DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks

Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of representation, e.g., by performing the K-means clustering on the last fully-connected layer or by associating some clustering loss to a specific layer, which neglect the possibilities of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In view of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. In particular, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which the ensemble clustering process is further conducted. Specifically, a set of diversified base clusterings are generated from the multi-layer representations via a highly efficient clusterer. Then the reliability of clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the set of base clusterings are re-formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final consensus clustering can be obtained. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.Comment: To appear in IEEE Transactions on Emerging Topics in Computational Intelligenc

Rethinking the Value of Gazetteer in Chinese Named Entity Recognition

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer improves most of the situations that the traditional NER model datasets are difficult to learn. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.Comment: Accepted by NLPCC 202

In Vivo Evaluation of a Biomimetic Polymer-Doxorubicin Conjugate for Cancer Therapy

This poster will describe a novel polymer pro-drug platform designed for conjugation and delivery of chemotherapeutics. Specifically, polymer pro-drugs were prepared from functional polymer zwitterions and doxorubicin (DOX), and evaluated in vivo to assess toxicological, pharmacokinetic and therapeutic properties. The biocompatible polymer scaffold (PolyMPC) consists of zwitterionic phosphorylcholine pendent groups, which mimic the natural hydrophilic moieties of phospholipids in cell membranes, and hydrazone linkages that allow for pH-triggered release of DOX. PolyMPC-DOX pro-drugs were isolated as dry solids using a facile strategy that allows for precise control of molecular weight and DOX incorporation. In vivo toxicity of PolyMPC and PolyMPC-DOX was assessed in a murine model. The maximum tolerated dose of the pro-drug was five times greater than that of free DOX, while PolyMPC alone exhibited no toxicity even at a dose of 800 mg/kg. A pharmacokinetic study in tumor-bearing mice demonstrated a significant increase in circulation half-life of conjugated DOX (t1/2=2 hours) compared to free DOX (t1/2=15 minutes), with conjugated DOX detectable in blood serum for longer than 24 hours. This pronounced enhancement in circulation time was attributed to the macromolecular scaffold, which precludes rapid renal clearance compared to native DOX. Examination of mice given PolyMPC-DOX five days after injection in the PK study showed a three-fold increase of drug accumulated in tumor tissue compared to that of mice treated with free DOX and drug accumulation in off-target organs was reduced for mice given DOX conjugate. The therapeutic efficacy of the PolyMPC-DOX conjugates was then assessed in an orthotopic murine breast cancer model. The treatment group given PolyMPC-DOX exhibited a two-fold increase in overall survival and a significant reduction in average tumor volume compared to the free DOX and saline control groups. A study evaluating the therapeutic efficacy of PolyMPC-DOX in a human ovarian xenograft tumor model is ongoing

Disentanglement Dynamics in Nonequilibrium Environments

We theoretically study the non-Markovian disentanglement dynamics of a two-qubit system coupled to nonequilibrium environments with nonstationary and non-Markovian random telegraph noise statistical properties. The reduced density matrix of the two-qubit system can be expressed as the Kraus representation in terms of the tensor products of the single qubit Kraus operators. We derive the relation between the entanglement and nonlocality of the two-qubit system which are both closely associated with the decoherence function. We identify the threshold values of the decoherence function to ensure the existences of the concurrence and nonlocal quantum correlations for an arbitrary evolution time when the two-qubit system is initially prepared in the composite Bell states and the Werner states, respectively. It is shown that the environmental nonequilibrium feature can suppress the disentanglement dynamics and reduce the entanglement revivals in non-Markovian dynamics regime. In addition, the environmental nonequilibrium feature can enhance the nonlocality of the two-qubit system. Moreover, the entanglement sudden death and rebirth phenomena and the transition between quantum and classical nonlocalities closely depend on the parameters of the initial states and the environmental parameters in nonequilibrium environments

CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity

The recurrent neural networks (RNNs) have shown good performance for sentence similarity modeling in recent years. Most RNNs focus on modeling the hidden states based on the current sentence, while the context information from the other sentence is not well investigated during the hidden state generation. In this paper, we propose a context-aligned RNN (CA-RNN) model, which incorporates the contextual information of the aligned words in a sentence pair for the inner hidden state generation. Specifically, we first perform word alignment detection to identify the aligned words in the two sentences. Then, we present a context alignment gating mechanism and embed it into our model to automatically absorb the aligned words' context for the hidden state update. Experiments on three benchmark datasets, namely TREC-QA and WikiQA for answer selection and MSRP for paraphrase identification, show the great advantages of our proposed model. In particular, we achieve the new state-of-the-art performance on TREC-QA and WikiQA. Furthermore, our model is comparable to if not better than the recent neural network based approaches on MSRP

Quantum State Tomography in Nonequilibrium Environments

We generalize an approach to studying the quantum state tomography (QST) of open systems in terms of the dynamical map in Kraus representation within the framework of dynamic generation of informationally complete positive operator-valued measures. As applications, we use the generalized approach to theoretically study the QST of qubit systems in the presence of nonequilibrium environments which exhibit nonstationary and non-Markovian random telegraph noise statistical properties. We derive the time-dependent measurement operators for the quantum state reconstruction of the single qubit and two-qubit systems in terms of the polarization operator basis. It is shown that the behavior of the time-dependent measurement operators is closely associated with the dynamical map of the qubit systems

Quantum State Tomography in Nonequilibrium Environments

We generalize an approach to studying the quantum state tomography (QST) of open systems in terms of the dynamical map in Kraus representation within the framework of dynamic generation of informationally complete positive operator-valued measures. As applications, we use the generalized approach to theoretically study the QST of qubit systems in the presence of nonequilibrium environments which exhibit nonstationary and non-Markovian random telegraph noise statistical properties. We derive the time-dependent measurement operators for the quantum state reconstruction of the single qubit and two-qubit systems in terms of the polarization operator basis. It is shown that the behavior of the time-dependent measurement operators is closely associated with the dynamical map of the qubit systems