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arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

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MetaCity: data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

arXiv

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arXiv 2023年

作者： Cai, Junhui Chen, Ran Wainwright, Martin J. Zhao, Linda Department of Information Technology Analytics and Operations University of Notre Dame United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Laboratory for Information and Decision Systems Statistics and Data Science Center EECS & Mathematics Massachusetts Institute of Technology United States Department of Statistics and Data Science University of Pennsylvania United States

Key challenges in running a retail business include how to select products to present to consumers (the assortment problem), and how to price products (the pricing problem) to maximize revenue or profit. Instead of considering these problems in isolation, we propose a joint approach to assortment-pricing based on contextual bandits. Our model is doubly high-dimensional, in that both context vectors and actions are allowed to take values in high-dimensional spaces. In order to circumvent the curse of dimensionality, we propose a simple yet flexible model that captures the interactions between covariates and actions via a (near) low-rank representation matrix. The resulting class of models is reasonably expressive while remaining interpretable through latent factors, and includes various structured linear bandit and pricing models as particular cases. We propose a computationally tractable procedure that combines an exploration/exploitation protocol with an efficient low-rank matrix estimator, and we prove bounds on its regret. Simulation results show that this method has lower regret than state-of-the-art methods applied to various standard bandit and pricing models. Real-world case studies on the assortment-pricing problem, from an industry-leading instant noodles company to an emerging beauty start-up, underscore the gains achievable using our method. In each case, we show at least three-fold gains in revenue or profit by our bandit method, as well as the interpretability of the latent factor models that are learned. Copyright © 2023, The Authors. All rights reserved.

关键词： Profitability

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

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IEEE International Conference on big data

作者： Avantika Shrestha ML Tlachac Ricardo Flores Kevin Hickey Elke A. Rundensteiner Data Science Department Worcester Polytechnic Institute Worcester MA USA Department of Information Systems and Analytics Center for Health and Behavioral Sciences Bryant University Smithfield RI USA Department of Computer Science University of Concepción Concepción Chile Data Science Department Computer Science Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants – which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding ‘regret’ and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding ‘medical history’. Our findings may help researchers develop more effective mental illness screening models.

关键词： Mental disorders Bidirectional control Human factors Linguistics Multitasking Transformers Depression Encoding Interviews Plugs

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MD-HRNet: A Land use Classification Method Based on Unbalanced Samples

MD-HRNet: A Land use Classification Method Based on Unbalanc...

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International Conference on big data and information analytics (bigDIA)

作者： Huacheng Zhang Yong Shao Li Zhang Bowei Chen Fanquan Kong Bowen Han Jiajia Qiao Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Haikou Marine Geological Survey Center China Geological Survey Haikou China Laboratory for Big Data and Decision Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Land use/land cover classification (LULC) can reveal changes in land use patterns and assess the impact of human activities on ecosystems. This study utilizes Sentinel-2 high-resolution satellite images to classify land use in Haikou City. Due to the complexity of the actual land classes in Haikou City, which leads to sample imbalance in the visually interpreted dataset, this paper proposes an MD-HRNet model to solve the sample imbalance problem and utilizes the combination of multi-scale convolution and frequency domain to extract more information. During the experiment, the land use categories are divided into nine categories, and the experimental results show that the model classification results are better, including 43.82% for mIOU, 53.57% for recall, 55.72% for F1, and 82.99% for accuracy.

关键词： Analytical models Accuracy Urban planning data models Satellite images Reliability Resource management Sustainable development Protection Optimization

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Fine Classification of Industrial and Mining Areas in XuZhou City Using Sentinel-2 Imagery

Fine Classification of Industrial and Mining Areas in XuZhou...

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International Conference on big data and information analytics (bigDIA)

作者： Bowen Han Li Zhang Bowei Chen Fanquan Kong Yong Shao Yuanlin Cai Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Haikou Marine Geological Survey Center China Geological Survey Haikou China Laboratory for Big Data and Decision Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Restoring the ecological function and land value of mining areas is crucial for achieving sustainable development. The spatial distribution of fine classification in mining areas serves as a scientific basis for related policy planning and implementation, providing theoretical support for ecological restoration in these regions. This study focuses on the Chengbei mining area of Xuzhou City as the research area, utilizing Sentinel-2 imagery to establish a classification system for fine classification specific to mining areas. By coupling remote sensing image texture features, remote sensing indices, and topographic parameters, a Random Forest model was constructed to extract fine classification from the mining area and analyze the evolution of their spatial distribution patterns. The experimental results demonstrate that this model significantly enhances the efficiency and accuracy of extracting fine classification in mining areas. Following the restoration of the mining area, there is a noticeable increase in types such as arbor forests and other woodland, resulting in a more stable ecosystem conducive to the sustainable development of fine classification in the mining area.

关键词： Accuracy Graphical models Forests Biological system modeling Urban areas Feature extraction Image restoration Sustainable development Remote sensing Random forests

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Multi-Task Learning Using Facial Features for Mental Health Screening

Multi-Task Learning Using Facial Features for Mental Health ...

