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In a large cloud data center HPC system, a critical problem is how to allocate the submitted tasks to heterogeneous servers that will achieve the goal of maximizing the system’s gain defined as the value of completed tasks minus system operation costs. We consider this problem in the online setting that tasks arrive in batches and propose a novel deep reinforcement learning (DRL) enhanced greedy optimization algorithm of two-stage scheduling interacting task sequencing and task allocation. For task sequencing, we deploy a DRL module to predict the best allocation sequence for each arriving batch of tasks based on the knowledge (allocation strategies) learnt from previous batches. For task allocation, we propose a greedy strategy that allocates tasks to servers one by one online following the allocation sequence to maximize the total gain increase. We show that our greedy strategy has a performance guarantee of competitive ratio $\frac{1}{1+\kappa }$ 1 1 + κ to the optimal offline solution, which improves the existing result for the same problem, where $\kappa$ κ is upper bounded by the maximum cost-to-gain ratio of each task. While our DRL module enhances the greedy algorithm by providing the likely-optimal allocation sequence for each batch of arriving tasks, our greedy strategy bounds DRL’s prediction error within a proven worst-case performance guarantee for any allocation sequence. It enables a better solution quality than that obtainable from both DRL and greedy optimization alone. Extensive experiment evaluation results in both simulation and real application environments demonstrate the effectiveness and efficiency of our proposed algorithm. Compared with the state-of-the-art baselines, our algorithm increases the system gain by about 10% to 30%. Our algorithm provides an interesting example of combining machine learning (ML) and greedy optimization techniques to improve ML-based solutions with a worst-case performance guarantee for solving hard optimization problems. [ABSTRACT FROM AUTHOR]

Domain adaptation techniques learn transferable knowledge from a source domain to a target domain and train models that generalize well in the target domain. Unfortunately, a majority of the existing techniques are only applicable to scenarios that the target-domain data in the task of interest is available for training, yet this is not often true in practice. In general, human beings are experts in generalization across domains. For example, a baby can easily identify the bear from a clipart image after learning this category of animal from the photo images. To reduce the gap between the generalization ability of human and that of machines, we propose a new solution to the challenging zero-shot domain adaptation (ZSDA) problem, where only a single source domain is available and the target domain for the task of interest is not accessible. Inspired by the observation that the knowledge about domain correlation can improve our generalization ability, we explore the correlation between source domain and target domain in an irrelevant knowledge task ($ \mathbb {K}$ K -task), where dual-domain samples are available. We denote the task of interest as the question task ($ \mathbb {Q}$ Q -task) and synthesize its non-accessible target-domain as such that these two tasks have the shared domain correlation. In order to realize our idea, we introduce a new network structure, i.e., conditional coupled generative adversarial networks (CoCoGAN), by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. With a pair of coupling GANs, our CoCoGAN is able to capture the joint distribution of data samples across two domains and two tasks. For CoCoGAN training in a ZSDA task, we introduce three supervisory signals, i.e., semantic relationship consistency across domains, global representation alignment across tasks, and alignment consistency across domains. Experimental results demonstrate that our method can learn a suitable model for the non-accessible target domain and outperforms the existing state of the arts in both image classification and semantic segmentation. [ABSTRACT FROM AUTHOR]

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments. [ABSTRACT FROM AUTHOR]

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM’s null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach’s feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

To achieve high efficiency with limitation of energy consumption is a key problem in the system of UAVs. In existing UAV task allocation methods, the relevance between tasks and UAV cruise directions is neglected, which may further influence the energy consumption and delay of task accomplishment. In view of this, this paper proposed an UAV task allocation method based on the relevance analysis between tasks and cruise directions. This method includes two phases: task screening phase and conflict resolution phase based on consensus. In the first phase, this method selects out tasks without turning back for an UAV according to angles between the cruise direction of the UAV and directions of these tasks, then designs an algorithm to further select candidate tasks before interaction from the tasks without turning back according to their energy consumption utility parameters and time urgency parameters. In the second phase, this method solves tasks conflicts between UAVs after exchanging of their candidate tasks according to energy consumption utility parameters and time delay parameters of these tasks in different cruise directions of these UAVs. Simulation results verify that the proposed method can achieve lower average task energy consumption and average task delay. [ABSTRACT FROM AUTHOR]

