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Making the most of ai and machine learning in organizations and strategy research: Supervised machine learning, causal inference, and matching models

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STRATEGIC MANAGEMENT JOURNAL 2024年第10期45卷 1926-1953页

作者： Rathje, Jason Katila, Riitta Reineke, Philipp Dept Def Arlington VA USA Stanford Univ Stanford CA 94305 USA

We spotlight the use of machine learning in two-stage matching models to deal with sample selection bias. Recent advances in machine learning have unlocked new empirical possibilities for inductive theorizing. In contrast, the opportunities to use machine learning in regression studies involving large-scale data with many covariates and a causal claim are still less well understood. Our core contribution is to guide researchers in the use of machine learning approaches to choosing matching variables for enhanced causal inference in propensity score matching models. We use an analysis of real-world technology invention data of public-private relationships to demonstrate the method and find that machine learning can provide an alternative approach to ad hoc matching. However, as with any method, it is also important to understand its limitations. This article explores the use of machine learning to enhance decision-making, particularly in addressing sample selection bias in large-scale datasets. The rapid development of ai and machine learning offers new, powerful tools especially for digital ecosystems where complex data and causal relationships are complex to analyze. We offer managers and stakeholders insight into the effective integration of machine learning for selecting critical variables in propensity score matching models. Through a detailed examination of real-world data on technology inventions within public-private relationships, we demonstrate the effectiveness of machine learning as a robust alternative to traditional matching methods.

关键词： ai and machine learning government regulation and public-private relationship patents propensity score matching technological change and types of innovation

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Deep active-learning based model-synchronization of digital manufacturing stations using human-in-the-loop simulation

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JOURNAL OF MANUFACTURING SYSTEMS 2023年 70卷 436-450页

作者： Steed, Clint Alex Kim, Namhun Ulsan Natl Inst Sci & Technol UNIST Dept Mech Engn Ulsan 44919 South Korea Stellenbosch Univ Dept Ind Engn ZA-7600 Stellenbosch South Africa

The effective and accurate modeling of human performance is one of the key technologies in virtual/smart manufacturing systems. However, a significant challenge lies in acquiring sufficient data for such modeling. Virtual Reality (VR) emerges as a promising solution, making human manufacturing experiments more practical and accessible. In this paper, we present a novel framework that efficiently models human assembly duration by leveraging VR to prototype data-acquisition systems for assembly tasks. Central to the framework is an active learning model, which intelligently selects experimental conditions to yield the most informative results, effectively reducing the number of experiments required. As a result, the system demands fewer experimental trials and operates on an automated basis. In VR experiments involving throughput rate, the active model significantly reduces the data requirement, thereby expediting the experiment and modeling process. While this framework demonstrates remarkable efficiency, it does exhibit sensitivity to non-constant noise and may necessitate prior data from similar assembly tasks to identify high-noise. Notably, this proposed method extends beyond manufacturing, allowing the quick generation of human performance models in virtual systems and enhancing experiment scalability across various fields. With its potential to revolutionize human performance modeling, our framework represents a promising avenue for advancing virtual/smart manufacturing systems and other related applications.

关键词： Human-centric manufacturing Digital twin Virtual reality (VR) ai and machine learning Virtual manufacturing Digital transformation

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ICN: Interactive convolutional network for forecasting travel demand of shared micromobility

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GEOINFORMATICA 2025年第2期29卷 175-200页

作者： Xu, Yiming Ke, Qian Zhang, Xiaojian Zhao, Xilei Univ Florida Dept Civil & Coastal Engn 1949 Stadium Rd Gainesville FL 32611 USA Univ Texas Austin Sch Architecture 310 Inner Campus Dr B7500 Austin TX 78712 USA Bloomberg 731 Lexington Ave New York NY 10022 USA

Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning methods provide robust mechanisms to tackle demand forecasting challenges, current models based on graph neural networks suffer from limited scalability and high computational cost. There is both a need and significant potential to enhance the accuracy and efficiency of existing shared micromobility demand forecasting models. To fill these research gaps, this paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility. The proposed model develops a novel channel dilation method by utilizing multi-dimensional spatial information (i.e., demographics, functionality, and transportation supply) based on travel behavior knowledge for building the deep learning model. We use the convolution operation to process the dilated tensor to simultaneously capture temporal and spatial dependencies. Based on a binary-tree-structured architecture and interactive convolution, the ICN model extracts features at different temporal resolutions and then generates predictions using a fully-connected layer. We conducted two practical case studies from Chicago, IL, and Austin, TX to test the proposed model. The results show that the ICN model significantly outperforms all benchmark models. The model predictions have the potential to assist micromobility operators in developing efficient vehicle rebalancing strategies, while also providing cities with guidance on enhancing the management of their shared micromobility system.

