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.

Ex Input:
We present a new system S for handling uncertainty in a quantified modal logic (first-order modal logic). The system is based on both probability theory and proof theory. The system is derived from Chisholm's epistemology. We concretize Chisholm's system by grounding his undefined and primitive (i.e. foundational) concept of reasonableness in probability and proof theory. S can be useful in systems that have to interact with humans and provide justifications for their uncertainty. As a demonstration of the system, we apply the system to provide a solution to the lottery paradox. Another advantage of the system is that it can be used to provide uncertainty values for counterfactual statements. Counterfactuals are statements that an agent knows for sure are false. Among other cases, counterfactuals are useful when systems have to explain their actions to users (If I had not done , then would have happened). Uncertainties for counterfactuals fall out naturally from our system. Efficient reasoning in just simple first-order logic is a hard problem. Resolution-based first-order reasoning systems have made significant progress over the last several decades in building systems that have solved non-trivial tasks (even unsolved conjectures in mathematics). We present a sketch of a novel algorithm for reasoning that extends firstorder resolution. Finally, while there have been many systems of uncertainty for propositional logics, first-order logics and propositional modal logics, there has been very little work in building systems of uncertainty for first-order modal logics. The work described below is in progress; and once finished will address this lack.

Ex Output:
Strength Factors: An Uncertainty System for a Quantified Modal Logic

Ex Input:
This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data as commonly encountered in robotics applications and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.

Ex Output:
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

Ex Input:
Deep networks rely on massive amounts of labeled data to learn powerful models. For a target task short of labeled data, transfer learning enables model adaptation from a different source domain. This paper addresses deep transfer learning under a more general scenario that the joint distributions of features and labels may change substantially across domains. Based on the theory of Hilbert space embedding of distributions, a novel joint distribution discrepancy is proposed to directly compare joint distributions across domains, eliminating the need of marginal-conditional factorization. Transfer learning is enabled in deep convolutional networks, where the dataset shifts may linger in multiple task-specific feature layers and the classifier layer. A set of joint adaptation networks are crafted to match the joint distributions of these layers across domains by minimizing the joint distribution discrepancy, which can be trained efficiently using back-propagation. Experiments show that the new approach yields state of the art results on standard domain adaptation datasets.

Ex Output:
Deep Transfer Learning with Joint Adaptation Networks