Deep Open Intent Classification with Adaptive Decision Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at https://github.com/thuiar/Adaptive-Decision-Boundary.Comment: Accepted by AAAI 2021 (Main Track, Long Paper

Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines.Comment: Accepted by AAAI202

Discovering New Intents with Deep Aligned Clustering

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods. The codes are released at https://github.com/thuiar/DeepAligned-Clustering.Comment: Accepted by AAAI 2021 (Main Track, Long Paper

Radial Angular Momentum Transfer and Magnetic Barrier for Short-Type Gamma-Ray Burst Central Engine Activity

Soft extended emission (EE) following initial hard spikes up to 100 seconds was observed with {\em Swift}/BAT for about half of short-type gamma-ray bursts (SGRBs). This challenges the conversional central engine models of SGRBs, i.e., compact star merger models. In the framework of the black hole-neutron star merger models, we study the roles of the radial angular momentum transfer in the disk and the magnetic barrier around the black hole for the activity of SGRB central engines. We show that the radial angular momentum transfer may significantly prolong the lifetime of the accretion process and multiple episodes may be switched by the magnetic barrier. Our numerical calculations based on the models of the neutrino-dominated accretion flows suggest that the disk mass is critical for producing the observed EE. In case of the mass being $\sim 0.8M_{\odot}$, our model can reproduce the observed timescale and luminosity of both the main and EE episodes in a reasonable parameter set. The predicted luminosity of the EE component is lower than the observed EE with about one order of magnitude and the timescale is shorter than 20 seconds if the disk mass being $\sim 0.2M_{\odot}$. {\em Swift}/BAT-like instruments may be not sensitive enough to detect the EE component in this case. We argue that the EE component would be a probe for merger process and disk formation for compact star mergers.Comment: 9 pages, 3 figures, accepted for publication in Ap

Design of automotive structural components using high strength sheet steels structural strength of cold-formed steel I-beams and hat sections subjected to web crippling load

INTRODUCTION When cold-formed steel beam webs are subjected to partial edge loading, they may fail by web crippling rather than bending of the beam. Web crippling is caused by a highly localized intensity of the load or reaction. Because of the complexity of the web crippling behavior, empirical expressions are presently used for the design of cold-formed steel beams in buildings and automotive structural components to prevent web crippling. 1,2,3. The research on the structural behavior of cold-formed steel beam webs subjected to web crippling has been conducted at Cornell University and the University of Missouri-Rolla (UMR) under the sponsorship of the American Iron and Steel Institute (AISI).4,5 Since 1982, additional work has been performed at the University of Missouri-Rolla, Inland Steel Company, and Ford Motor Company to investigate the web crippling strength of automotive structural components using high strength sheet steels. 6-9 The research findings of the UMR study were summarized in the Eighth Progress Report. 7 In the UMR Fifth and Eighth Progress Reports, it was noted that if the I-beam specimens are subjected to the end one-flange loading without connecting the beam flange to the bearing plate, the failure of all sections used in the pilot tests occured by cross-bending of the flange about the connector location as shown in Fig. 1 instead of the conventional web crippling. This type of failure will be referred to as a flange cross-bending mode of failure in this report. It seems to be dependent primarily on the bend radius, the thickness of the web, the location of connectors and other parameters. Figure 2 shows the failure of an I-beam subjected to end one-flange loading. The tested loads for the specimens having the flange cross-bending type of failure were lower than those caused by the conventional web crippling. The purpose of this brief study reported herein was to review the test results described in Ref. 6 for the flange cross-bending type of failure of cold-formed steel I-beams using high strength sheet steels and to develop some new design criteria, if possible. Because of the limited number of test results, the present investigation can only be treated as a preliminary study of the problem. An extensive experimental work will be needed for the development of general design criteria. Section II contains a review of the experimental research results described in Ref. 6. In Section III, an analytical study of this type of failure mode for cold-formed steel I-beams is presented by using the finite element method. The development of an empirical expression for predicting the ultimate load is discussed in Section IV. Also included in this section is the comparison of test results and predicted values based on the newly developed equations for flange cross-bending failure. In addition to the study of the web crippling strength of I-beams, this report also evaluates the results of 157 beam tests using hat sections. These tests were conducted recently at the Research Laboratories of Inland Steel Company. Section V includes the information on beam specimens 10 and comparisons of the tested and predicted failure loads on the basis of the design recommendations proposed in Ref. 7. Finally, conclusions are drawn in Section VI

3-D neurohistology of transparent tongue in health and injury with optical clearing

Tongue receives extensive innervation to perform taste, sensory, and motor functions. Details of the tongue neuroanatomy and its plasticity in response to injury offer insights to investigate tongue neurophysiology and pathophysiology. However, due to the dispersed nature of the neural network, standard histology cannot provide a global view of the innervation. We prepared transparent mouse tongue by optical clearing to reveal the spatial features of the tongue innervation and its remodeling in injury. Immunostaining of neuronal markers, including PGP9.5 (pan-neuronal marker), calcitonin gene-related peptide (sensory nerves), tyrosine hydroxylase (sympathetic nerves), and vesicular acetylcholine transporter (cholinergic parasympathetic nerves and neuromuscular junctions), was combined with vessel painting and nuclear staining to label the tissue network and architecture. The tongue specimens were immersed in the optical-clearing solution to facilitate photon penetration for 3-dimensiontal (3-D) confocal microscopy. Taking advantage of the transparent tissue, we simultaneously revealed the tongue microstructure and innervation with subcellular-level resolution. 3-D projection of the papillary neurovascular complex and taste bud innervation was used to demonstrate the spatial features of tongue mucosa and the panoramic imaging approach. In the tongue injury induced by 4-nitroquinoline 1-oxide administration in the drinking water, we observed neural tissue remodeling in response to the changes of mucosal and muscular structures. Neural networks and the neuromuscular junctions were both found rearranged at the peri-lesional region, suggesting the nerve-lesion interactions in response to injury. Overall, this new tongue histological approach provides a useful tool for 3-D imaging of neural tissues to better characterize their roles with the mucosal and muscular components in health and disease