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Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, image completion has aimed to create plausible appearance for the invisible regions, but requires a manual mask as input. In this work, we propose a higher-level scene understanding system to tackle both visible and invisible parts of objects and backgrounds in a given scene. Particularly, we built a system to decompose a scene into individual objects, infer their underlying occlusion relationships, and even automatically learn which parts of the objects are occluded that need to be completed. In order to disentangle the occluded relationships of all objects in a complex scene, we use the fact that the front object without being occluded is easy to be identified, detected, and segmented. Our system interleaves the two tasks of instance segmentation and scene completion through multiple iterations, solving for objects layer-by-layer. We first provide a thorough experiment using a new realistically rendered dataset with ground-truths for all invisible regions. To bridge the domain gap to real imagery where ground-truths are unavailable, we then train another model with the pseudo-ground-truths generated from our trained synthesis model. We demonstrate results on a wide variety of datasets and show significant improvement over the state-of-the-art. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-stage R-CNN network called ParallelNet is proposed for thigh fracture detection task. In the proposed method, multiple parallel backbone networks and a feature fusion connection structure are designed, which can extract features with different reception fields. Specifically, the first backbone network is denoted as main network, which adopted normal convolution to detect small fractures, the rest backbone networks are denoted as sub-networks which adopted dilated convolution to detect large fractures. We evaluated the proposed method on a thigh fracture dataset containing 3842 X-ray radiographs, 3484 of which is assigned as a training dataset and 358 as a testing dataset. The experiments compare the proposed method with other state-of-the-art deep learning frameworks, including Faster R-CNN, FPN, Cascade R-CNN and RetinaNet, especially DCFPN which focus on thighbone fracture detection task. Our framework achieved 87.8% AP50 and 49.3% AP75 which outperformed other state-of-the-art frameworks. Moreover, ablation experiments on the backbone numbers, connection styles, different dilation rates and the position of dilated convolution have been attempted, and the function of each hyperparameter is analyzed. [ABSTRACT FROM AUTHOR]

This paper analyzes within-session test/retest data from four different tasks used to elicit risk attitudes. Maximum-likelihood and non-parametric estimations on 16 datasets reveal that, irrespective of the task, measurement error accounts for approximately 50% of the variance of the observed variable capturing risk attitudes. The consequences of this large noise element are evaluated by means of simulations. First, as predicted by theory, the coefficient on the risk measure in univariate OLS regressions is attenuated to approximately half of its true value, irrespective of the sample size. Second, the risk-attitude measure may spuriously appear to be insignificant, especially in small samples. Unlike the measurement error arising from within-individual variability, rounding has little influence on significance and biases. In the last part, we show that instrumental-variable estimation and the ORIV method, developed by Gillen et al. (2019), both of which require test/retest data, can eliminate the attenuation bias, but do not fully solve the insignificance problem in small samples. Increasing the number of observations to N=500 removes most of the insignificance issues. [ABSTRACT FROM AUTHOR]

Despite its pervasiveness, the concept of 'levels of organization' has received relatively little attention in its own right. I propose here an emerging approach that posits 'levels' as a fragmentary concept situated within an interest-relative matrix of operational usage within scientific practice. To this end I propose one important component of meaning, namely the epistemic goal (sensu Ingo Brigandt) motivating the term's usage, which recovers a remarkably conserved and sufficiently unifying significance attributable to 'levels' across different instances of usage. This epistemic goal, to provide structure to scientific problems, delegates tasks whose execution generates the term's expressed content in a given instance. This treatment of levels does not diminish the concept's general importance to science, but rather allows for its use in, and usefulness for, scientific practice to be better contextualized to particular tasks encompassing varying breadths of activity. [ABSTRACT FROM AUTHOR]

Understanding the way learners engage with learning technologies, and its relation with their learning, is crucial for motivating design of effective learning interventions. Assessing the learners' state of engagement, however, is non-trivial. Research suggests that performance is not always a good indicator of learning, especially with open-ended constructivist activities. In this paper, we describe a combined multi-modal learning analytics and interaction analysis method that uses video, audio and log data to identify multi-modal collaborative learning behavioral profiles of 32 dyads as they work on an open-ended task around interactive tabletops with a robot mediator. These profiles, which we name Expressive Explorers, Calm Tinkerers, and Silent Wanderers, confirm previous collaborative learning findings. In particular, the amount of speech interaction and the overlap of speech between a pair of learners are behavior patterns that strongly distinguish between learning and non-learning pairs. Delving deeper, findings suggest that overlapping speech between learners can indicate engagement that is conducive to learning. When we more broadly consider learner affect and actions during the task, we are better able to characterize the range of behavioral profiles exhibited among those who learn. Specifically, we discover two behavioral dimensions along which those who learn vary, namely, problem solving strategy (actions) and emotional expressivity (affect). This finding suggests a relation between problem solving strategy and emotional behavior; one strategy leads to more frustration compared to another. These findings have implications for the design of real-time learning interventions that support productive collaborative learning in open-ended tasks. [ABSTRACT FROM AUTHOR]

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In experiments investigating the processing of true and false negative sentences, it is often reported that polarity interacts with truth-value, in the sense that true sentences lead to faster reaction times than false sentences in affirmative conditions whereas the same does not hold for negative sentences. Various reasons for this difference between affirmative and negative sentences have been discussed in the literature (e.g., lexical associations, predictability, ease of comparing sentence and world). In the present study, we excluded lexical associations as a potential influencing factor. Participants saw artificial visual worlds (e.g., a white square and a black circle) and corresponding sentences (i.e., "The square/circle is (not) white"). The results showed a clear effect of truth-value for affirmative sentences (true faster than false) but not for negative sentences. This result implies that the well-known truth-value-by-polarity interaction cannot solely be due to long-term lexical associations. Additional predictability manipulations allowed us to also rule out an explanatory account that attributes the missing truth-value effect for negative sentences to low predictability. We also discuss the viability of an informativeness account. [ABSTRACT FROM AUTHOR]

