Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 80% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.

Dedicated to the memory of Juan C. Simo for his seminal contributions to solid and computational mechanics Large deformation generalized plasticity is presented in a covariant setting. For this purpose, the tensor analysis on manifolds is utilized and the manifold structure of the body as well of the ambient and the state space is postulated. On the basis of the multiplicative decomposition of the deformation gradient into elastic and plastic parts and the use of hyperelastic stress-strain relations, a large deformation elastoplasticity model is proposed. Computational aspects and the predictions of the model under uniaxial and biaxial straining are also presented.

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the COMPLEXWEBQUESTIONS dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.

Abstract. Face recognition systems detect faces in moving or still images and then recognize them. However, face detection is not an error-free process, especially when designed for real-time systems. Thus the face recognition algorithms have to operate on faces that are not ideally framed. In this paper we analyze quantitatively the impact of face detection errors on six different face recognition algorithms. Hence, we propose a matching of face recognition algorithms with face detector performance, which can be used for a system based on the expected performance of the face detector.

We consider a continuous-space shortest path problem in a two-dimensional plane. This is the problem of finding a trajectory that starts at a given point, ends at the boundary of a compact set of 2 , and minimizes a cost function of the form . We prove the finite termination of these methods, and we present computational results showing that they are competitive and often superior to the Dijkstra-like method.