关键词： Micromobility Convolutional network Deep learning Travel demand forecasting ai and machine learning

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learning-based essential matrix estimation for visual localization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第3期9卷 1097-1106页

作者： Son, Moongu Ko, Kwanghee Gwangju Inst Sci & Technol Sch Mech Engn Cheomdangwagi Ro 123 Gwangju 61005 South Korea

Visual localization is defined as finding the camera pose from two-dimensional images, which is a core technique in many computer vision tasks, including robot navigation, autonomous driving, augmented/mixed/virtual reality, mapping, etc. In this study, we address the pose estimation problem from a single-color image using a neural network. We propose a coarse-to-fine approach based on a deep learning framework, which consists of two steps: direct regression-based coarse pose estimation that obtains a pose by finding a pose-based similar image retrieval and Siamese network-based essential matrix estimation to obtain a refined pose. Experimental results using the 7-scenes, Cambridge, and RobotCar datasets demonstrate that the proposed method performs better than the existing methods in terms of accuracy and stability.

关键词： visual localization 6-DoF pose estimation ai and machine learning

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Shared Autonomy Locomotion Synthesis With a Virtual Powered Prosthetic Ankle

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 4738-4748页

作者： Hodossy, Balint K. Farina, Dario Imperial Coll London Dept Bioengn London SW7 2AZ England

Virtual environments provide a safe and accessible way to test innovative technologies for controlling wearable robotic devices. However, to simulate devices that support walking, such as powered prosthetic legs, it is not enough to model the hardware without its user. Predictive locomotion synthesizers can generate the movements of a virtual user, with whom the simulated device can be trained or evaluated. We implemented a Deep Reinforcement learning based motion controller in the MuJoCo physics engine, where autonomy over the humanoid model was shared between the simulated user and the control policy of an active prosthesis. Despite not optimising the controller to match experimental dynamics, realistic torque profiles and ground reaction force curves were produced by the agent. A data-driven and continuous representation of user intent was used to simulate a Human machine Interface that controlled a transtibial prosthesis in a non-steady state walking setting. The continuous intent representation was shown to mitigate the need for compensatory gait patterns from their virtual users and halve the rate of tripping. Co-adaptation was identified as a potential challenge for training human-in-the-loop prosthesis control policies. The proposed framework outlines a way to explore the complex design space of robot-assisted gait, promoting the transfer of the next generation of intent driven controllers from the lab to real-life scenarios.

关键词： ai and machine learning human-robot interaction locomotion synthesis simulation rehabilitation robotics

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Marker-Based Localization System Using an Active PTZ Camera and CNN-Based Ellipse Detection

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第4期28卷 1984-1992页

作者： Oh, Xueyan Lim, Ryan Foong, Shaohui Tan, U-Xuan Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore 487372 Singapore

Localization in GPS-denied environments is challenging, and many existing solutions have infrastructural and on-site calibration requirements. This article tackles these challenges by proposing a localization system that is infrastructure free and does not require on-site calibration, using a single active pan-tilt-zoom camera to detect, track, and localize a circular LED marker. We propose to use a convolutional neural network (CNN) trained using only synthetic images to detect the LED marker as an ellipse and show that our approach is more robust than using traditional ellipse detection without requiring tuning of parameters for feature extraction. We also propose to leverage the predicted elliptical angle as a measure of uncertainty of the CNN's predictions and show how it can be used in a filter to improve marker range estimation and 3-D localization. We evaluate our system's performance through localization of a unmanned aerial vehicle in real-world flight experiments and show that it can outperform alternative methods for localization in GPS-denied environments. We also demonstrate our system's performance in indoor and outdoor environments.

关键词： ai and machine learning localization

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Conquering PROTAC molecular design and drugability

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BIOANALYSIS 2025年第7期17卷 455-470页

作者： Bhole, Ritesh P. Labhade, Sonali Gurav, Shilendra S. Dr DY Patil Inst Pharmaceut Sci & Res Pune India Dr DY Patil Vidyapeeth Dr DY Patil Dent Coll Hosp & Res Ctr Pune India Goa Univ Goa Coll Pharm Dept Pharmacognosy Panaji 403001 Goa India

PROTACs are reshaping drug discovery by enabling targeted protein degradation, overcoming the limitations of traditional inhibitors, and addressing previously "undruggable" proteins. The present perspective explores advancements in PROTAC molecular design, focusing on ligand discovery, E3 ligase recruitment, and ternary complex optimization. Integrating ai-driven modeling, FBDD, and SBDD accelerates PROTAC development. In contrast, emerging innovations, such as PHOTACs, hypoxia-responsive systems, and Ab-PROTACs, enhance precision and reduce systemic toxicity. Clinical successes, including ARV-110 for castration-resistant prostate cancer and ARV-471 for breast cancer, exemplify their ability to overcome resistance and provide durable effects. PROTACs are expanding into neurodegenerative diseases and rare conditions, highlighting their versatility. By addressing challenges in pharmacokinetics, safety, and scalability, PROTACs are poised to revolutionize precision medicine. This article presents a forward-looking perspective on conquering the molecular design and drugability of PROTACs, paving the path for transformative therapies.

