Efficient feasibility analysis of real-time asynchronous task sets

Several schedulability tests for real-time periodic task sets scheduled under the Earliest Deadline First algorithm have been proposed in literature, including analyses for precedence and resource constraints. However, all available tests consider synchronous task sets only, that are task sets in which all tasks are initially activated at the same time. In fact, every necessary and sufficient feasibility condition for asynchronous task sets, also known as task sets with offsets, is proven to be NP-complete in the number of tasks. We propose a new schedulability test for asynchronous task sets that, while being only sufficient, performs extremely better than available tests at the cost of a slight complexity increase. The test is further extended to task sets with resource constraints, and we discuss the importance of task offsets on the problems of feasibility and release jitter. We then show how our methodology can be extended in order to account for precedence constraints and multiprocessor and distributed computation applying holistic response time analysis to a real-time transaction-based model. This analysis is finally applied to asymmetric multiprocessor systems where it is able to achieve a dramatic performance increase over existing schedulability tests

A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters

Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs to make a number of difficult choices: on which processor should a certain task be implemented? Should a component be implemented in parallel or sequentially? These choices may have a great impact on feasibility, as the difference in the processor internal architectures impact on the tasks' execution time and preemption cost. To help the designer explore the wide space of design choices and tune the scheduling parameters, in this paper we propose a novel real-time application model, called C-DAG, specifically conceived for heterogeneous platforms. A C-DAG allows to specify alternative implementations of the same component of an application for different processing engines to be selected off-line, as well as conditional branches to model if-then-else statements to be selected at run-time. We also propose a schedulability analysis for the C-DAG model and a heuristic allocation algorithm so that all deadlines are respected. Our analysis takes into account the cost of preempting a task, which can be non-negligible on certain processors. We demonstrate the effectiveness of our approach on a large set of synthetic experiments by comparing with state of the art algorithms in the literature

Attentional demand influences strategies for encoding into visual working memory

Visual selective attention and visual working memory (WM) share the same capacity-limited resources. We investigated whether and how participants can cope with a task in which these 2 mechanisms interfere. The task required participants to scan an array of 9 objects in order to select the target locations and to encode the items presented at these locations into WM (1 to 5 shapes). Determination of the target locations required either few attentional resources (“popout condition”) or an attention-demanding serial search (“non pop-out condition”). Participants were able to achieve high memory performance in all stimulation conditions but, in the non popout conditions, this came at the cost of additional processing time. Both empirical evidence and subjective reports suggest that participants invested the additional time in memorizing the locations of all target objects prior to the encoding of their shapes into WM. Thus, they seemed to be unable to interleave the steps of search with those of encoding. We propose that the memory for target locations substitutes for perceptual pop-out and thus may be the key component that allows for flexible coping with the common processing limitations of visual WM and attention. The findings have implications for understanding how we cope with real-life situations in which the demands on visual attention and WM occur simultaneously. Keywords: attention, working memory, interference, encoding strategie

A dynamic neural field model of temporal order judgments

Temporal ordering of events is biased, or influenced, by perceptual organization—figure–ground organization—and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target’s offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (PsycINFO Database Record (c) 2015 APA, all rights reserved

Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Functional neuroanatomy of time-to-passage perception

The time until an approaching object passes the observer is referred to as time-to-passage (TTP). Accurate judgment of TTP is critical for visually guided navigation, such as when walking, riding a bicycle, or driving a car. Previous research has shown that observers are able to make TTP judgments in the absence of information about local retinal object expansion. In this paper we combine psychophysics and functional MRI (fMRI) to investigate the neural substrate of TTP processing. In a previous psychophysical study, we demonstrated that when local retinal expansion cues are not available, observers take advantage of multiple sources of information to judge TTP, such as optic flow and object retinal velocities, and integrate these cues through a flexible and economic strategy. To induce strategy changes, we introduced trials with motion but without coherent optic flow (0% coherence of the background), and trials with coherent, but noisy, optic flow (75% coherence of the background). In a functional magnetic resonance imaging (fMRI) study we found that coherent optic flow cues resulted in better behavioral performance as well as higher and broader cortical activations across the visual motion processing pathway. Blood oxygen-level-dependent (BOLD) signal changes showed significant involvement of optic flow processing in the precentral sulcus (PreCS), postcentral sulcus (PostCS) and middle temporal gyrus (MTG) across all conditions. Not only highly activated during motion processing, bilateral hMT areas also showed a complex pattern in TTP judgment processing, which reflected a flexible TTP response strategy.Accepted manuscrip

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released