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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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EvDiG: Event-guided Direct and Global Components Separation

EvDiG: Event-guided Direct and Global Components Separation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xinyu Zhou Peiqi Duan Boyu Li Chu Zhou Chao Xu Boxin Shi National Key Lab of General AI School of Intelligence Science and Technology Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Separating the direct and global components of a scene aids in shape recovery and basic material understanding. Conventional methods capture multiple frames under high frequency illumination patterns or shadows, requiring the scene to keep stationary during the image acquisition process. Single-frame methods simplify the capture procedure but yield lower-quality separation results. In this paper, we leverage the event camera to facilitate the separation of direct and global components, enabling video-rate separation of high quality. In detail, we adopt an event camera to record rapid illumination changes caused by the shadow of a line occluder sweeping over the scene, and reconstruct the coarse separation results through event accumulation. We then design a network to resolve the noise in the coarse sep-aration results and restore color information. A real-world dataset is collected using a hybrid camera system for network training and evaluation. Experimental results show superior performance over state-of-the-art methods.

关键词： Training computer vision Shape Image color analysis Lighting Cameras Pattern recognition

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Neural Machine Translation with Contrastive Translation Memories

Neural Machine Translation with Contrastive Translation Memo...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Cheng, Xin Gao, Shen Liu, Lemao Zhao, Dongyan Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China Tencent AI Lab School of Computer Science and Technology Shandong University China State Key Laboratory of Media Convergence Production Technology and Systems China China Gaoling School of Artificial Intelligence Renmin University of China China

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories (TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

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Adaptive Multi-Scale Language Reinforcement for Multimodal Named Entity Recognition

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IEEE Transactions on Multimedia 2025年

作者： Li, Enping Li, Tianrui Luo, Huaishao Chu, Jielei Duan, Lixin Lv, Fengmao Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education School of Computing and Artificial Intelligence Chengdu611756 China JD AI Research Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Over the recent years, multimodal named entity recognition has gained increasing attentions due to its wide applications in social media. The key factor of multimodal named entity recognition is to effectively fuse information of different modalities. Existing works mainly focus on reinforcing textual representations by fusing image features via the cross-modal attention mechanism. However, these works are limited in reinforcing the text modality at the token level. As a named entity usually contains several tokens, modeling token-level inter-modal interactions is suboptimal for the multimodal named entity recognition problem. In this work, we propose a multimodal named entity recognition approach dubbed Adaptive Multi-scale Language Reinforcement (AMLR) to implement entity-level language reinforcement. To this end, our model first expands token-level textual representations into multi-scale textual representations which are composed of language units of different lengths. After that, the visual information reinforces the language modality by modeling the cross-modal attention between images and expanded multi-scale textual representations. Unlike existing token-level language reinforcement methods, the word sequences of named entities can be directly interacted with the visual features as a whole, making the modeled cross-modal correlations more reasonable. Although the underlying entity is not given, the training procedure can encourage the relevant image contents to adaptively attend to the appropriate language units, making our approach not rely on the pipeline design. Comprehensive evaluation results on two public Twitter datasets clearly demonstrate the superiority of our proposed model. © 2025 IEEE.

关键词： Modeling languages

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Differentially Private Deep Learning with Iterative Gradient Descent Optimization

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ACM/IMS Transactions on data science 2021年第4期2卷 1–27页

作者： Ding, Xiaofeng Chen, Lin Zhou, Pan Jiang, Wenbin Jin, Hai National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://***/CGCL-codes/***. © 2022 Association for Computing Machinery.

关键词： Large dataset

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CMD: A Cache-Assisted GPU Memory Deduplication Architecture

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IEEE Transactions on computer-aided Design of Integrated Circuits and Systems 2025年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck. We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the intra-dup and inter-dup lines. We design several techniques to manage duplicate blocks, reducing massive off-chip writes. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 42.53%. Besides, CMD can improve the performance of memory-intensive workloads by 57.56%. © 2025 IEEE.

关键词： Cache memory

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Category re-balancing based domain adaptive semantic segmentation 11

Category re-balancing based domain adaptive semantic segment...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Liu, Minghao Wen, Haonan Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Yuquan Road Shijingshan District Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China

The semantic segmentation task faces the bottleneck of high manual annotation costs. Domain adaptive learning provides an effective solution through inter domain knowledge transfer. However, existing domain adaptive semantic segmentation tasks face the situation of category asymmetry between scenes when dealing with complex scenes. Existing methods lack handling of class imbalance in the prediction process. In domain adaptive semantic segmentation tasks, class imbalance limits the performance of inter domain transfer. Our work focuses on the impact of category distribution on domain adaptive semantic segmentation tasks in image pairs. Inspired by long tail learning, we divided categories into head, middle, and tail categories, and designed effective strategies to enhance the contribution of tail categories in domain adaptation tasks. We introduce the concept of category re-balancing into domain adaptive image matching, and our method is lightweight and effective, proving its effectiveness on general datasets. © 2024 The Authors.

关键词： Semantic Segmentation

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Flickr30K-CFQ: A Compact and Fragmented Query dataset for Text-image Retrieval

arXiv

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

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computers, Materials & Continua 2021年第4期67卷 951-964页

作者： Bin Yang Lingyun Xiang Xianyi Chen Wenjing Jia School of Design Jiangnan UniversityWuxi214122China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical and Data Engineering University of Technology SydneySydney2000Australia

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural ***,due to the complexity of the human body,there are still many challenges to face in that *** of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,*** paper presents a novel chronic disease prediction system based on an incremental deep neural *** propensity of users suffering from chronic diseases can continuously be evaluated in an incremental *** time,the system can predict diabetes more and more accurately by processing the feedback *** diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input *** order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was ***’data collected by different sensors were used to train the network *** evaluated our system using a real-world diabetes dataset to confirm its *** experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

关键词： Deep learning incremental learning network architecture design chronic disease prediction

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Disentangling Tabular data Towards Better One-Class Anomaly Detection 39

Disentangling Tabular Data Towards Better One-Class Anomaly ...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Ye, Jianan Tan, Zhaorui Hu, Yijie Yang, Xi Cheng, Guangliang Huang, Kaizhu School of Advanced Technology Xi'an Jiaotong-Liverpool University China School of Electrical Engineering Electronics and Computer Science University of Liverpool United Kingdom Data Science Research Center Duke Kunshan University China

ISBN: (纸本)157735897X

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing “normal” derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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