Ranking-based Deep Cross-modal Hashing

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications

Development of a network-integrated feature-driven engineering environment

Ph.DDOCTOR OF PHILOSOPH

Multi-View Multiple Clusterings using Deep Matrix Factorization

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions

Safety and efficacy of laparoscopic digestive tract nutrition reconstruction combined with conversion therapy for patients with unresectable and obstructive gastric cancer

BackgroundTo explore the safety, efficacy, and survival benefits of laparoscopic digestive tract nutrition reconstruction (LDTNR) combined with conversion therapy in patients with unresectable gastric cancer with obstruction.MethodsThe clinical data of patients with unresectable gastric cancer with obstruction who was treated in Fujian Provincial Hospital from January 2016 to December 2019, were analyzed. LDTNR was performed according to the type and degree of obstruction. All patients received the epirubicin + oxaliplatin + capecitabine regimen as conversion therapy.ResultsThirty-seven patients with unresectable obstructive gastric cancer underwent LDTNR, while thirty-three patients received chemotherapy only. In LDTNR group patients, the proportion of nutritional risks gradually decreased, the rate of severe malnutrition decreased, the proportion of neutrophil-lymphocyte ratio (NLR) <2.5 increased, the proportion of prognosis nutrition index (PNI) ≥45 increased, and the Spitzer QOL Index significantly increased at day 7 and 1 month postoperatively (P<0.05). One patient (6.3%) developed grade III anastomotic leakage and was discharged after the endoscopic intervention. The median chemotherapy cycle of patients in LDTNR group was 6 cycles (2-10 cycles), higher than that in Non-LDTNR group (P<0.001). Among those who received LDTNR therapy, 2 patients had a complete response, 17 had a partial response, 8 had stable disease, and 10 had progressive disease, which was significantly better than the response rate in Non-LDTNR group(P<0.001). The 1-year cumulative survival rates of the patients with or without LDTNR were 59.5% and 9.1%. The 3-year cumulative survival rate with or without LDTNR was 29.7% and 0%, respectively (P<0.001).ConclusionsLDTNR can improve the inflammatory and immune status, increase compliance with chemotherapy, and have potential benefits in improving the safety and effectiveness of and survival after conversion treatment

Reinforcement Causal Structure Learning on Order Graph

Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence(AAAI2023