OCR. Each user performed six pre-formulated and six self-formulated short queries and evaluated subjectively the top 10 results using a graded relevance scale of 0–3. Users were not informed about the OCR quality differences of the otherwise identical articles.FindingsThe main result of the study is that improved OCR quality affects subjective user perception of historical newspaper articles positively: higher relevance scores are given to better-quality texts.Originality/valueTo the best of the authors’ knowledge, this simulated interactive work task experiment is the first one showing empirically that users' subjective relevance assessments are affected by a change in the quality of an optically read text.

Connectome-based prediction of craving in gambling disorder and cocaine use disorder.

Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.

A network-level test of the role of the co-activated default mode network in episodic recall and social cognition

Resting-state network research is extremely influential, yet the functions of many networks remain unknown. In part, this is due to typical (e.g., univariate) analyses independently testing the function of individual regions and not examining the full set of regions that form a network whilst co-activated. Connectivity is dynamic and the function of a region may change based on its current connections. Therefore, determining the function of a network requires assessment at this network-level. Yet popular theories implicating the default mode network (DMN) in episodic memory and social cognition, rest principally upon analyses performed at the level of individual brain regions. Here we use independent component analysis to formally test the role of the DMN in episodic and social processing at the network level. As well as an episodic retrieval task, two independent datasets were employed to assess DMN function across the breadth of social cognition; a person knowledge judgement and a theory of mind task. Each task dataset was separated into networks of co-activated regions. In each, the co-activated DMN, was identified through comparison to an a priori template and its relation to the task model assessed. This co-activated DMN did not show greater activity in episodic or social tasks than high-level baseline conditions. Thus, no evidence was found to support hypotheses that the co-activated DMN is involved in explicit episodic or social tasks at a network-level. The networks associated with these processes are described. Implications for prior univariate findings and the functional significance of the co-activated DMN are considered.