OpenMP has become a reference standard for the design of parallel applications. This standard is evolving quickly, thus offering new opportunities to the application programmers. However, OpenMP runtime environments are often not fully aligned with the actual requirements imposed by the evolution of such a standard. Among the main lacks, we find: (a) a limited capability to effectively cope with task priorities, and (b) the inadequacy in guaranteeing core properties while processing tasks such as the so-called work-conservativeness—the ability of the OpenMP runtime environment to fully exploit the underlying multi-processor/multi-core machine through the avoidance of thread-blocking phases. In this article, we present the design of extensions to the GNU OpenMP (GOMP) implementation, integrated into gcc, which allow the effective management of tasks and their priorities. Our proposal is based on a user-space library—modularly combined with the one already offered by GOMP—and an external kernel-level Linux module—offering the opportunity to exploit raising hardware facilities for task/priority management. We also provide experimental results showing the effectiveness of our proposal, achieved by running either OpenMP common benchmarks or a new benchmark application (Hashtag-Text) that we explicitly devised to stress the runtime environment in relation to the above-mentioned task/priority management aspects. [ABSTRACT FROM AUTHOR]

Few-shot learning is an extremely challenging task in computer vision that has attracted increased research attention in recent years. However, most recent methods do not fully use the task’s information, and few of the seen samples result in large intraclass differences among the same classes. In this paper, we propose a novel task encoding with distribution calibration (TEDC) model for few-shot learning, which uses the relationships among the feature distributions to reduce intraclass differences. In the TEDC model, an integrated feature extraction module (IFEM) is proposed, which extracts the multiangle visual features of an image and fuses them to obtain more representative features. To effectively utilize the task information, a novel task encoding module (TEM) is proposed, which obtains the task features by fusing all the seen samples’ information and uses them to adjust all the samples’ features for more generalizable task-specific representations. We also propose a distribution calibration module (DCM) to reduce the bias between the distribution of the support features and the query features in the same class. Extensive experiments show that our proposed TEDC model achieves an excellent performance and outperforms the state-of-the-art methods on three widely used few-shot classification benchmarks, specifically miniImageNet, tieredImageNet and CUB-200-2011. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of user tasks in the conceptual models, with an overview of the objectives and functions of the catalogue from Cutter to the International Cataloguing Principles, a critical reflection on user tasks and a proposal for a rethinking of these. The user tasks as presented in the conceptual models present critical aspects, in particular they do not bring out some important aspects concerning the users' motivations for searches and the users' competences. The presentation of some critical aspects of user Ttasks is followed by an initial proposal for a more complex reflection on how users use catalogues to carry out searches of different kinds. [ABSTRACT FROM AUTHOR]

In this study, we developed an evaluation method using image analysis for reaching tasks. Using this method, we studied forearm function during the reaching task in rats that received a unilateral injection of 6-OHDA into the striatum. The success ratio of the reaching task reduced to 40.5% seven days after the injection. In addition, significant changes were observed in the pronation angle of the forearm, posture control, and targeting (i.e., the distance between all fingertips and the center of the target pellet). Thus, unilateral injection of 6-OHDA reduces dopaminergic function in the brain and causes deterioration of forearm function and posture control in the reaching task. [ABSTRACT FROM AUTHOR]

Suspension-based locks are widely used in real-time systems to coordinate simultaneous accesses to exclusive shared resources. Although suspension-based locks have been well studied for sequential real-time tasks, little work has been done on this topic for parallel real-time tasks. This article for the first time studies the problem of how to extend existing sequential-task locking protocols and their analysis techniques to the parallel task model. More specifically, we extend two locking protocols OMLP and OMIP, which were designed for clustered scheduling of sequential real-time tasks, to federated scheduling of parallel real-time tasks. We present corresponding blocking analysis techniques, and develop path-oriented techniques to analyze and count blocking time. Schedulability tests with different efficiency and accuracy are further developed. Experiments are conducted to evaluate the performance of our proposed approaches against the state-of-the-art. [ABSTRACT FROM AUTHOR]

Automatic detection of cervical lesion cells or cell clumps using cervical cytology images is critical to computer-aided diagnosis (CAD) for accurate, objective, and efficient cervical cancer screening. Recently, many methods based on modern object detectors were proposed and showed great potential for automatic cervical lesion detection. Although effective, several issues still hinder further performance improvement of such known methods, such as large appearance variances between single-cell and multi-cell lesion regions, neglecting normal cells, and visual similarity among abnormal cells. To tackle these issues, we propose a new task decomposing and cell comparing network, called TDCC-Net, for cervical lesion cell detection. Specifically, our task decomposing scheme decomposes the original detection task into two subtasks and models them separately, which aims to learn more efficient and useful feature representations for specific cell structures and then improve the detection performance of the original task. Our cell comparing scheme imitates clinical diagnosis of experts and performs cell comparison with a dynamic comparing module (normal-abnormal cells comparing) and an instance contrastive loss (abnormal-abnormal cells comparing). Comprehensive experiments on a large cervical cytology image dataset confirm the superiority of our method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest. [ABSTRACT FROM AUTHOR]