Comparative studies on paper and pencil– and computer-based tests principally focus on statistical analysis of students' performances. In educational assessment, comparing students' performance (in terms of right or wrong results) does not imply a comparison of problem-solving processes followed by students. In this paper, we present a theoretical tool for task analysis that allows us to highlight how students' problem-solving processes could change in switching from paper to computer format and how these changes could be affected by the use of one environment rather than another. In particular, the aim of our study lies in identifying a set of indexes to highlight possible consequences that specific changes in task formulation have, in terms of task comparability. Therefore, we propose an example of the use of the tool for comparing paper-based and computer-based tasks. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is more and more used in the industrial context for maintenance, assembly operation. However, owing to the evolution and maturity of the technology, it is still necessary to evaluate the usage and benefits of AR in an industrial context to go further than proof of concept. As such, this paper proposes an analysis of AR case study in the literature. Then, this paper presents a methodology for comparing AR and electronic document-based complex maintenance instructions considering tasks complexity and operator competency level. The main results show that the consultation duration of the AR tablet is 34% statistically faster than the PDF tablet. It also shows that error concerning similar objects is reduced thanks to AR. Moreover, the study specifically focuses on types of task that worth using AR. However, the study shows that usability of the AR device is less well rated than the PDF for "beginner–intermediate" operators. Finally, an in-depth analysis permits extracting recommendations of the different factors to take into account with industrial AR applications. [ABSTRACT FROM AUTHOR]

Evaluation of interactive search systems and study of users' struggling search behaviors require a significant number of search tasks. However, generation of such tasks is inherently difficult, as each task is supposed to trigger struggling search behavior rather than simple search behavior. To the best of our knowledge, there has not been a commonly used task set for research in struggling search. Moreover, the everchanging landscape of information needs would render old task sets less ideal if not unusable for evaluation. To deal with this problem, we propose a crowd-powered task generation method and develop a platform to efficiently generate struggling search tasks on basis of online wikis such as Wikipedia. Our experiments and analysis show that the generated tasks are qualified to emulate struggling search behaviors consisting of "repeated similar queries" and "quick-back clicks"; tasks of diverse topics, high quality and difficulty can be created using this method. For benefit of the community, we publicly released a task generation platform TaskGenie, a task set of 80 topically diverse struggling search tasks with "baselines," and the corresponding anonymized user behavior logs. [ABSTRACT FROM AUTHOR]

Many areas of psychology assume that performance on tasks of interest is stable through time. Here, using time-sensitive modeling of working memory task performance, we show not only was this assumption incorrect, but that certain components of the performance trajectory (e.g., final task performance; rate of change) were independently predictive of fluid intelligence. This fact has clear implications for theoretical frameworks linking working memory and fluid intelligence, and beyond. [ABSTRACT FROM AUTHOR]

This study deals with the task assignment problem in a heterogeneous unmanned aerial vehicle system under the multiple constraints of limited resources, effective task execution scope, obstacle avoidance and task priority. Considering the nonlinearity, multimoding and high complexity of the task assignment problem, many existing optimization methods may fall into local optimum and even fail to provide satisfactory solutions. To address the issues, the discrete adaptive search whale optimization algorithm is proposed. First, we present the obstacle-avoiding distance estimation method to provide precise task scenario information and reduce the computational complexity. Then, the crossover-based updating method is presented to enable the algorithm to solve discrete problems such as task assignment. In addition, the search intensity adaptive mechanism is presented to optimize and balance the exploration and exploitation intensities in the solution space reasonably, thereby achieving high-quality task assignment results quickly. Further, joint leadership mechanism and local search mechanism are proposed to enhance the exploration ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Sexual conflict arises when two individuals invest in their common offspring because both individuals benefit when their partner invests more. Conditional cooperation is a theoretical concept that could resolve this conflict. Here, parents are thought to motivate each other to contribute to provisioning visits by following the rules of turn taking, which results in equal and efficient investment. However, parents have other tasks besides provisioning, which might hinder taking turns. To investigate restrictions by other care tasks and whether turn taking can be used to match investment, we manipulated brooding duration in female blue tits (Cyanistes caeruleus) during the early nestling phase by changing nest box temperature. As expected, females subjected to cold conditions brooded longer than females under warm conditions. Yet, contrary to our prediction, females had similar visit rates in both treatments, which suggests that females in the cold treatment invested more overall. In addition, the females' turn taking level was higher in the more demanding cold condition (and the calculated randomised turn taking levels of females did not differ), hence females don't seem to be restricted in their turn taking strategy by other care tasks. However, males did not seem to match the females' turn taking levels because they did not adjust their visit rates. Thus, level of turn taking was not restricted by an other sex-specific task in females and did not facilitate a greater investment by their male partners. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance and encourages feature reuse. We evaluate our proposed architecture on three independent tasks: classification, segmentation and facial landmark localisation. For this, we use benchmark datasets such as ImageNet, CIFAR-10, CIFAR-100, SVHN CamVid and 300W. [ABSTRACT FROM AUTHOR]

