Structured Attention for Unsupervised Dialogue Structure Induction

Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation.Comment: Long paper accepted by EMNLP 202

On the Bethe states of the one-dimensional supersymmetric t-J model with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the supersymmetric t-J model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe states which have well-defined homogeneous limit. This exact solution provides basis for further analyzing the thermodynamic properties and correlation functions of the model.Comment: 17 pages, 2 tables, published versio

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing

Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure

Hydrodynamics around a deep-draft-semi-submersible with biomimetic tubercle corner design

Leading-edge tubercles have been investigating widely on the performance of foils in the last decade. In this study, the biomimetic tubercle design has been applied to the corner shape on a deep-draft semi-submersible. A numerical study on flow over a deep-draft semi-submersible (DDS) with a biomimetic tubercle corner shape was carried out to investigate the corner shape effects on the overall hydrodynamics and motion responses. The hydrodynamic performance of the biomimetic tubercle corner is compared with a traditional round corner design platform. It is demonstrated that, as the corner shape design changed, the motion responses alter drastically. In addition, the flow patterns were examined to reveal some insights into fluid physics due to the biomimetic tubercle corner design. The comprehensive numerical results showed that the three-dimensional effect, which causes spanwise flow, can be reduced by a continuous spanwise (column-wise) variation of the shear-layer separation points

Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-scale datasets extremely time-consuming and labor-intensive. Training the multi-label image recognition models with partial labels (MLR-PL) is an alternative way, in which merely some labels are known while others are unknown for each image. However, current MLP-PL algorithms rely on pre-trained image similarity models or iteratively updating the image classification models to generate pseudo labels for the unknown labels. Thus, they depend on a certain amount of annotations and inevitably suffer from obvious performance drops, especially when the known label proportion is low. To address this dilemma, we propose a dual-perspective semantic-aware representation blending (DSRB) that blends multi-granularity category-specific semantic representation across different images, from instance and prototype perspective respectively, to transfer information of known labels to complement unknown labels. Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels. Meanwhile, a prototype-perspective representation blending (PPRB) module is introduced to learn more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels, in a location-sensitive manner, to complement these unknown labels. Extensive experiments on the MS-COCO, Visual Genome, and Pascal VOC 2007 datasets show that the proposed DSRB consistently outperforms current state-of-the-art algorithms on all known label proportion settings.Comment: Technical Report. arXiv admin note: text overlap with arXiv:2203.0217

Mirror symmetry decomposition in double-twisted multilayer graphene systems

Due to the observed superconductivity, the alternating twisted trilayer graphene (ATTLG) has drawn great research interest very recently, in which three monolayer graphene (MLG) are stacked in alternating twist way. If one or several of the MLG in ATTLG are replaced by a multilayer graphene, we get a double twisted multilayer graphene (DTMLG). In this work, we theoretically illustrate that, if the DTMLG has a mirror symmetry along z direction like the ATTLG, there exists a mirror symmetry decomposition (MSD), by which the DTMLG can be exactly decoupled into two subsystems with opposite parity. The two subsystems are either a twisted multilayer graphene (single twist) or a multilayer graphene, depending on the stacking configuration. Such MSD can give a clear interpretation about all the novel features of the moir\'{e} band structures of DTMLG, e.g. the fourfold degenerate flat bands and the enlarged magic angle. Meanwhile, in such DTMLG, the parity becomes a new degree of freedom of the electrons, so that we can define a parity resolved Chern number for the moir\'{e} flat bands. More importantly, the MSD implies that all the novel correlated phases in the twisted multilayer graphene should also exist in the corresponding DTMLGs, since they have the exact same Hamiltonian in form. Specifically, according to the MSD, we predict that the superconductivity should exist in the (1+3+1)-DTMLG.Comment: 12 pages, 6 figure