As one of the core components of high-end manufacturing equipment, the linear motor (LM) plays an important role in high efficiency and high quality production of the equipment. In this article, a high-precision robust control method with periodic dynamic compensation is proposed for the most common repetitive trajectory tracking task in the manufacturing industry. To overcome the influence of unknown periodic dynamics on the control accuracy, a B-spline wavelet neural network observer is designed to estimate the uncertainties. Moreover, the control framework in the form of robust control is employed to ensure the stability of the closed-loop system under other unknown aperiodic disturbance. Finally, the high-precision control for LM-driven stage in high-speed environment is realized, and the comparative experiments show that the control accuracy of the proposed method is significantly improved at least 28.9% compared with the other three existing control methods. [ABSTRACT FROM AUTHOR]

This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities. Thus, we attempt to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features. Recently, some nonlinear multi-modal fusion approaches are proposed by utilizing nonlinear attention mechanism that is extended from Squeeze-and-Excitation Networks (SENet). Inspired by this, we propose a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN) to effectively address the problem of elderly activity recognition, which learns modal and channel-wise Expansion-Squeeze-Excitation (ESE) attentions for attentively fusing the multi-modal features in the modal and channel-wise ways. Specifically, ESE-FN firstly implements the modal-wise fusion with the Modal-wise ESE Attention (M-ESEA) to aggregate discriminative information in modal-wise way, and then implements the channel-wise fusion with the Channel-wise ESE Attention (C-ESEA) to aggregate the multi-channel discriminative information in channel-wise way (referring to). Furthermore, we design a new Multi-modal Loss (ML) to keep the consistency between the single-modal features and the fused multi-modal features by adding the penalty of difference between the minimum prediction losses on single modalities and the prediction loss on the fused modality. Finally, we conduct experiments on a largest-scale elderly activity dataset, i.e., ETRI-Activity3D (including 110,000+ videos, and 50+ categories), to demonstrate that the proposed ESE-FN achieves the best accuracy compared with the state-of-the-art methods. In addition, more extensive experimental results show that the proposed ESE-FN is also comparable to the other methods in terms of normal action recognition task. [ABSTRACT FROM AUTHOR]

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of $\frac{1}{2}(1+\beta +\sqrt{(1+\beta)^2-\frac{4}{m}})$ 1 2 (1 + β + (1 + β) 2 - 4 m ) when scheduling multiple DAG tasks with constrained deadlines, in which $m$ m is the number of processors and $\beta$ β is the maximum ratio of task period to deadline. This value is lower than the current best result $\beta +2\sqrt{(\beta +1-\frac{1}{m})(1-\frac{1}{m})}$ β + 2 (β + 1 - 1 m) (1 - 1 m) . We also prove that a capacity augmentation bound of $\frac{1}{2}(1+\sqrt{2}+\sqrt{(1+\sqrt{2})^2-\frac{4\sqrt{2}}{m}})$ 1 2 (1 + 2 + (1 + 2) 2 - 4 2 m ) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets. [ABSTRACT FROM AUTHOR]

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing. [ABSTRACT FROM AUTHOR]

Web virtual reality (WebVR) is gaining increasing attention as interactive VR experiences become more prevalent. The high energy consumption and strict quality requirements are still significant challenges for its application in the mobile edge network. In this context, content caching and task offloading are promising solutions to save the energy of WebVR. This paper proposes a hybrid decentralized offloading architecture in which the MEC server and multiple terminals jointly participate in the caching and processing of WebVR tasks. In this framework, the graph convolutional network and unsupervised clustering algorithm are jointly applied to process the WebVR service feature graph to achieve FoV-level content caching. Moreover, WebVR users with idle computing resources assist neighboring users with performing tasks. On this basis, we propose a distributed low-bound-based alternative direction method of multiplier (LADMM) algorithm to optimize the offloading mode and the allocation of tasks and computing power resources to minimize system energy consumption. The proposed offloading mode can lower energy consumption while maintaining a good balance between delay performance and resource utilization. [ABSTRACT FROM AUTHOR]

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay. [ABSTRACT FROM AUTHOR]