The present study aimed to separate the neural activities between response inhibition and memory updating processes in the Count/Nocount task. Memory load was manipulated to investigate the memory updating process. Within each trial, participants were asked to count/withhold counting the number of O/X letters in the Count/Nocount task. The participants were asked to silently add 1 if a Count letter was presented in the low load condition, and add 2 in the high load condition. Data from 28 healthy participants showed that: (1) in both high load and low load conditions, the latencies of P2 and N2 components were shorter for the Nocount than Count trials, indicating faster attentional orienting and conflict monitoring processes for the Nocount stimuli (i.e., inhibition processes triggered by the Nocount stimuli against those response execution processes triggered by Count stimuli); (2) more positive frontal P3 amplitudes were evoked for the Nocount relative to the Count stimuli, indicating a more intensive response inhibition process for the Nocount trials; (3) a more positive parietal P3 component was evoked for the low load relative to high load condition, indicating a more intensive working memory updating process for the high load condition. This load effect was absent for the frontal P3 component, suggesting that the frontal P3 might not be associated with the memory updating process. In sum, both the cognitive inhibition process (reflected by the frontal P3 component) and working memory updating process (reflected by the parietal P3 component) appear to be involved in the Count/Nocount task. [ABSTRACT FROM AUTHOR]

This study investigated how skill level and task complexity influence the calibration of perception–action and particularly how close an individual acts relative to his or her maximal action capabilities. Complexity was manipulated between two (Touch, Grasp) and more than two (Removing, Moving Up) nested affordance conditions. For all conditions, we examined whether advanced climbers had greater maximal action capabilities than intermediate climbers or whether they better scaled their action (i.e., acted nearer to their maximal action capabilities) or both. Eleven intermediate and 11 advanced male climbers were first asked to estimate the maximum distance that they could reach a climbing hold. The hold was moved along a slide and fixed once requested by the participant; subsequently, the distance to the starting hold was measured. After each estimation, the participant was required to execute the climbing action. After four estimation-action trials in each of the four conditions, the maximal action capability (i.e., actual maximal reaching distance) was determined. Advanced climbers demonstrated greater actual maximal reaching distances than intermediate climbers for all conditions, but they only estimated greater maximal reaching distances for the more complex conditions, which featured more than two nested affordances. When estimated maximal reaching distances were scaled to actual maximal reaching distances, advanced climbers did not differ from intermediate climbers for any condition, and there were no differences between conditions. Our findings indicate that expertise was a function of greater action capabilities, but not due to the accuracy of calibration. [ABSTRACT FROM AUTHOR]

How do we compare the difficulty of different kinds of tasks, and how we do sequence tasks of different kinds when the basis for the ordering is the tasks' difficulty levels? The ability to do these things requires a common currency, but the identity of that currency, if it exists, is unknown. We hypothesized that people may believe that the time that attention is paid to tasks enables people to compare and sequence tasks of different kinds. To evaluate this hypothesis, we tested three groups of participants. One group estimated the proportion of time that performance of a task requires attention—what we called attention time proportions or ATPs. We obtained ATPs for tasks that were "more intellectual" (counting) and others that were "more physical" (locomotion). Two additional groups made 2-alternative-forced-choice decisions about the relative ease and preferred sequencing of all possible pairs of tasks for which ATPs were independently obtained. We found that ATPs predicted judgments of task difficulty and preferred task order. [ABSTRACT FROM AUTHOR]

The midsession reversal paradigm confronts an animal with a two-choice discrimination task where the reward contingencies are reversed at the midpoint of the session. Species react to the reversal with either win-stay/lose-shift, using local information of reinforcement, or reversal estimation, using global information, e.g. time, to estimate the point of reversal. Besides pigeons, only mammalian species were tested in this paradigm so far and analyses were conducted on pooled data, not considering possible individually different responses. We tested twelve kea parrots with a 40-trial midsession reversal test and additional shifted reversal tests with a variable point of reversal. Birds were tested in two groups on a touchscreen, with the discrimination task having either only visual or additional spatial information. We used Generalized Linear Mixed Models to control for individual differences when analysing the data. Our results demonstrate that kea can use win-stay/lose-shift independently of local information. The predictors group, session, and trial number as well as their interactions had a significant influence on the response. Furthermore, we discovered notable individual differences not only between birds but also between sessions of individual birds, including the ability to quite accurately estimate the reversal position in alternation to win-stay/lose-shift. Our findings of the kea's quick and flexible responses contribute to the knowledge of diversity in avian cognitive abilities and emphasize the need to consider individuality as well as the limitation of pooling the data when analysing midsession reversal data. [ABSTRACT FROM AUTHOR]

Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence's emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets. [ABSTRACT FROM AUTHOR]

Person detection and Re-identification are two well-defined support tasks for practically relevant tasks such as Person Search and Multiple Person Tracking. Person Search aims to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. Similarly, Multiple Person Tracking, especially when using the tracking-by-detection pipeline, requires to detect and associate all appeared persons in consecutive video frames. One major challenge shared by the two tasks comes from the contradictory goals of detection and re-identification, i.e, person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two support tasks in a joint model. To this end, we present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by misalignment. Our Norm-Aware Embedding achieves remarkable performance on both person search and multiple person tracking benchmarks, with the merit of being easy to train and resource-friendly. [ABSTRACT FROM AUTHOR]

Understanding scene image includes detecting and recognizing objects, estimating the interaction relationships of the detected objects, and describing image regions with sentences. However, since the complexity and variety of scene image, existing methods take object detection or vision relationship estimate as the research targets in scene understanding, and the obtained results are not satisfactory. In this work, we propose a Multi-level Semantic Tasks Generation Network (MSTG) to leverage mutual connections across object detection, visual relationship detection and image captioning, to solve jointly and improve the accuracy of the three vision tasks and achieve the more comprehensive and accurate understanding of scene image. The model uses a message pass graph to mutual connections and iterative updates across the different semantic features to improve the accuracy of scene graph generation, and introduces a fused attention mechanism to improve the accuracy of image captioning while using the mutual connections and refines of different semantic features to improve the accuracy of object detection and scene graph generation. Experiments on Visual Genome and COCO datasets indicate that the proposed method can jointly learn the three vision tasks to improve the accuracy of those visual tasks generation. [ABSTRACT FROM AUTHOR]