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IEEE International Conference on big data

作者： Ricardo Flores Avantika Shrestha Ml Tlachac Elke A. Rundensteiner Data Science Department Worcester Polytechnic Institute Worcester MA USA Department of Information Systems and Analytics Center for Health and Behavioral Sciences Bryant University Smithfield RI USA Data Science Department Computer Science Department Worcester Polytechnic Institute Worcester MA USA

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are prevalent mental health conditions with severe physical and social impacts. They are expensive and detection is difficult, requiring substantial time from trained mental professionals. To alleviate this issue, recent studies explore the diagnostic potential of deep learning models trained on modalities extracted from clinical interview videos, conducted by a virtual agent. However, deep learning models are challenging to train because of the long sequences and small number of participants common in the mental health community. To combat these challenges, we leverage multi-task learning, using temporal facial features as input, to screen for MDD and PTSD. The multi-task architecture is based on a bidirectional GRU model with self-attention. We evaluate our multi-task model on temporal facial features extracted from responses to 15 clinical interview questions conducted by a virtual agent. The results suggest that multi-task learning increases the generalization performance compared to single-task learning. For MDD screening, multi-task learning improved the balanced accuracy over single-task learning for 11 of the 15 datasets. In fact, our multi-learning model increased the MDD screening ability by 25 percent to a balanced accuracy of 0.87 in some scenarios. This work provides valuable findings for the future of mental screening applications leveraging temporal facial features.

关键词：

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CONCENTRATION OF DISCREPANCY-BASED APPROXIMATE BAYESIAN COMPUTATION VIA RADEMACHER COMPLEXITY

arXiv

引用

arXiv 2022年

作者： Legramanti, Sirio Durante, Daniele Alquier, Pierre Department of Economics University of Bergamo Italy Department of Decision Sciences Institute for Data Science and Analytics Bocconi University Italy Department of Information Systems Decision Sciences and Statistics ESSEC Business School France

There has been an increasing interest on summary-free solutions for approximate Bayesian computation (ABC) which replace distances among summaries with discrepancies between the empirical distributions of the observed data and the synthetic samples generated under the proposed parameter values. The success of these strategies has motivated theoretical studies on the limiting properties of the induced posteriors. However, there is still the lack of a theoretical framework for summary-free ABC that (i) is unified, instead of discrepancy-specific, (ii) does not necessarily require to constrain the analysis to data generating processes and statistical models meeting specific regularity conditions, but rather facilitates the derivation of limiting properties that hold uniformly, and (iii) relies on verifiable assumptions that provide more explicit concentration bounds clarifying which factors govern the limiting behavior of the ABC posterior. We address this gap via a novel theoretical framework that introduces the concept of Rademacher complexity in the analysis of the limiting properties for discrepancy-based ABC posteriors, including in non-i.i.d. and misspecified settings. This yields a unified theory that relies on constructive arguments and provides more informative asymptotic results and uniform concentration bounds, even in settings not covered by current studies. These advancements are obtained by relating the asymptotic properties of summary-free ABC posteriors to the behavior of the Rademacher complexity associated with the chosen discrepancy within the family of integral probability semimetrics (IPS). The IPS class extends summary-based distances, and also includes the widely-implemented Wasserstein distance and maximum mean discrepancy (MMD), among others. As clarified in specialized theoretical analyses of popular IPS discrepancies and via illustrative simulations, this new perspective improves the understanding of summary-free ABC. Copyright © 2022, The Au

关键词： Choquet integral

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Deep Learning-Based Flood Detection Using Multi-Temporal Sentinel-1 SAR data: A Case Study in Beijing Area, 2023

Deep Learning-Based Flood Detection Using Multi-Temporal Sen...

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2023 SAR in big data Era, bigSARdata 2023

作者： Zhao, Zibo Zhang, Bo Wu, Fan Yang, Jianxin School of Public Administration China University of Geosciences Department of Land Resource Management Wuhan430074 China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Digital Earth Science Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100049 China

ISBN: (纸本)9798350326239

Floods inflict widespread and severe damage on human lives, property, ecosystems, and economies. Remote sensing technology is crucial in monitoring flood disasters, offering critical information and data to support decision-making, disaster response planning, and resource allocation. Leveraging deep learning techniques for the timely detection and targeted response to disasters in affected areas represents a cost-effective approach to reducing economic losses resulting from flood disasters. This study utilized four Sentinel-1 Synthetic Aperture Radar (SAR) images from July to August 2023 to analyze flood disasters in Beijing and Hebei province. The research introduced a framework for flood detection. It analyzed the disaster situation in urban zones, agricultural land, and reservoir affected by the extraordinary rainfall on July 23rd in Beijing. This study offers valuable insights for effectively reducing disaster-related losses. It is a reference point for future disaster management and mitigation efforts. © 2023 IEEE.

关键词： Synthetic aperture radar

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