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".

Example Input: Paragraph: Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems. ½S i ðt 2 Þ þ S i ðt 6 Þ=2 and hS i ðt 2 þ 1Þi ¼ ½S i ðt 12 Þ þ S i ðt 14 Þ=2 based on the remaining strings marked in different colours (see Methods for more details). CST can be used to reconstruct the neighbouring vector X of node 2 from Y and U from Y ¼ U . X. 
 Title: ARTICLE Reconstructing propagation networks with natural diversity and identifying hidden sources
Example Output: False

Example Input: Paragraph: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns. 
 Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk
Example Output: True

Example Input: Paragraph: Quickly detecting harmful cascades in networks can allow us to analyze the causes and prevent further spreading of destructive influence. Since it is often impossible to observe the state of all nodes in a network, a common method is to detect harmful cascades from sparsely placed sensors. However, the harmful cascades are usually dynamic (e.g., the cascade initiators and diffusion trajectories can change over the time), which can severely destroy the robustness of selected sensors. Meanwhile the large scale of current networks greatly increases the time complexity of sensor selection. Motivated by the observation, in this paper we investigate the scalable sensor selection problem for early detection of dynamic harmful cascades in networks. Specifically, we first put forward a dynamic susceptible-infected model to describe harmful cascades, and formally define a detection time minimization (DTM) problem which focuses on effective sensors placement for early detection of dynamic cascades. We prove that it is #P-hard to calculate the objective function exactly and propose two Monte-Carlo methods to estimate it efficiently. We prove the NP-hardness of DTM problem and design a corresponding greedy algorithm. Based on that, we propose an efficient upper bound based greedy (UBG) algorithm with the theoretical performance guarantee reserved. To further meet different types of large-scale networks, we propose two accelerations of UBG: Quickest-Path-UBG for sparse networks and Local-Reduction-UBG for dense networks to improve the time complexity. The experimental results on synthetic and real-world social networks demonstrate the practicality of our approaches. 
 Title: Early detection of dynamic harmful cascades in large-scale networks ଝ
Example Output:
False