The article proposes a visual-inertial navigation method to support the autonomous operation of a quadrotor UAV during the building façade inspection tasks, where state-of-the-art vision-based localization methods may fail due to texture point feature insufficiency. Considering the appearance characteristics of the building façades, we additionally fuse line features and their corresponding structure prior information to the sliding-window-based estimator to improve localization reliability and accuracy. The contribution of the proposed method lies mainly in two aspects. First, we develop an informative feature selection mechanism according to the façade patterns and the inspection trajectory patterns to make a balance between localization accuracy and computation overheads. Second, we further utilize structure prior information, which is defined as line-to-line and point-to-line relationships, as another source of high-fidelity measurement to restrain the localization drifts. The proposed method is tested not only on public datasets, but also on an actual flight data package recorded in a building inspection task. Experimental results show that the proposed method can serve as a practical tool for navigating a robot in a building inspection task, with improved localization reliability and accuracy. [ABSTRACT FROM AUTHOR]

We constructed an open multi-access network platform using open source hardware and software. The open multi-access network platform is characterized by flexible utilization of network functions, integral management and control of wired and wireless access networks, zero-touch provisioning, intelligent resource monitoring, and dynamic task offloading. We also propose application-driven dynamic task offloading that utilizes intelligent resource monitoring to ensure effective task processing in edge and cloud servers. For this purpose, we developed a mobile application and server applications for the open multi-access network platform. To investigate the feasibility and availability of our developed platform, we experimentally and analytically evaluated the effectiveness of application-driven dynamic task offloading and intelligent resource monitoring. The experimental results demonstrate that application-driven dynamic task offloading could reduce real-time task response time and traffic over metro and core networks. [ABSTRACT FROM AUTHOR]

Deterministic executions of computation-intensive and delay-sensitive tasks are vital for realizing Industry 4.0. Multi-access edge computing (MEC) enables critical tasks to be offloaded to the network edge for quickly responding and processing tasks locally. However, during workload peaks at the resource-limited edge, the best effort IP forwarding in the typical network architecture incurs long-tail latency at both the access and interconnection networks of MEC systems. The long-tail latency results in strict service level agreements (SLAs) unable to be guaranteed in end-to-end communication. In this article, we propose a task deterministic network (TDN) architecture that provides communication with bounded low latency and zero jitter for critical tasks among MEC systems. In TDN, the cross-domain collaboration mechanisms of the layer 2/layer 3 deterministic networking and 5G are jointly designed to eliminate the long-tail latency in typical MEC system networks. In addition, we design a seamless working scheme for the time-sensitive networking controller, the MEC orchestrator, and other coordinators to enable edge collaboration and global deterministic forwarding. Moreover, the main functions of TDN are detailed, including clock synchronization, network slicing, cycle mapping, and multi-layer collaboration. Finally, we present simulation results to substantiate the performance of TDN for accommodating critical service flows among MEC systems. [ABSTRACT FROM AUTHOR]

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern: (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods; and (4) baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage. [ABSTRACT FROM AUTHOR]

With the advent of deep learning, many dense prediction tasks, i.e., tasks that produce pixel-level predictions, have seen significant performance improvements. The typical approach is to learn these tasks in isolation, that is, a separate neural network is trained for each individual task. Yet, recent multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint, by jointly tackling multiple tasks through a learned shared representation. In this survey, we provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision, explicitly emphasizing on dense prediction tasks. Our contributions concern the following. First, we consider MTL from a network architecture point-of-view. We include an extensive overview and discuss the advantages/disadvantages of recent popular MTL models. Second, we examine various optimization methods to tackle the joint learning of multiple tasks. We summarize the qualitative elements of these works and explore their commonalities and differences. Finally, we provide an extensive experimental evaluation across a variety of dense prediction benchmarks to examine the pros and cons of the different methods, including both architectural and optimization based strategies. [ABSTRACT FROM AUTHOR]

In human–robot shared manufacturing contexts, product parts or tools hand-over between the robot and the human is an important collaborative task. Facilitating the robot to figure out and predict human hand-over intentions correctly to improve the task efficiency in human–robot collaboration is therefore a necessary issue to be addressed. In this study, a teaching-learning-prediction (TLP) framework is proposed for the robot to learn from its human partner’s multimodal demonstrations and predict human hand-over intentions. In this approach, the robot can be programmed by the human through demonstrations utilizing natural language and wearable sensors according to task requirements and the human’s working preferences. Then the robot learns from human hand-over demonstrations online via extreme learning machine (ELM) algorithms to update its cognition capacity, allowing the robot to use its learned policy to predict human intentions actively and assist its human companion in hand-over tasks. Experimental results and evaluations suggest that the human may program the robot easily by the proposed approach when the task changes, as the robot can effectively predict hand-over intentions with competitive accuracy to complete the hand-over tasks. Note to Practitioners—This article is motivated by human–robot hand-over problems in smart manufacturing contexts. Product parts or tools delivery in worker–robot partnerships is an important collaborative task. We develop a teaching-learning-prediction (TLP) framework for the robot to learn from its human partner’s multimodal demonstrations and predict human hand-over intentions. The robot can be taught by human through natural language and wearable sensing information. The extreme learning machine (ELM) approach is employed for the robot to build its cognition capacity to predict human intentions actively and assist its human companion in hand-over tasks. We demonstrate that the proposed approach presents distinct and effective advantages to facilitate human–robot hand-over tasks in collaborative manufacturing contexts. [ABSTRACT FROM AUTHOR]

Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as objective performance indicators, OPIs) computed during key surgical tasks correlate with surgeon skill and clinical outcomes. Unambiguous identification of these surgical tasks can be particularly challenging for both human annotators and algorithms. Each surgical procedure has multiple approaches, each surgeon has their own level of skill, and the initiation and termination of surgical tasks can be subject to interpretation. As such, human annotators and machine learning models face the same basic problem, accurately identifying the boundaries of surgical tasks despite variable and unstructured information. For use in surgeon feedback, OPIs should also be robust to the variability and diversity in this data. To mitigate this difficulty, we propose a probabilistic approach to surgical task identification and calculation of OPIs. Rather than relying on tasks that are identified by hard temporal boundaries, we demonstrate an approach that relies on distributions of start and stop times, for a probabilistic interpretation of when the task was performed. We first use hypothetical data to outline how this approach is superior to other conventional approaches. Then we present similar analyses on surgical data. We find that when surgical tasks are identified by their individual probabilities, the resulting OPIs are less sensitive to noise in the identification of the start and stop times. These results suggest that this probabilistic approach holds promise for the future of surgical data science. [ABSTRACT FROM AUTHOR]

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model workers’ preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers’ preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, wherein workers’ tolerable waiting time, consensus, and tasks’ rewards are taken into consideration. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods. [ABSTRACT FROM AUTHOR]

This article proposes a decentralized collaboration scheme for the motion planning of multiagent systems under coupled linear temporal logic task specifications. In order to alleviate the massive computational complexity in centralized methods, coupled edges are introduced to decouple the product automata, and then the path of each agent is synthesized according to local messages. Furthermore, in order to achieve the real-time message exchange, the tableau and gossip protocol are employed during online communication, resulting in a distributed collaboration scheme. Finally, based on the resultant decoupled product automata, a united agent model is designed to deal with partial node failures, yielding a more robust collaboration scheme. Simulations are conducted to demonstrate the effectiveness and superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course. [ABSTRACT FROM AUTHOR]

Complex manipulation tasks can contain various execution branches of primitive skills in sequence or in parallel under different scenarios. Manual specifications of such branching conditions and associated skill parameters are not only error-prone due to corner cases, but also quickly untraceable given a large number of objects and skills. On the other hand, learning from demonstration has increasingly shown to be an intuitive and effective way to program such skills for industrial robots. Parameterized skill representations allow generalization over new scenarios, which however makes the planning process much slower thus unsuitable for online applications. In this article, we propose a hierarchical and compositional planning framework that learns a geometric task network (GTN) from exhaustive planners, without any manual inputs. A GTN is a goal-dependent task graph that encapsulates both the transition relations among skill representations and the geometric constraints underlying these transitions. This framework has shown to improve dramatically the offline learning efficiency, the online performance, and the transparency of decision process, by leveraging the task-parameterized models. We demonstrate the approach on a 7-DoF robot arm both in simulation and on hardware solving various manipulation tasks. [ABSTRACT FROM AUTHOR]

As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this article, we propose CARTAD, a new reinforcement learning (RL)-based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty of CARTAD framework is that we exploit the machine learning technique to analyze the applications’ intermediate representations (IRs) generated by a compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct an RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluate CARTAD on real platforms in comparison with the state-of-the-art approaches. Experimental results show CARTAD can reduce the maximum temperature by up to 16 °C and the average temperature by up to 10 °C. [ABSTRACT FROM AUTHOR]

Small data hold big artificial intelligence (AI) potential. As one of the promising small data AI approaches, few-shot learning has the goal to learn a model efficiently that can recognize novel classes with extremely limited training samples. Therefore, it is critical to accumulate useful prior knowledge obtained from large-scale base class dataset. To realize few-shot scene classification of optical remote sensing images, we start from a baseline model that trains all base classes using a standard cross-entropy loss leveraging two auxiliary objectives to capture intrinsical characteristics across the semantic classes. Specifically, rotation prediction learns to recognize the 2-D rotation of an input to guide the learning of class-transferable knowledge, and contrastive learning aims to pull together the positive pairs while pushing apart the negative pairs to promote intraclass consistency and interclass inconsistency. We jointly optimize two such pretext tasks and semantic class prediction task in an end-to-end manner. To further overcome the overfitting issue, we introduce a regularization technique, adversarial model perturbation, to calibrate the pretext tasks so as to enhance the generalization ability. Extensive experiments on public remote sensing benchmarks including Northwestern Polytechnical University (NWPU)-RESISC45, aerial image dataset (AID), and Wuhan University (WHU)-remote sensing (RS)-19 demonstrate that our method works effectively and achieves best performance that significantly outperforms many state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with $1.3\times $ fewer memory, $2.1\times $ less power consumption, and $2.9\times $ higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs. [ABSTRACT FROM AUTHOR]

