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3,102 results on '"Zhang, Jian-Guo"'

6. Application of retroauricular bone grinding groove technology in deep brain stimulation

Author

MENG Fan-gang and ZHANG Jian-guo

Subjects
deep brain stimulation, retroauricular bone grinding groove technology (not in mesh), postoperative complications, review, Neurology. Diseases of the nervous system, RC346-429
Abstract

Deep brain stimulation (DBS) has been widely used for Parkinson's disease, essential tremor, dystonia, drug - resistant epilepsy and other diseases. The operation procedure of this surgical method is complicated, and it also has certain complications in clinical application, including surgical complications such as intracranial hemorrhage and infection, hardware complications such as lead fracture and displacement, and stimulation complications such as paresthesia and muscle spasm, in which lead fracture and erosion and infection of the skin behind the ear are common and troublesome complications. In clinical practice, we found that lead fracture and erosion and infection of the skin were mainly located behind the ear. Since 2012, Beijing Tiantan Hospital, Capital Medical University had applied the retroauricular bone grinding groove technology in DBS, which significantly reduced the incidence of lead fracture and erosion and infection of the skin. Therefore, the retroauricular bone grinding groove technology can effectively solve the above problems. This paper mainly introduces the retroauricular bone grinding groove technology and the clinical application attention matters, providing references for colleagues.

Published
2024
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14. HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization

Author

Liu, Ye, Zhang, Jian-Guo, Wan, Yao, Xia, Congying, He, Lifang, and Yu, Philip S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HETFORMER, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HETFORMER achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters., Comment: EMNLP 2021 (short paper)

Published
2021

15. Author Correction: CIS is a potent checkpoint in NK cell–mediated tumor immunity

Author

Delconte, Rebecca B., Kolesnik, Tatiana B., Dagley, Laura F., Rautela, Jai, Shi, Wei, Putz, Eva M., Stannard, Kimberley, Zhang, Jian-Guo, Teh, Charis, Firth, Matt, Ushiki, Takashi, Andoniou, Christopher E., Degli-Esposti, Mariapia A., Sharp, Phillip P., Sanvitale, Caroline E., Infusini, Giuseppe, Liau, Nicholas P. D., Linossi, Edmond M., Burns, Christopher J., Carotta, Sebastian, Gray, Daniel H. D., Seillet, Cyril, Hutchinson, Dana S., Belz, Gabrielle T., Webb, Andrew I., Alexander, Warren S., Li, Shawn S., Bullock, Alex N., Babon, Jeffrey J., Smyth, Mark J., Nicholson, Sandra E., and Huntington, Nicholas D.

Published
2024
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19. Aqueous extract of pulp of maqui-berry (Aristotelia chilensis) induces apoptosis in human endometrial carcinoma ishikawa cells via mitochondrial and endoplasmic reticulum stress pathways

Author

Zhang, Wang-Wei, Liang, Yan-Cui, Thakur, Kiran, Zhang, Jian-Guo, Vidal-San Martin, Carla, Bastias-Montes, Jose M., Neira-Hinojosa, Jose Y., Mah, Siau Hui, Zengin, Gokhan, Saleem, Hammad, El-Haffidi, Mohammed, Avila-Acevedo, Jose G., Alarcon-Enos, Julio, and Cespedes-Acuña, Carlos L.

Published
2024
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37. Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation

Author

Liu, Ye, Wan, Yao, Zhang, Jian-Guo, Zhao, Wenting, and Yu, Philip S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness when comparing with the autoregressive counterparts. In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance. However, these structures are rarely considered in the existing non-autoregressive models. Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer, for the task of neural machine translation. Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies. Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several state-of-the-art non-autoregressive models., Comment: 10 pages, Appear in EACL 2021

Published
2021

38. Comparison of the real‐world effectiveness of vertical versus lateral functional hemispherotomy techniques for pediatric drug‐resistant epilepsy: A post hoc analysis of the HOPS study

Author

Fallah, Aria, Lewis, Evan, Ibrahim, George M, Kola, Olivia, Tseng, Chi‐Hong, Harris, William B, Chen, Jia‐Shu, Lin, Kao‐Min, Cai, Li‐Xin, Liu, Qing‐Zhu, Lin, Jiu‐Luan, Zhou, Wen‐Jing, Mathern, Gary W, Smyth, Matthew D, O'Neill, Brent R, Dudley, Roy WR, Ragheb, John, Bhatia, Sanjiv, Delev, Daniel, Ramantani, Georgia, Zentner, Josef, Wang, Anthony C, Dorfer, Christian, Feucht, Martha, Czech, Thomas, Bollo, Robert J, Issabekov, Galymzhan, Zhu, Hongwei, Connolly, Mary, Steinbok, Paul, Zhang, Jian‐Guo, Zhang, Kai, Hidalgo, Eveline Teresa, Weiner, Howard L, Wong‐Kisiel, Lily, Lapalme‐Remis, Samuel, Tripathi, Manjari, Chandra, Poodipedi Sarat, Hader, Walter, Wang, Feng‐Peng, Yao, Yi, Champagne, Pierre‐Olivier, Brunette‐Clément, Tristan, Guo, Qiang, Li, Shao‐Chun, Budke, Marcelo, Pérez‐Jiménez, Maria Angeles, Raftopoulos, Christian, Finet, Patrice, Michel, Pauline, Schaller, Karl, Stienen, Martin N, Baro, Valentina, Malone, Christian Cantillano, Pociecha, Juan, Chamorro, Noelia, Muro, Valeria L, Lehe, Marec, Vieker, Silvia, Oluigbo, Chima, Gaillard, William D, Al‐Khateeb, Mashael, Otaibi, Faisal Al, Krayenbühl, Niklaus, Bolton, Jeffrey, Pearl, Phillip L, and Weil, Alexander G