关键词： Precision medicine therapeutic innovation ligand discovery targeted and combination therapy E3 ligase recruitment immunotherapy ai and machine learning targeted protein degradation

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Reinforcement learning Grasping With Force Feedback From Modeling of Compliant Fingers

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年

作者： Beddow, Luke Wurdemann, Helge Kanoulas, Dimitrios UCL Dept Comp Sci & Mech Engn London England

Current reinforcement learning approaches for grasping do not consider force feedback combined with compliant grippers, but these features are suited to grocery object grasping, where objects can be bruised by high forces, and exhibit varied weights, textures, and surface irregularities. This work combines force feedback with reinforcement learning on a compliant gripper. We design a three degrees of freedom caging inspired gripper, which can grasp by trapping objects with three compliant fingers and a movable palm. We instrument the fingers with strain gauges for force sensing, then, model their bending for simulating grasping at 16.8x real time. Then, we train a reinforcement learning grasping controller based on in-grasp force feedback, with real world transfer achieving 98.0% grasp success rate on training objects, with an average sim2real gap of 3.1%. We demonstrate generalization to 42 novel grocery objects with a success rate of 95.0%, with 80.1% of grasps tolerating a 5 N vertical disturbance. In-grasp finger forces averaged 1.4 and palm forces 3.0 N. We also validate our method with three finger rigidities, show that our model and in-grasp sensing improve learning and performance, and compare against three baselines.

关键词： ai and machine learning design grasping and manipulation mechanisms modeling and control ai and machine learning design grasping and manipulation mechanisms modeling and control

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Real-Time Accurate Determination of Table Tennis Ball and Evaluation of Player Stroke Effectiveness with Computer Vision-Based Deep learning

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APPLIED SCIENCES-BASEL 2025年第10期15卷 5370-5370页

作者： He, Zilin Yang, Zeyi Xu, Jiarui Chen, Hongyu Li, Xuanfeng Wang, Anzhe Yang, Jiayi Chow, Gary Chi-Ching Chen, Xihan Educ Univ Hong Kong Dept Hlth & Phys Educ Hong Kong 999077 Peoples R China Southern Univ Sci & Technol Ctr Phys Educ Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Engn Shenzhen 518055 Peoples R China

The adoption of artificial intelligence (ai) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time performance analysis. The system integrates YOLOv5 for high-precision ball detection (98% accuracy) and MediaPipe for athlete posture evaluation. A dynamic time-wrapping algorithm further assesses stroke effectiveness, demonstrating statistically significant discrimination between beginner and intermediate players (p = 0.004 and Cohen's d = 0.86) in a cohort of 50 participants. By automating feedback and reducing reliance on expert observation, this system offers a scalable tool for coaching, self-training, and sports analysis. Its modular design also allows adaptation to other racket sports, highlighting broader utility in athletic training and entertainment applications.

关键词： technology in sports ai and machine learning table tennis training dynamic time wrapping assessment

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Investigating the Role of ChatGPT in Supporting Text-Based Programming Education for Students and Teachers 16th

Investigating the Role of ChatGPT in Supporting Text-Based P...

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16th International Conference on Informatics in Schools - Situation, Evolution, and Perspectives (ISSEP)

作者： Wieser, Markus Schoffmann, Klaus Stefanics, Daniela Bollin, Andreas Pasterk, Stefan Univ Klagenfurt Inst Informat Didact Klagenfurt am Worthersee Austria Univ Klagenfurt Inst Informat Technol Klagenfurt am Worthersee Austria

ISBN: (纸本)9783031448997;9783031449000

Teaching text-based programming poses significant challenges in both school and university contexts. This study explores the potential of ChatGPT as a sustainable didactic tool to support students, freshmen, and teachers. By focusing on a beginner's course with examples also relevant to vocational schools, we investigated three research questions. First, the extent to which ChatGPT assists students in solving and understanding initial examples;secondly, the feasibility of teachers utilizing the chatbot for grading student solutions;and finally, the additional support ChatGPT provides in terms of teaching. Our findings demonstrate that ChatGPT offers valuable guidance for teachers in terms of assessment and grading and aids students in understanding and optimizing their solutions.

关键词： ai and machine learning Pedagogy/Teaching Approach Programming Education Higher-Secondary and Vocational Schools

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