Concordia University

About: Concordia University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Context (language use) & Control theory. The organization has 13565 authors who have published 31084 publications receiving 783525 citations. The organization is also known as: Sir George Williams University & Loyola College, Montreal.

Automatic and controlled processes in the first- and second-language reading of fluent bilinguals.

Abstract: Many fluent bilinguals read their two languages with equal levels of comprehension but read their second language at a slower rate. The present study examined whether, compared with first language reading, slower second-language reading is associated with reduced involvement of automatic processing during lexical access. Subjects were bilinguals with fluent speaking and listening skills under ordinary conditions of communication and with equivalent comprehension of their first and second languages when reading and listening under speeded conditions. Half these subjects, however, read their first and second languages equally fast, and half read the second language more slowly than the first. Subjects were tested on a lexical decision task that manipulated expectations about the semantic relatedness of prime and target words and the stimulus onset asynchrony between them. Bilinguals with equal first- and second-language reading rates produced in each language a pattern of reaction times suggesting automatic processing, whereas bilinguals with a slower second-language reading rate did so in their first language but not in their second.

Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing

Abstract: Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.