In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm’s best performance.

Output: Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments


Input: Consider Input: The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle’s physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDARvideo inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.

Output: Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks


Input: Consider Input: We present a data-driven approach to the problem of inductive computer program synthesis. Our method learns a probabilistic model for realworld programs from a corpus of existing code. It uses this model during synthesis to automatically infer a posterior distribution over sketches, or syntactic models of the problem to be synthesized. Sketches sampled from this posterior are then used to drive combinatorial synthesis of a program in a high-level programming language. The key technical innovation of our approach — embodied in a system called BAYOU— is utilizing user-supplied evidence as to the program’s desired behavior, along with a Bayesian update, to obtain a posterior distribution over the program’s true, latent specification (indicating user intent), which in turn produces a posterior over possible sketches. As we show experimentally, explicitly modeling uncertainty in specification significantly increases the accuracy of the synthesis algorithm. We evaluate BAYOU’s ability to synthesize Java and Android methods. We find that using just a few example API sequences to communicate user intent, BAYOU can synthesize complex method bodies, some implementing tasks never encountered during training.


Output: Bayesian Sketch Learning for Program Synthesis