The Cloud-IoT framework offers on-demand service for numerous applications with the aid of data gathered by IoT and the computing resources of cloud computing. The quality of service (QoS) degrades due to task-VM mismatch due to the heterogeneous service request from IoT devices. The tasks processed by an inappropriate VM may cause delay and affect the Quality of Service (QoS). The proposed task allocation and scheduling algorithm aim is to improve the QoS of education service offered by Cloud-IoT in an educational organisation. In the task allocation stage, task VM pairs are prioritized initially and task-VM pairs are selected based on the minimum of the expected completion time (ECT) with the approach named Priority Based Task Allocation and Buffering (PBTAB) Algorithm. In this stage, at each of the clouds, the selected task-VM pairs are placed on queues based on the proximal value of the MCT. In the scheduling stage, task-VM pair matching (T-VMBS) Algorithm schedules the task with the selection of the best of the VM from the total clouds to speed up the task execution. The PBTAB and T-VMBS algorithm achieved throughput performance of more than 90% with larger dataset and huge number of VM. The proposed approach achieved a decreased makespan of less than 50%. Similarly deadline violation rate and average reliability exhibited a better performance. [ABSTRACT FROM AUTHOR]

Flexible use of reactive and proactive control according to environmental demands is the key to adaptive behavior. In this study, forty-eight adults performed ten blocks of an AX-CPT task to reveal the strength of proactive control by the calculation of the proactive behavioral index (PBI). They also filled out the UPPS questionnaire to assess their impulsiveness. The median-split method based on the global UPPS score distribution was used to categorize participants as having high (HI) or low (LI) impulsiveness traits. The analyses revealed that the PBI was negatively correlated with the UPPS scores, suggesting that the higher is the impulsiveness, the weaker the dominance of proactive control processes. We showed, at an individual level, that the PBI increased across blocks and suggested that this effect was due to a smaller decrease in reactive control processes. Notably, the PBI increase was slower in the HI group than in the LI group. Moreover, participants who did not adapt to task demands were all characterized as high impulsive. Overall, the current study demonstrates that (1) impulsiveness is associated with less dominant proactive control due to (2) slower adaptation to task demands (3) driven by a stronger reliance on reactive processes. These findings are discussed in regards to pathological populations. [ABSTRACT FROM AUTHOR]

Suppression-induced forgetting (SIF) refers to a memory impairment resulting from repeated attempts to stop the retrieval of unwanted memory associates. SIF has become established in the literature through a growing number of reports built upon the Think/No-Think (TNT) paradigm. Not all individuals and not all reported experiments yield reliable forgetting, however. Given the reliance on task instructions to motivate participants to suppress target memories, such inconsistencies in SIF may reasonably owe to differences in compliance or expectations as to whether they will again need to retrieve those items (on, say, a final test). We tested these possibilities on a large (N = 497) sample of TNT participants. In addition to successfully replicating SIF, we found that the magnitude of the effect was significantly and negatively correlated with participants' reported compliance during the No-Think trials. This pattern held true on both same- and independent-probe measures of forgetting, as well as when the analysis was conditionalized on initial learning. In contrast, test expectancy was not associated with SIF. Supporting previous intuition and more limited post-hoc examinations, this study provides robust evidence that a lack of compliance with No-Think instructions significantly compromises SIF. As such, it suggests that diminished effects in some studies may owe, at least in part, to non-compliance—a factor that should be carefully tracked and/or controlled. Motivated forgetting is possible, provided that one is sufficiently motivated and capable of following the task instructions. [ABSTRACT FROM AUTHOR]

The mood induction paradigm has been an important tool for investigating the effects of negative emotional states on working memory (WM) executive functions. Though some evidence showed that negative mood has a differential effect on verbal and visuospatial WM, other findings did not report a similar effect. To explore this issue, we examined the negative mood's impact on verbal and visuospatial WM executive tasks based on grammatical reasoning and visuospatial rotation. Participants with no anxiety or depression disorders performed the tasks before and after negative (n = 14) or neutral (n = 13) mood induction. Participants' mood at the beginning and the end of the session was assessed by the Present Mood States List (LEAP) and word valence rating. The analyses showed changes in the emotional state of the negative group (ps <.03) but not of the neutral group (ps >.83) in the LEAP instrument. No significant differences between groups were observed in the WM tasks (ps >.33). Performance in the visuospatial WM task improved after mood induction for both groups (p <.05), possibly due to a practice effect. In sum, our findings challenge the view that negative mood modulates WM executive functions; thus, they were discussed considering the similarities and differences between studies that found negative mood effects on WM and those that did not find. Different WM tasks tap distinct processes and components, which may underlie behavioral effects of negative mood on WM tasks. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved. [ABSTRACT FROM AUTHOR]

Distributed stream processing engines (DSPEs) provide stream partitioning methods for distributing messages to tasks deployed in the distributed environment for real-time stream processing. Among these methods, the original locality-aware stream partitioning (LSP) is a binary LSP that sends messages only to downstreams on the same node as upstreams. The binary LSP degrades performance at general configurations because it focuses only on task locality and does not consider downstream status like distributed batch processing engines. In this paper, we propose a Stochastic LSP (SLSP) method that considers not only task locality but also downstream status by computing stream partitioning probability based on the round-trip time to downstreams. We also present coarse-grained and fine-grained methods for probing downstreams at node-level and process-level, respectively. Then, we optimize our SLSP using a weighted closeness to prioritize the partitioning probabilities and a parallel thread model to process each stage of the SLSP in parallel. Finally, we implement the SLSP in Apache Storm, a representative DSPE, and empirically evaluate it with the binary LSP. Experimental results show that our SLSP greatly reduces latency by up to 208% while maintaining a similar throughput compared to the binary LSP at general configurations. These results indicate that our SLSP performs the optimized stream partitioning by reflecting downstream status as well as task locality. [ABSTRACT FROM AUTHOR]