Targeting global EDF scheduling, this article proposes an optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interference-based sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a one-way search–based optimal algorithm with polynomial time complexity. We present a formal proof of the optimality of the proposed algorithm. We first address the single-segment task model and then extend to the multi-segment task model. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 (1 + ξ) ln (d + 1) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms. [ABSTRACT FROM AUTHOR]

We observe that data access and processing takes a significant amount of time in large-scale deep learning training tasks (DLTs) on image datasets. Three factors contribute to this problem: (1) the massive and recurrent accesses to large numbers of small files; (2) the repeated, expensive decoding computation on each image, and (3) the frequent communication between computation nodes and storage nodes. Existing work has addressed some aspects of these problems; however, no end-to-end solutions have been proposed. In this article, we propose DIESEL+, an all-in-one system which accelerates the entire I/O pipeline of deep learning training tasks. DIESEL+ contains several components: (1) local metadata snapshot; (2) per-task distributed caching; (3) chunk-wise shuffling; (4) GPU-assisted image decoding and (5) online region-of-interest (ROI) decoding. The metadata snapshot removes the bottleneck on metadata access in frequent reading of large numbers of files. The per-task distributed cache across the worker nodes of a DLT task to reduce the I/O pressure on the underlying storage. The chunk-based shuffle method converts small file reads into large chunk reads, so that the performance is improved without sacrificing the training accuracy. The GPU-assisted image decoding and the online ROI method minimize the image decoding workloads and reduce the cost of data movement between nodes. These techniques are seamlessly integrated into the system. In our experiments, DIESEL+ outperforms existing systems by a factor of two to three times on the overall training time. [ABSTRACT FROM AUTHOR]

Multiagent dynamic task assignment of forest fires is a complicated optimization problem because it requires the consideration of multiple factors, such as the spread speed of fires, firefighting speed of agents, the movement speed of agents, and the number of deployed agents. In this article, we investigate multiagent dynamic task assignment based on a forest fire point model, the objective of which is to minimize task completion time. First, we establish a model for the spread of fire and dynamic task assignments. Second, we prove that the optimal static task assignment always makes all task completion times the same under certain assumptions. Furthermore, we calculate the optimal solution to the static task assignment problem assuming no travel time for the agents, which provides the theoretical basis for the initial deployment and dynamic deployment. Third, we propose a dynamic task assignment scheme based on the global information, which ensures that every reassignment reduces the task completion time and makes all task completion times close to each other. Finally, the simulation is carried out on the MATLAB platform to verify the performance of the proposed dynamic task assignment scheme by comparing with a multistage global auction algorithm. We hope that this work provides insight for decision-makers designing reasonable assignment strategies based on the model and solving assignment optimization problem in different situations. Note to practitioners—The forest firefighting problem considered in this article is a typical multitask and multistage optimization problem. Many searching algorithms for multistage optimization problem are available in the existing literature. However, one of the main challenges is that the time of searching increases exponentially with the number of stages. This work first proves that the tasks are completed in the minimum amount of time, under the constraint of one-shot assignment. This finding helps us to evaluate the gap between the searching algorithm and the optimal solution. In addition, in practice, if the underlying dynamic process can be modeled or partially modeled, then we can predict the behavior of future stages and reduce the searching domain. If a model is available, then we can also adjust the assignment scheme dynamically based on the principle that each adjustment would reduce the total time of tasks completion. In this article, we establish a dynamical fire-spreading model and propose a model-based solution to the multistage optimization problems. The findings in this work can serve as a supplement to the existing optimization algorithms. [ABSTRACT FROM AUTHOR]

