Searching For a Lost Plane

Malaysia plane MH370 disappeared en route from Kuala Lumpur to Beijing on 8, March 2014. Besides considering the factors such as air piracy, weather, electromagnetic wave, and kinds of bugs of the airplane, in order to find the wreckage efficiently the growing concern is to confirm a limited area where the airplane probably fell, and then to find an optimum way to find the plane. It’s essential to build such a model involving both of the two layers mentioned above that can cover all the searching area by using the most efficient way. The first layer is to confirm the limited area. We use the Poisson Probability Distribution, the Drag equation, and the Proper Orthogonal Decomposition Theorem to assume the direction of the airplane and the sea area where it probably fell. All assumptions are based on the actual situation. The second model will basically rely on the Bayesian principles. In this case, the model would be advantageous as it will rely on contingency as an important role in the search for lost objects in the sea or on land. As matter of fact, any information that is provided to the search team would be put into good use as it will be used in developing the probabilities. It is also good in that it\u27s flexible and would be good enough to sustain the ongoing search even with new information or facts obtained regarding the flight of the plane and/or the initial findings of the debris. This helps in rounding down to a lesser geographical search region and, by extension, increases the probability of getting the plane

INSET: Sentence Infilling with INter-SEntential Transformer

Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. This task asks the model to generate intermediate missing sentences that can syntactically and semantically bridge the surrounding context. Solving the sentence infilling task requires techniques in natural language processing ranging from understanding to discourse-level planning to generation. In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. We empirically demonstrate the effectiveness of our model in learning a sentence representation for generation and further generating a missing sentence that fits the context.Comment: Y.H. and Y.Z. contributed equally to this work. v2: published version with updated results and reference

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape

CRISPR/Cas9-Facilitated Chromosome Engineering to Model Human Chromosomal Alterations

Rodents, particularly the mouse, have been used extensively for genetic modeling and analysis of human chromosomal alterations based on the syntenic conservations between the human and rodent genomes. In this article, we will discuss the emergence of CRISPR/Cas9-facilitated chromosome engineering techniques, which may open up a new avenue to study human diseases associated with chromosomal abnormalities, such as Down syndrome and cancer