Recent evidences suggest that binaural vestibular stimulation affects tactile temporal processing. However, it remains difficult to determine the physiological mechanisms supporting the vestibular–somatosensory interactions observed during a TOJ task. Controlling the activation of the right or left vestibular system separately could allow to better understand the physiological bases of these findings and reconcile previous studies. The objective of the present study was to examine tactile temporal processing using a temporal order judgment task following selective stimulation of the right and left vestibular system with bi-thermal caloric vestibular stimulation (CVS). A total of 24 right-handed participants received bi-thermal CVS either in the right ear (n = 12) or the left ear (n = 12). Participants held vibrators in both hands which delivered a signal temporally separated by a variable asynchrony. Participants had to report the hand where the vibration was perceived first. The task was performed in three different CVS conditions: (1) baseline, (2) warm CVS, and (3) cold CVS. Analysis of the logistics curve parameters—just noticeable difference (JND) and point of subjective simultaneity (PSS)—for each participant in each CVS conditions revealed an increase in JND greater following warm CVS. A significant increase in JND following warm CVS was measured bilaterally. However, cold CVS increased JND only when CVS was applied in the left ear, but not in the right ear. Finally, no influence of CVS on PSS was observed. [ABSTRACT FROM AUTHOR]

Several space-based and object-based attention studies suggest these selection mechanisms may be voluntarily deployed, depending on task parameters and the attentional scope of the observer. Here, we sought to elucidate factors related to involuntary deployment of object-mediated space-based attention through two experiments. Experiment 1 used a modified flanker task where a target and nearby distractor were presented within the same or different object frames, such that an object-based attentional spread should be detrimental to performance. Results showed the presence of a flanker effect with no significant difference in magnitude between grouping conditions, indicating participants may have uniformly used a diffused attentional spotlight regardless of object segmentation. In a second experiment, we manipulated the extent of the observer's sustained attentional scope via an inducer task to determine whether object-based selection depends on the initial spotlight size. The results revealed object-based effects solely when attention narrowly encompassed the target, but not when it was widened to include the distracting flanker. This suggests the deployment of object-based attention may occur when spatial attention is initially focused narrowly. Because selecting the whole object frame directly interfered with task goals, we conclude that object-based attention may not always fully conform to relevant task goals or operate in a goal-oriented manner. We discuss these results in the context of existing literature while proposing a reconciliation of previously inconsistent findings of object-based selection. [ABSTRACT FROM AUTHOR]

In most of our daily activities and in team sports, we interact with other individuals and do not act in isolation. Using a social variant of the standard two-choice Simon task, this study aims to test if competitive/cooperative processing modes (i.e., metacontrol states) change the degree of bodily self-other integration between two persons in joint action. In addition, and more exploratory the study tested if this effect depends on a shared group experience with the partner. Two participants shared a visual Simon task, so that each person basically performed complementary parts of the task, which transfers the paradigm into a go/no-go Simon task for each person. Before running this joint Simon task, we set both participants either in a competitive or a cooperative control state by means of a dyadic game, a manipulation aimed at testing possible goal transfer across tasks. We found significant joint Simon effects for participants who were in a competitive state and for participants who were in a cooperative state. The joint Simon effect for participants being in a competitive state was significantly smaller than for participants being in a cooperative state. When experiencing the goal induction together with the partner, the joint Simon effect was significantly decreased as when the induction was performed alone. Both effects (metacontrol state induction and shared experience) seem to be statistically independent of each other. In line with predictions of metacontrol state theory, our study indicated that abstract cognitive goal states can be transferred from one task to another task, able to affect the degree of bodily self-other integration across different task situations. [ABSTRACT FROM AUTHOR]

The ubiquitous availability of technological aids requires individuals to constantly decide between either externalizing cognitive processes into these aids (i.e. cognitive offloading) or relying on their own internal cognitive resources. With the present research, we investigated the influence of metacognitive beliefs on individuals' offloading behavior in an experimental setup (N = 159). We manipulated participants' metacognitive beliefs about their memory abilities by providing fake performance feedback: below-average feedback, above-average feedback, or no feedback (control-group). We then measured offloading behavior, using a pattern copying task in which participants copied a color pattern from a model window into a workspace window. While solving this task, participants could rely either more on an internal memory strategy or more on an offloading strategy. Fake performance feedback affected the participants' metacognitive evaluations about their memory abilities (below-group < control-group < above-group). Although fake performance feedback did not affect actual offloading behavior, the participants receiving below-average performance feedback reported that they had relied more on an offloading strategy than those participants receiving above-average performance feedback. Furthermore, the participants in the below-group reported lower general memory abilities than the other groups at the end of the experiment. We conclude that while fake performance feedback strongly influenced metacognitive beliefs, this did not transfer into a change of strategy selection, thus not influencing offloading behavior. We propose to consider not only metacognitive beliefs but also metacognitive experiences as potential determinants of cognitive offloading. [ABSTRACT FROM AUTHOR]