This article presents a cooperative dynamic task assignment framework for a certain class of autonomous underwater vehicles (AUVs) employed to control the outbreak of Crown-Of-Thorns Starfish (COTS) in Australia’s Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment, including seabed terrain, clusters of COTS, and coastlines, is constructed. Then, a novel heuristic algorithm called heuristic fleet cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef. Note to Practitioners—This research is motivated by controlling the outbreak of Crown-Of-Thorns Starfish (COTS), in Australia’s Great Barrier Reef, by employing a certain class of autonomous underwater vehicles (AUVs) equipped with the injection arm. A new evolutionary algorithm, called heuristic fleet cooperation (HFC), is designed upon a hierarchal priority evolution to facilitate a multivehicle dynamic task assignment problem. The HFC is able to rapidly prototype multiple optimal solutions for efficient grouping and accomplishment of the tasks spread over a large operation area. [ABSTRACT FROM AUTHOR]

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring the spatial distribution to be decomposed as Fourier series. This approach does not scale well to control problems requiring exploration in search space of more than two dimensions. To address this issue, we propose the use of tensor trains, a recent low-rank tensor decomposition technique from the field of multilinear algebra. The proposed solution is efficient, both computationally and storagewise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task requiring full 6-D end-effector poses, implemented with a seven-axis Franka Emika Panda robot. In this experiment, ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors. [ABSTRACT FROM AUTHOR]

The task-schedulingalgorithm is a key module to satisfy various complex user requirements, and improve the usage flexibility and efficiency of data relay satellites networks (DRSN). In this context, we first propose a novel application mode for DRSN, in which users are allowed to submit multiple optional service time windows and specify a preferred antenna as well as an expected execution duration for each task. Meanwhile, the start time of a service time window can be adjusted within a specified range. A mathematical programming model that maximizes the completion ratio of tasks and the expectation satisfaction of users is established. Moreover, a conflict resolution-assisted iterative task-scheduling algorithm (CRITS) is designed, composing of five closely dependent operators: resource matching, service durations generation, conflict evaluation, conflict resolution, and solution update. To verify the effectiveness of the proposed CRITS, extensive experiments are carried out. The experimental results demonstrate the competitive performance of CRITS in addressing the DRSN scheduling problem. In comparison with two heuristic algorithms (heuristic algorithm based on time-freedom degree and a heuristic algorithm based on task priority) and a meta-heuristic algorithm (adaptive variable neighborhood descent combined with a tabu list), the proposed CRITS increases the overall completion ratio of tasks by 6.65, 10.26, and 10.96%, respectively. [ABSTRACT FROM AUTHOR]

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their weights as a warm start of meta-training, and (2) freezing their convolutional layers as the feature extractor of base-learners. In this paper, we propose a novel approach called meta-transfer learning (MTL), which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights (and biases) for each task. To further boost the learning efficiency of MTL, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum of few-shot classification tasks. We conduct experiments for five-class few-shot classification tasks on three challenging benchmarks, miniImageNet, tieredImageNet, and Fewshot-CIFAR100 (FC100), in both supervised and semi-supervised settings. Extensive comparisons to related works validate that our MTL approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy. [ABSTRACT FROM AUTHOR]

This article considers heterogeneous, cold standby systems performing missions with the fixed amount of work when a failure of an operating element results in a mission failure. A system is operating in a random environment modeled by the Poisson process of shocks. Each shock decreases the remaining lifetime of an operating element and, therefore, its preventive replacement (PR) is scheduled on experiencing the predetermined number of shocks. An important feature of the discussed model is that the failure can also occur during these PRs (task transfers) with two elements involved. The duration of the task transfer depends on the time from the start of a mission. The recursive equations for obtaining the mission success probability are derived and the corresponding numerical algorithm is developed. The number of shocks triggering elements’ replacements is obtained as a solution of the formulated optimization problem. The numerical example with the detailed analysis for a set of virtual machines operating in a cloud computing environment is presented. [ABSTRACT FROM AUTHOR]

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR). [ABSTRACT FROM AUTHOR]

Dynamically adaptive streaming over HTTP requires a large-scale server to transcode various bitrate versions in which different preset parameters can be used to provide different video qualities at each resolution. When transcoding servers contain a heterogeneous mix of CPUs and GPUs, the task scheduler must choose a processor and preset parameter for each transcoding task to meet the transcoding deadlines while achieving the best possible video quality. We apply regression analysis to sample variable-bit-rate videos to provide accurate (mean absolute percentage error values from 1.3% to 13.9%) model for predicting bitrate, transcoding time and video quality at each resolution on different processors. We build this into a greedy allocation and scheduling algorithm which first satisfies deadlines with low video quality, and then redistributes the workload to improve that quality while continuing to meet the deadlines. This scheme was both simulated and implemented on a testbed server. It satisfies all deadlines while outperforming standard algorithms by between 3.12% and 15.59% in terms of popularity-weighted video quality divided by bitrate. [ABSTRACT FROM AUTHOR]

