3,824 research outputs found

    Capacity of Hybrid Wireless Networks with Long-Range Social Contacts Behavior

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    Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.postprin

    Transcranial direct current stimulation to optimise participation in stroke rehabilitation – A Sham-Controlled Cross-Over feasibility study

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    Background: Fatigue and attentional decline limit the duration of many therapy sessions in older adults poststroke. Transcranial direct current stimulation (tDCS) may facilitate participation in rehabilitation, potentially via reduced fatigue and improved sustained attention poststroke. Objective: To evaluate whether tDCS results in an increase in the number of completed rehabilitation therapy sessions in stroke survivors. Methods: Nineteen participants were randomly allocated to receive 10 sessions of 2-mA anodal (excitatory) tDCS, or sham tDCS, applied to the left dorsolateral prefrontal cortex (DLPFC) for 20 minutes within 1 hour prior to the first rehabilitation therapy session of the day. After a 2-day washout period, participants then crossed-over. Researchers applying the tDCS, and those recording measures were blinded to group allocation. The number of first rehabilitation therapy sessions completed as planned, as well as the total duration of rehabilitation therapy, were used to determine the influence of tDCS on participation in stroke rehabilitation. Results: The total number of first therapy sessions completed as planned did not vary according to group allocation (111 of 139 sessions for tDCS, 110 of 147 sessions for sham treatment; chi-square 1.0; P = .31). Conclusions: Our results suggest that, while tDCS to the DLPFC was well tolerated, it did not significantly influence the number of completed rehabilitation therapy sessions in stroke survivors

    Commission des Communautes Europeennes: Groupe du Porte-Parole = Commission of European Communities: Spokesman Group. Spokesman Service Note to National Offices Bio No. (81) 276, 8 July 1981

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    This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse – but still acceptable – performance when compared to the single language model, while benefiting from better generalization properties across languages

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