In a word valence judgment task, positive words (e.g., excellence) are judged faster when presented at the top (congruent position) than at the bottom of the screen (incongruent position), whereas the opposite pattern occurs for negative words (e.g., disaster). This spatial-valence metaphoric congruency effect reflects top-positive/bottom-negative metaphoric association and may be attributed to at least three possible mechanisms: spreading activation between spatial and valence concepts (activation account), epistemic function that a conceptual metaphor serves to reduce the uncertainty associated with valence concepts (epistemic account), and/or the extent to which spatial-valence metaphoric association is relevant to task demand (relevance account). In three experiments, we manipulated congruency proportion, target eccentricity, and valence strength in a word valence judgment task to test these three possible accounts. Results showed that the metaphoric congruency effect was larger when a high (vs. equal) proportion of targets appears in congruent, relative to incongruent, position, and for targets with strong (vs. weak) valence. However, the effect in reaction time measure was not modulated by whether the target appeared in the position being near vs. far away from the center of the screen. The overall findings are better accommodated by the relevance account. The implications of the current findings on other theoretical accounts, such as Conceptual Metaphor Theory and polarity correspondence account, are also discussed. [ABSTRACT FROM AUTHOR]

Recently, it was demonstrated that even basic numerical cognition such as the processing of number magnitude is under cognitive control. However, evidence so far primarily came from adaptation effects to stimulus characteristics (e.g., relative frequency of specific stimulus categories). Expanding this approach, we evaluated a possible influence of more active exertion of cognitive control on basic number processing in task switching. Participants had to perform a magnitude comparison task while we manipulated the order of compatible and incompatible input–output modalities (i.e., auditory/vocal input–visual/manual output vs. auditory/visual input–manual/vocal output, respectively) on the trial level, differentiating repeat vs. switch trials. Results indicated that the numerical distance effect but not the problem size effect was increased after a switch in input–output modality compatibility. In sum, these findings substantiate that basic number processing is under cognitive control by providing first evidence that it is influenced by the active exertion of cognitive control as required in task switching. [ABSTRACT FROM AUTHOR]

Image completion involves filling plausible contents to missing regions in images. Current image completion methods produce only one result for a given masked image, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion—the task of generating multiple and diverse plausible solutions for free-form image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label for this multi-output problem. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one ground truth to get prior distribution of missing patches and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by adversarial learning. We then introduce a new short+long term patch attention layer that exploits distant relations among decoder and encoder features, to improve appearance consistency between the original visible and the generated new regions. Experiments show that our method not only yields better results in various datasets than existing state-of-the-art methods, but also provides multiple and diverse outputs. [ABSTRACT FROM AUTHOR]

Studies on the effects of dual tasking in patients with chronic inflammatory rheumatic diseases are limited. The aim of this study was to assess dual tasking while walking in patients with axial spondyloarthritis (axSpA) in comparison to healthy controls. Thirty patients with axSpA and thirty healthy controls underwent a 10-m walk test at a self-selected comfortable walking speed in single- and dual-task conditions. Foot-worn inertial sensors were used to compute spatiotemporal gait parameters. Analysis of spatiotemporal gait parameters showed that the secondary manual task negatively affected walking performance in terms of significantly decreased mean speed (p < 0.001), stride length (p < 0.001) and swing time (p = 0.008) and increased double support (p = 0.002) and stance time (p = 0.008). No significant interaction of group and condition was observed. Both groups showed lower gait performance in dual task condition by reducing speed, swing time and stride length, and increasing double support and stance time. Patients with axSpA were not more affected by the dual task than matched healthy controls, suggesting that the secondary manual task did not require greater attention in patients with axSpA. Increasing the complexity of the walking and/or secondary task may increase the sensitivity of the dual-task design to axial spondyloarthritis. [ABSTRACT FROM AUTHOR]

To understand when, how, and why people cheat, the ability to detect cheating in a laboratory setting is crucial. However, commonly used paradigms are confronted with a conflict between allowing participants to believe they can cheat unnoticed and allowing experimenters to detect cheating. This project aimed to develop and establish a new nonverbal task to resolve this conflict. Study 1 and Study 2 developed a new unsolvable paradigm called the Difference Spotting Task. In Study 1, participants were incentivized to indicate whether they found any difference between a pair of pictures without being asked to point the difference(s) out, so they could overreport their performance to earn extra money. Unbeknownst to them, the pairs of pictures from half of the items were identical so that the task could not be solved without cheating. This paradigm allowed experimenters to detect cheating for each unsolvable item. Study 3 examined the validity of the Difference Spotting Task and demonstrated it as a valid tool to assess cheating. The Difference Spotting Task is nonverbal and thus applicable to populations across age, educational level, and culture. In this unsolvable task, participants feel safe in cheating, and experimenters can detect cheating at the item level. The task holds the potential to gain acceptance by many researchers and facilitate the investigation of the underlying processes of cheating behavior. [ABSTRACT FROM AUTHOR]

Critical attention to Wilfrid Sellars's "Philosophy and the Scientific Image of Man" (PSIM) has focused on the dubious Peircean optimism about scientific convergence that underwrites Sellars's talk of "the" scientific image. Sellars's ultimate Peircean ontology has led Willem deVries, for instance, to accuse him of being a naturalistic "monistic visionary." But this complaint of monism misplays the status of the ideal end of science in Sellars's thinking. I propose a novel reading of PSIM, foregrounding its opening methodological re ections. On this reading, the central point of the paper is to accuse figures like Wittgenstein and Strawson, whom I call "analytical quietists," of taking the unity of intellectual endeavor as somehow given. Such unity as is forthcoming is, Sellars tells us, a task. I conclude by noting that a structurally similar accusation of too easily presumed unity emerges at the end of the paper, against a familiar sort of anti-relativistic moral theorizing. Thus, Sellars's conception of the task of philosophy is, at least potentially, a point of surprising ethico-political significance as well. [ABSTRACT FROM AUTHOR]