This article considers the problem of planning a trajectory that maximizes the probability that a robot will be able to complete a set of point-to-point tasks, after experiencing locked joint failures. The proposed approach first develops a method to calculate the probability of task failure for an arbitrary trajectory based on its failure scenarios, which are efficiently computed by identifying the ranges of task point self-motion manifolds. Then, a novel trajectory planning algorithm is proposed to find the optimal trajectory with maximum probability of task completion. The planning algorithm exploits the overlap of self-motion manifold bounding boxes, as opposed to always using the shortest distance, to determine an optimal trajectory. The proposed trajectory planning algorithm is demonstrated on planar positioning 3R, spatial positioning 4R, and spatial positioning/orienting 7R redundant robots, resulting in average improvement of 17%, 22%, and 30%, respectively, compared to the best shortest distance trajectory. [ABSTRACT FROM AUTHOR]

In recent years, the increasing concern about the labour market implications of technological change has led economists to look in more detail at the structure of work content and job tasks. Incorporating insights from other traditions of task analysis, in particular from the labour process approach, as well as from recent research on skills, work organisation and occupational change, in this paper we propose a comprehensive and detailed taxonomy of tasks. Going beyond existing broad classifications, our taxonomy aims at connecting the substantive content of work with its organisational context by answering two key questions: what do people do at work and how do they do their work? For illustrative purposes, we show how our approach allows a better understanding of the impact of new technologies on work, by accounting for relevant ongoing transformations such as the diffusion of artificial intelligence and the unfolding of digital labour platforms. [ABSTRACT FROM AUTHOR]

The traditional object (person) retrieval (re-identification) task aims to learn a discriminative feature representation with intra-similarity and inter-dissimilarity, which supposes that the objects in an image are manually or automatically pre-cropped exactly. However, in many real-world searching scenarios (e.g., video surveillance), the objects (e.g., persons, vehicles, etc.) are seldom accurately detected or annotated. Therefore, object-level retrieval becomes intractable without bounding-box annotation, which leads to a new but challenging topic, i.e., image-level search with multi-task integration of joint detection and retrieval. In this paper, to address the image search issue, we first introduce an end-to-end Integrated Net (I-Net), which has three merits: 1) A Siamese architecture and an on-line pairing strategy for similar and dissimilar objects in the given images are designed. Benefited by the Siamese structure, I-Net learns the shared feature representation, because, on which, both object detection and classification tasks are handled. 2) A novel on-line pairing (OLP) loss is introduced with a dynamic feature dictionary, which alleviates the multi-task training stagnation problem, by automatically generating a number of negative pairs to restrict the positives. 3) A hard example priority (HEP) based softmax loss is proposed to improve the robustness of classification task by selecting hard categories. The shared feature representation of I-Net may restrict the task-specific flexibility and learning capability between detection and retrieval tasks. Therefore, with the philosophy of divide and conquer, we further propose an improved I-Net, called DC-I-Net, which makes two new contributions: 1) two modules are tailored to handle different tasks separately in the integrated framework, such that the task specification is guaranteed. 2) A class-center guided HEP loss (C $^2$ 2 HEP) by exploiting the stored class centers is proposed, such that the intra-similarity and inter-dissimilarity can be captured for ultimate retrieval. Extensive experiments on famous image-level search oriented benchmark datasets, such as CUHK-SYSU dataset and PRW dataset for person search and the large-scale WebTattoo dataset for tattoo search, demonstrate that the proposed DC-I-Net outperforms the state-of-the-art tasks-integrated and tasks-separated image search models. [ABSTRACT FROM AUTHOR]

Obstacle detection and facial emotion recognition are two critical visual tasks for pedestrians. In previous studies, the effect of changes in lighting was tested for these as individual tasks, where the task to be performed next in a sequence was known. In natural situations, a pedestrian is required to attend to multiple tasks, perhaps simultaneously, or at least does not know which of several possible tasks would next require their attention. This multi-tasking might impair performance on any one task and affect evaluation of optimal lighting conditions. In two experiments, obstacle detection and facial emotion recognition tasks were performed in parallel under different illuminances. Comparison of these results with previous studies, where these same tasks were performed individually, suggests that multi-tasking impaired performance on the peripheral detection task but not the on-axis facial emotion recognition task. [ABSTRACT FROM AUTHOR]

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred through three transfer rules in another driving mission and evaluated. Simulation results reveal that the decision-making strategies related to similar tasks are transferable and have a high success rate. It indicates that the presented control framework could reduce time consumption and realize online implementation. Therefore, the transfer RL concept is helpful for establishing the real-time decision-making policy for autonomous vehicles. [ABSTRACT FROM AUTHOR]