Subjects
Neurodegenerative, Clinical Research, Epilepsy, Neurosciences, Brain Disorders, Clinical Trials and Supportive Activities, Child, Drug Resistant Epilepsy, Hemispherectomy, Humans, Pilot Projects, Prospective Studies, Retrospective Studies, Seizures, Treatment Outcome, hemispherectomy, hemispherotomy, seizure outcomes, technique, Clinical Sciences, Neurology & Neurosurgery
Abstract

ObjectiveThis study was undertaken to determine whether the vertical parasagittal approach or the lateral peri-insular/peri-Sylvian approach to hemispheric surgery is the superior technique in achieving long-term seizure freedom.MethodsWe conducted a post hoc subgroup analysis of the HOPS (Hemispheric Surgery Outcome Prediction Scale) study, an international, multicenter, retrospective cohort study that identified predictors of seizure freedom through logistic regression modeling. Only patients undergoing vertical parasagittal, lateral peri-insular/peri-Sylvian, or lateral trans-Sylvian hemispherotomy were included in this post hoc analysis. Differences in seizure freedom rates were assessed using a time-to-event method and calculated using the Kaplan-Meier survival method.ResultsData for 672 participants across 23 centers were collected on the specific hemispherotomy approach. Of these, 72 (10.7%) underwent vertical parasagittal hemispherotomy and 600 (89.3%) underwent lateral peri-insular/peri-Sylvian or trans-Sylvian hemispherotomy. Seizure freedom was obtained in 62.4% (95% confidence interval [CI] = 53.5%-70.2%) of the entire cohort at 10-year follow-up. Seizure freedom was 88.8% (95% CI = 78.9%-94.3%) at 1-year follow-up and persisted at 85.5% (95% CI = 74.7%-92.0%) across 5- and 10-year follow-up in the vertical subgroup. In contrast, seizure freedom decreased from 89.2% (95% CI = 86.3%-91.5%) at 1-year to 72.1% (95% CI = 66.9%-76.7%) at 5-year to 57.2% (95% CI = 46.6%-66.4%) at 10-year follow-up for the lateral subgroup. Log-rank test found that vertical hemispherotomy was associated with durable seizure-free progression compared to the lateral approach (p = .01). Patients undergoing the lateral hemispherotomy technique had a shorter time-to-seizure recurrence (hazard ratio = 2.56, 95% CI = 1.08-6.04, p = .03) and increased seizure recurrence odds (odds ratio = 3.67, 95% CI = 1.05-12.86, p = .04) compared to those undergoing the vertical hemispherotomy technique.SignificanceThis pilot study demonstrated more durable seizure freedom of the vertical technique compared to lateral hemispherotomy techniques. Further studies, such as prospective expertise-based observational studies or a randomized clinical trial, are required to determine whether a vertical approach to hemispheric surgery provides superior long-term seizure outcomes.

Published
2021

39. NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Author

Wan, Yao, He, Yang, Zhang, Jian-Guo, Sui, Yulei, Jin, Hai, Xu, Guandong, Xiong, Caiming, and Yu, Philip S.

Subjects
Computer Science - Software Engineering
Abstract

We present NaturalCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NaturalCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NaturalCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model's performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is available at https://www.youtube.com/watch?v=q4W5VSI-u3E&t=25s.

Published
2020

40. Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Author

Zhang, Jian-Guo, Hashimoto, Kazuma, Liu, Wenhao, Wu, Chien-Sheng, Wan, Yao, Yu, Philip S., Socher, Richard, and Xiong, Caiming

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model., Comment: 19 pages, accepted by EMNLP 2020 main conference as a long paper. Code will be available at https://github.com/salesforce/DNNC-few-shot-intent

Published
2020

43. Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking

Author

Zhang, Jian-Guo, Hashimoto, Kazuma, Wu, Chien-Sheng, Wan, Yao, Yu, Philip S., Socher, Richard, and Xiong, Caiming

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and ontology-free methods. An ontology-based method selects a value from a candidate-value list for each target slot, while an ontology-free method extracts spans from dialog contexts. Recent work introduced a BERT-based model to strike a balance between the two methods by pre-defining categorical and non-categorical slots. However, it is not clear enough which slots are better handled by either of the two slot types, and the way to use the pre-trained model has not been well investigated. In this paper, we propose a simple yet effective dual-strategy model for DST, by adapting a single BERT-style reading comprehension model to jointly handle both the categorical and non-categorical slots. Our experiments on the MultiWOZ datasets show that our method significantly outperforms the BERT-based counterpart, finding that the key is a deep interaction between the domain-slot and context information. When evaluated on noisy (MultiWOZ 2.0) and cleaner (MultiWOZ 2.1) settings, our method performs competitively and robustly across the two different settings. Our method sets the new state of the art in the noisy setting, while performing more robustly than the best model in the cleaner setting. We also conduct a comprehensive error analysis on the dataset, including the effects of the dual strategy for each slot, to facilitate future research., Comment: 14 pages, accepted at the 9th Joint Conference on Lexical and Computational Semantics (*SEM 2020). This version fixes small errors

Published
2019

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