Sykesgrace9770

Recognition of ancient Korean-Chinese cursive character (Hanja) is a challenging problem mainly because of large number of classes, damaged cursive characters, various hand-writing styles, and similar confusable characters. They also suffer from lack of training data and class imbalance issues. To address these problems, we propose a unified Regularized Low-shot Attention Transfer with Imbalance τ-Normalizing (RELATIN) framework. This handles the problem with instance-poor classes using a novel low-shot regularizer that encourages the norm of the weight vectors for classes with few samples to be aligned to those of many-shot classes. To overcome the class imbalance problem, we incorporate a decoupled classifier to rectify the decision boundaries via classifier weight-scaling into the proposed low-shot regularizer framework. To address the limited training data issue, the proposed framework performs Jensen-Shannon divergence based data augmentation and incorporate an attention module that aligns the most attentive features of the pretrained network to a target network. find more We verify the proposed RELATIN framework using highly-imbalanced ancient cursive handwritten character datasets. The results suggest that (i) the extreme class imbalance has a detrimental effect on classification performance; (ii) the proposed low-shot regularizer aligns the norm of the classifier in favor of classes with few samples; (iii) weight-scaling of decoupled classifier for addressing class imbalance appeared to be dominant in all the other baseline conditions; (iv) further addition of the attention module attempts to select more representative features maps from base pretrained model; (v) the proposed (RELATIN) framework results in superior representations to address extreme class imbalance issue.Network pruning techniques are widely employed to reduce the memory requirements and increase the inference speed of neural networks. This work proposes a novel RNN pruning method that considers the RNN weight matrices as collections of time-evolving signals. Such signals that represent weight vectors can be modelled using Linear Dynamical Systems (LDSs). In this way, weight vectors with similar temporal dynamics can be pruned as they have limited effect on the performance of the model. Additionally, during the fine-tuning of the pruned model, a novel discrimination-aware variation of the L2 regularization is introduced to penalize network weights (i.e., reduce the magnitude), whose impact on the output of an RNN network is minimal. Finally, an iterative fine-tuning approach is proposed that employs a bigger model to guide an increasingly smaller pruned one, as a steep decrease of the network parameters can irreversibly harm the performance of the pruned model. Extensive experimentation with different network architectures demonstrates the potential of the proposed method to create pruned models with significantly improved perplexity by at least 0.62% on the PTB dataset and improved F1-score by 1.39% on the SQuAD dataset, contrary to other state-of-the-art approaches that slightly improve or even deteriorate models' performance.Questions about what addiction recovery is and the mechanisms by which people 'recover' have long animated alcohol and other drug research and policy. These debates became even more intense following the advent, and increasing influence in some quarters, of the 'new recovery'. Starting from the position that recovery is ontologically multiple (Mol & Law, 2002), we trace how alcohol and other drug professionals attempted to make sense of 'new recovery' as a concept and set of professional practices during a period of Australian drug treatment system reform. Drawing on Annemarie Mol's (2002) account of organising relations and forms of coordination (addition, translation and distribution), we explore how the new recovery was enacted and coordinated in alcohol and other drug professionals' sociomaterial practices, and highlight the ontological work involved in holding such an unstable object together. First, we argue that the addition of multiple enactments of addiction and recovery contributed to the formation of a singular and serviceable problem (that was simultaneously heterogeneous and complex), making the 'disease-to-be-treated' amenable to diverse treatment approaches, including new recovery. Second, we analyse the role of metaphor in translating authoritative logics and obligations into an enactment of new recovery suitable for application in clinical settings. Lastly, we track how incompatible enactments of recovery, both new and old, were kept apart through distribution. Although new recovery ultimately failed to gain policy traction in the Australian context, we focus on the ontological work undertaken by professionals in response to its introduction as such case studies can be useful for analysing other powerfully governing policy objects and their operations.

Early thrombolysis for acute ischemic stroke (AIS) due to emergent large vessel occlusion (ELVO) is associated with better clinical outcome. This is thought to be due to greater tissue salvage with earlier recanalization. We explored whether ultra-early administration of intravenous tissue plasminogen activator (IV tPA) within 60min (Golden Hour) of symptom onset for AIS due to ELVO is associated with a higher rate of recanalization.

We performed a retrospective analysis of recanalization rates and clinical outcomes in patients with AIS due to ELVO treated with IV tPA, comparing patients who received IV tPA within 60min of stroke symptom onset with those treated beyond 60min.

Between January 2013 and December 2016, 158 patients with AIS due to ELVO were treated with IV tPA. Of these, 25 (15.8%) patients received IV tPA within 60min of stroke symptom onset, while the remaining 133 (84.2%) patients received IV tPA beyond 60min. The ultra-early treatment group was found to have a higher rate of complete recanalization (28.0% vs 6.8%, 95% CI 1.78-16.63), better chance of early neurological improvement (76.0% vs 50.4%, 95% CI 1.16-8.65), favorable clinical outcomes (mRS≤2 or return to premorbid mRS) (65.0% vs 36.8%, 95% CI 1.42-9.34), and lower mortality (5% vs 31.1%, 95% CI 0.01-0.74) at 90-day follow-up compared to the later treatment group.

Our data suggest that ultra-early administration of IV tPA significantly improves recanalization rates and clinical outcomes in patients with AIS due to ELVO.

Our data suggest that ultra-early administration of IV tPA significantly improves recanalization rates and clinical outcomes in patients with AIS due to ELVO.