Average precision (AP) and many other related evaluation indices have been employed ubiquitously in classification tasks for a long time. However, they have defects and can hardly provide both overall evaluations and individual evaluations. In practice, we have to strike a balance between whole and individual performances to satisfy diverse demands. To this end, we propose a new index for multiclass classification tasks, named R ′ , which is an unbiased estimator of AP. Specifically, we improve the R index by taking the numerical differences between the real labels and predicted labels of each class into consideration. We evaluate its effectiveness and robustness on the MNIST and CIFAR-10 datasets. Experimental results show that it is positively correlated with some related indices. More importantly, we can obtain both overall and individual evaluations, which can be beneficial for improving training processes and model selection. Furthermore, as an evaluation architecture, the index can be promoted to evaluate any classification task, thereby implying broad application prospects. [ABSTRACT FROM AUTHOR]

Emotional cause identification (ECI) is an important task for emotion analysis, aiming to identify the causes behind a certain emotion expressed in the text. Most of the previous studies are restricted to the clause-level binary classification, which have ignored an important fact that not all words in the clause are useful cause information for people to express emotions. In this work, we propose a new task: emotion-cause span extraction (ECSE), which is capable of obtaining more accurate and effective emotion causes. Inspired by recent advances in using joint learning approaches to ECI, we propose a novel joint learning framework for emotion-cause span extraction and span-based emotion classification so as to better address the ECSE task. Taken as the default backbone network, the Bidirectional Encoder Representations from Transformers (BERT) is used to encode multiple words and serve contextualized token representations. Furthermore, we also propose a multi-attention mechanism with emotional context awareness and a relative position learning mechanism on word-level, which is able to further capture the mutual interactions between the emotion clauses and candidate spans. According to the experimental results on a benchmark emotion cause corpus, it proves the reliability of the ECSE task and the effectiveness of our approach. In addition, through an in-depth analysis of traditional ECI task by converting ECSE into the clause-level binary classification task, we achieve the best performance among the systems in comparison, which further demonstrates the feasibility of the new ECSE task. [ABSTRACT FROM AUTHOR]

This study focuses on connections between linear functions and their graphs that were made by tertiary remedial algebra students. In particular, we describe students' work on a Task designed to examine the connection between points on a graph and the equation of a line. The data consist of 63 responses to a written questionnaire and individual interviews with three participants. The results indicate that visual approaches impede students' solutions and point to incomplete connections between algebraic and graphical representation. While algebraic approaches point to various connections used in approaching the Task, students' ability to work with algebraic representation did not necessarily result in capitalizing on these connections. Furthermore, interpretations of the graph based on visual inspection appeared most useful when used in support of the algebraic approach. [ABSTRACT FROM AUTHOR]

We investigated whether interindividual attentional vulnerability moderates performance on domain-specific cognitive tasks during sleep restriction (SR) and subsequent recovery sleep. Fifteen healthy men (M ± SD, 22.3 ± 2.8 years) were exposed to three nights of baseline, five nights of 5-h time in bed SR, and two nights of recovery sleep. Participants completed tasks assessing working memory, visuospatial processing, and processing speed approximately every two hours during wake. Analyses examined performance across SR and recovery (linear predictor day or quadratic predictor day2) moderated by attentional vulnerability per participant (difference between mean psychomotor vigilance task lapses after the fifth SR night versus the last baseline night). For significant interactions between day/day2 and vulnerability, we investigated the effect of day/day2 at 1 SD below (less vulnerable level) and above (more vulnerable level) the mean of attentional vulnerability (N = 15 in all analyses). Working memory accuracy and speed on the Fractal 2-Back and visuospatial processing speed and efficiency on the Line Orientation Task improved across the entire study at the less vulnerable level (mean − 1SD) but not the more vulnerable level (mean + 1SD). Therefore, vulnerability to attentional lapses after SR is a marker of susceptibility to working memory and visuospatial processing impairment during SR and subsequent recovery. [ABSTRACT FROM AUTHOR]

Alterations in frontal and parietal neural activations during working memory task performance have been suggested as a candidate endophenotype of obsessive-compulsive disorder (OCD) in studies involving first-degree relatives. However, the direct link between genetic risk for OCD and neuro-functional alterations during working memory performance has not been investigated to date. Thus, the aim of the current functional magnetic resonance imaging (fMRI) study was to test the direct association between polygenic risk for OCD and neural activity during the performance of a numeric n-back task with four working memory load conditions in 128 participants, including patients with OCD, unaffected first-degree relatives of OCD patients, and healthy controls. Behavioral results show a significant performance deficit at high working memory load in both patients with OCD and first-degree relatives (p < 0.05). A whole-brain analysis of the fMRI data indicated decreased neural activity in bilateral inferior parietal lobule and dorsolateral prefrontal cortex in both patients and relatives. Most importantly, OCD polygenic risk scores predicted neural activity in orbitofrontal cortex. Results indicate that genetic risk for OCD can partly explain alterations in brain response during working memory performance, supporting the notion of a neuro-functional endophenotype for OCD. [ABSTRACT FROM AUTHOR]

Extracting and analyzing relevant information from bio-signal recordings are complex tasks in which action potential detection and sorting processes take place, moreover if these are performed in real time. In this regard, the present paper introduces real-time FPGA-based architectures for detection and sorting of bio-signals, in particular macaque and human pancreatic signals. Action potential detection is performed by using an adaptive threshold. Also, during this process we have identified six different action potential shapes from the signals, which have been used to classify the action potentials. Our implementation runs at a frequency of 100 MHz with a low resource consumption for both architectures, and action potentials can be also observed in real time during a simulation in an OLED display. [ABSTRACT FROM AUTHOR]

