While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized framework and further show that the learned policies quickly transfer to scenarios with different team sizes along with strong zero-shot generalization performance. This is an important step towards developing multi-agent teams which can be realistically deployed in the real world without assuming complete prior knowledge or instantaneous communication at unbounded distances.
Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. A particular concern is that these networks pose high requirements for computing hardware and training budgets. The state-of-the-art transformer models are a vivid example. Simplifying the computations performed by a network is one way of addressing the issue of the increasing complexity. In this paper, we propose an end to end binarized neural network for the task of intent and text classification. In order to fully utilize the potential of end to end binarization, both the input representations (vector embeddings of tokens statistics) and the classifier are binarized. We demonstrate the efficiency of such a network on the intent classification of short texts over three datasets and text classification with a larger dataset. On the considered datasets, the proposed network achieves comparable to the state-of-the-art results while utilizing ∼ 20-40% lesser memory and training time compared to the benchmarks.
Introduction: Cortisol, an important hormone in the hypothalamic pituitary adrenal axis, has important effects on the metabolism of glucose, protein and lipid[i]. A stress response consists of increased levels of cortisol and catecholamines in the 1st weeks after acute stroke. The cortisol response has been observed in cerebral infarction as well as in intracerebral haemorrhage. Change in serum level of cortisol has been reported in patients with ischemic stroke and studies reported that high levels of this hormone are independently associated with increase in ischemic lesion volume. Also it has been observed that cortisol level in patients with ischemic stroke is associated with significantly increased mortality rate. Increase in the circulating levels of catecholamines was shown in insular damage in experimental stroke suggesting this as a mechanism for the cardiac complications associated with stroke.
Patients and Methods: All patients were included in the study who was admitted within 6 hours in the hospital after the episode of stroke. Scandinavian Stroke Scale (SSS)[ii] was monitored in all patients from admission. SSS was performed every 2 hours in the first 24 hours, every 4 hours in the next 48 hours and then daily up to day 7. Blood samples were obtained for routine investigation and estimation of serum cortisol. No patients had blood samples drawn for cortisol determination between 01:00 and 07:00 am.
Results: Mean age was observed in the current series was 72.8 ± 12.54 years. There were 34 (53.1%) male and 30(46.9%) female. SSS was observed to be 36 (21-47) on admission. History of hypertension, History of stroke, Diabetes mellitus and Atrial fibrillation was observed in 38(59.4%), 12(18.8%), 24(37.5%) and 11(17.2%) respectively. In univariate logistic regression analysis of the relations to 7 days of mortality, s-cortisol, SSS on admission, and pulse rate reached a significance level. Age, atrial fibrillation, blood glucose, body temperature 12 h after stroke onset, and the presence of early infarctions signs did not reach a significance level of 0.1 in univariate testing. S-cortisol level was higher in patients with insular involvement, 635 nmol/l, in comparison to patients without insular involvement, 589 nmol/l.
Conclusion: Adrenal glucocorticoid stress response in acute stroke is harmful. High cortisol levels are associated with the poor outcome and mortality of the patients with stroke.
Keywords: Cortisol, HPA, Stroke, SSS
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