In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Ex Input:
Paragraph: Among the causes for pregnancy loss, viruses and non-infectious factors are among the most important. In both dogs and cats, research and clinical evidence provide proof that there is an increasing incidence of pregnancy loss associated with infectious diseases like herpesvirus, as well as the presence of toxicants or chemicals in the animal's diet and environment. Endocrine causes must be taken into consideration when dealing with pregnancy loss. This review will cover the most recent knowledge regarding viral and non-infectious of pregnancy losses in the dog and cat. #
Title: Canine and feline pregnancy loss due to viral and non-infectious causes: A review

Ex Output:
True

Ex Input:
Paragraph: Background Characterisation of the severity profi le of human infections with infl uenza viruses of animal origin is a part of pandemic risk assessment, and an important part of the assessment of disease epidemiology. Our objective was to assess the clinical severity of human infections with avian infl uenza A H7N9 virus, which emerged in China in early 2013. Prevention. We estimated the risk of fatality, mechanical ventilation, and admission to the intensive care unit for patients who required hospital admission for medical reasons. We also used information about laboratory-confi rmed cases detected through sentinel infl uenza-like illness surveillance to estimate the symptomatic case fatality risk. 17 patients who were still in hospital, we estimated the fatality risk for all ages to be 36% (95% CI 26-45) on admission to hospital. Risks of mechanical ventilation or fatality (69%, 95% CI 60-77) and of admission to an intensive care unit, mechanical ventilation, or fatality (83%, 76-90) were high. With assumptions about coverage of the sentinel surveillance network and health-care-seeking behaviour for patients with infl uenza-like illness associated with infl uenza A H7N9 virus infection, and pro-rata extrapolation, we estimated that the symptomatic case fatality risk could be between 160 (63-460) and 2800 (1000-9400) per 100 000 symptomatic cases. Interpretation Human infections with avian infl uenza A H7N9 virus seem to be less serious than has been previously reported. Many mild cases might already have occurred. Continued vigilance and sustained intensive control eff orts are needed to minimise the risk of human infection. Funding
Title: Human infection with avian infl uenza A H7N9 virus: an assessment of clinical severity

Ex Output:
True

Ex Input:
Paragraph: Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets.
Title: Concept learning via granular computing: A cognitive viewpoint

Ex Output:
True