We explored the organization of motor units recorded in the flexor digitorum superficialis into stable groups (MU-modes) and force-stabilizing synergies in spaces of MU-modes. Young, healthy participants performed one-finger and three-finger accurate cyclical force production tasks. Two wireless sensor arrays (Trigno Galileo, Delsys, Inc.) were placed over the proximal and distal portions of the muscle for surface recording and identification of motor unit action potentials. Principal component analysis with Varimax rotation and factor extraction was used to identify MU-modes. The framework of the uncontrolled manifold hypothesis was used to analyze inter-cycle variance in the space of MU-modes and compute the index of force-stabilizing synergy. Multiple linear regression between the first MU-mode in the three-finger task and the first MU-modes in the three single-finger tasks showed no differences between the data recorded by the two electrodes suggesting that MU-modes were unlikely to be synonymous with muscle compartments. Multi-MU-mode synergies stabilizing task force were documented across all tasks. In contrast, there were no force-stabilizing synergies in the three-finger task analyzed in the space of individual finger forces. Our results confirm the synergic organization of motor units in single-finger tasks and, for the first time, expand this result to multi-finger tasks. We offer an interpretation of the findings within the theoretical scheme of control with spatial referent coordinates expanded to the analysis of individual motor units. The results confirm trade-offs between synergies at different hierarchical levels and expand this notion to intra-muscle synergies. [ABSTRACT FROM AUTHOR]

Sensorimotor adaptation can involve multiple learning processes with different time courses, and these processes may have different patterns of transfer. In this study, we tested how slow learning and fast learning transfer across tasks, and the specificity of transfer. We tested two natural goal-directed tasks: pointing and walking toward a visible target. We also tested a novel "hand locomotion" task in which subjects used pointing movements to cause simulated self-motion in virtual reality. The hand locomotion task used the same physical movement as pointing, but performed the same function as stepping. During an experimental block, subjects performed alternating training trials with perturbed visual feedback and test trials with no feedback. The test trials were either the same task to measure adaptation, or a different task to measure transfer. Perturbations on adaptation trials varied over time as a sum of sinusoids with different frequencies. Fast learning would be expected to produce equal responses to fast and slow perturbations, while slower learning would dampen responses to higher frequency perturbations. Subjects were generally not aware of the smoothly varying perturbations, but showed detectable adaptation for all three tasks. Only pointing produced significantly different responses to high- and low-frequency perturbations, consistent with slow learning. Adaptation of pointing produced more transfer to the hand locomotion task, which shared the same effector and motor actions, than to the stepping task. The other tasks showed fast learning but little or no slow learning, and equal transfer to tasks with different effector or function. Our results suggest that the slower components of sensorimotor adaptation are more movement specific, while faster learning is more generalizable. [ABSTRACT FROM AUTHOR]

Modality compatibility denotes the match between sensory stimulus modality and the sensory modality of the anticipated response effect (for example, vocal responses usually lead to auditory effects, so that auditory–vocal stimulus–response mappings are modality-compatible, whereas visual–vocal mappings are modality incompatible). In task switching studies, it has been found that switching between two modality-incompatible mappings (auditory-manual and visual–vocal) resulted in higher switch costs than switching between two modality-compatible mappings (auditory–vocal and visual-manual). This finding suggests that with modality-incompatible mappings, the anticipation of the effect of each response primes the stimulus modality linked to the competing task, creating task confusion. In Experiment 1, we examined whether modality-compatibility effects in task switching are increased by strengthening the auditory–vocal coupling using spatial-verbal stimuli relative to spatial-location stimuli. In Experiment 2, we aimed at achieving the same goal by requiring temporal stimulus discrimination relative to spatial stimulus localisation. Results suggest that both spatial-verbal stimuli and temporal discrimination can increase modality-specific task interference through a variation of the strength of anticipation in the response-effect coupling. This provides further support for modality specificity of cognitive control processes in task switching. [ABSTRACT FROM AUTHOR]

Task conflict is a type of conflict that emerges when a stimulus triggers two or more competing tasks. In the Stroop task, task conflict occurs between the relevant color-naming task and irrelevant word reading task and can be observed mainly on congruent trials, which only involve task conflict and are devoid of additional conflict types. We hypothesized that task conflict also manifests in the affordances task between the relevant task (e.g., classifying manipulatable objects), and the automatic task afforded by the object (e.g., grasping the object), and is mostly evident on congruent trials. Using an individual differences design we assessed the relationship between control mechanisms operating on Stroop congruent and affordances congruent trials under conditions of high and low cognitive control requirements. We hypothesized that task control is employed in both tasks. One-hundred and twenty-three participants performed an affordances task and two blocks of a Stroop task, each requiring a different level of task control (high vs. low). In a hierarchical regression model, we found a significant and specific correlation between affordances congruent and Stroop congruent conditions only in the high-control block, designed to greatly engage participants' task control, thus linking the task control mechanism in both tasks. These results indicate that task control underlies diverse modalities of response (visuomotor and linguistic), independently of other conflict types. We suggest that the affordances task may serve as a supplementary tool for the assessment of task control in the lab. [ABSTRACT FROM AUTHOR]

People respond to small numbers faster with the left hand and respond to large numbers faster with the right hand, a phenomenon known as the Spatial-Numerical Association of Response Codes (SNARC) effect. Whether the SNARC effect originates from culturally determined long-term experience or the task-set-influenced temporary associations among spaces, locations, and numerical magnitudes in working memory (WM) is still controversial. In the present study, we used a trisection paradigm in which numbers were divided into three categories (small: 1, 2; middle: 4, 5, 6; and large: 8, 9) to explore whether the central executive control can modulate the SNARC effect. Participants were serially presented with a cue and a target number. The cue denoted a task rule, which informed participants to compare the target number with either 3 or 7. The cue was either switched or repeated across trials. We found that the SNARC effects were observed in the cue-switching condition. In the cue-repeat condition, the SNARC effect disappeared. These findings suggest that the SNARC effect is modulated by set-shifting-related central executive control in WM, supporting the view that the SNARC effect is WM-dependent. [ABSTRACT FROM AUTHOR]