Wernerwilkerson6820

Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale labeled data set, which incurs a heavy burden due to manual annotation. Domain adaptation is one of the most promising solutions to this problem, where rich labeled data from the relevant source domain are utilized to strengthen the generalizability of a model based on the target domain. buy CAL-101 However, the mainstream cross-domain NER models are still affected by the following two challenges (1) Extracting domain-invariant information such as syntactic information for cross-domain transfer. (2) Integrating domain-specific information such as semantic information into the model to improve the performance of NER. In this study, we present a semi-supervised framework for transferable NER, which disentangles the domain-invariant latent variables and domain-specific latent variables. In the proposed framework, the domain-specific information is integrated with the domain-specific latent variables by using a domain predictor. The domain-specific and domain-invariant latent variables are disentangled using three mutual information regularization terms, i.e., maximizing the mutual information between the domain-specific latent variables and the original embedding, maximizing the mutual information between the domain-invariant latent variables and the original embedding, and minimizing the mutual information between the domain-specific and domain-invariant latent variables. Extensive experiments demonstrated that our model can obtain state-of-the-art performance with cross-domain and cross-lingual NER benchmark data sets.Modular Reinforcement Learning decomposes a monolithic task into several tasks with sub-goals and learns each one in parallel to solve the original problem. Such learning patterns can be traced in the brains of animals. Recent evidence in neuroscience shows that animals utilize separate systems for processing rewards and punishments, illuminating a different perspective for modularizing Reinforcement Learning tasks. MaxPain and its deep variant, Deep MaxPain, showed the advances of such dichotomy-based decomposing architecture over conventional Q-learning in terms of safety and learning efficiency. These two methods differ in policy derivation. MaxPain linearly unified the reward and punishment value functions and generated a joint policy based on unified values; Deep MaxPain tackled scaling problems in high-dimensional cases by linearly forming a joint policy from two sub-policies obtained from their value functions. However, the mixing weights in both methods were determined manually, causing inadequate use of the learned modules. In this work, we discuss the signal scaling of reward and punishment related to discounting factor γ, and propose a weak constraint for signaling design. To further exploit the learning models, we propose a state-value dependent weighting scheme that automatically tunes the mixing weights hard-max and softmax based on a case analysis of Boltzmann distribution. We focus on maze-solving navigation tasks and investigate how two metrics (pain-avoiding and goal-reaching) influence each other's behaviors during learning. We propose a sensor fusion network structure that utilizes lidar and images captured by a monocular camera instead of lidar-only and image-only sensing. Our results, both in the simulation of three types of mazes with different complexities and a real robot experiment of an L-maze on Turtlebot3 Waffle Pi, showed the improvements of our methods.Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Aware Dirichlet networks, that learn an explicit Dirichlet prior distribution on predictive distributions by minimizing a bound on the expected max norm of the prediction error and penalizing information associated with incorrect outcomes. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms, by a large margin, state-of-the-art neural networks for estimating within-distribution and out-of-distribution uncertainty, and detecting adversarial examples.The pathogen burden, defined by the frequency of antibodies to several viruses and a parasite, is greater in Hispanic whites and black populations than it is in non-Hispanic whites, in the USA. The poor and those without higher education also have higher pathogen burdens. The most frequent pathogen that was measured, was the Herpes simplex virus type 1 (HSV-1). This virus can inactivate most of the elements in the immune system, that are designed to protect against the incursions of viruses, bacteria and other pathogens. HSV-1 can also damage the blood brain barrier (BBB), which prevents the entry of pathogens into the central nervous system. Without the help of HSV-1, the COVID-19 virus may not be able to cause serious illness or death in humans. A prophylactic treatment to contain HSV-1, could be vital in the fight against COVID-19.

Perceived food intolerance (PFI) is a distressing condition reported by 3% - 35% of individuals, whereas prevalence of food allergy is 0.9%-3%. The present paper aims to systematically review the evidence for psychological, clinical and psychosocial factors associated with PFI in order to advance the current understanding.

Articles published from 1970 until October 2020 were identified. Case-control, prospective cohort, cross-sectional and retrospective studies published in English that a) included a subject population of adults over 18 with PFI and b) examined psychological, clinical and/or psychosocial factors of PFI were reviewed against inclusion criteria. Methodological quality was assessed, data extracted, and a narrative synthesis conducted.

Of 2864 abstracts identified, thirty-six articles met inclusion criteria. Evidence consistently found PFI is associated with female sex, and individuals with PFI often report physical health complaints including gastrointestinal and extraintestinal symptoms, and gastrointestinal and atopic conditions.