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36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Luo, Shengjie Li, Shanda Zheng, Shuxin Liu, Tie-Yan Wang, Liwei He, Di National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research United States Center for Data Science Peking University China Zhejiang Lab China

ISBN: (纸本)9781713871088

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to-sequence functions. One may naturally assume the answer is in the affirmative-RPE-based Transformers are universal function approximators. However, we present a negative result by showing there exist continuous sequence-to-sequence functions that RPE-based Transformers cannot approximate no matter how deep and wide the neural network is. One key reason lies in that most RPEs are placed in the softmax attention that always generates a right stochastic matrix. This restricts the network from capturing positional information in the RPEs and limits its capacity. To overcome the problem and make the model more powerful, we first present sufficient conditions for RPE-based Transformers to achieve universal function approximation. With the theoretical guidance, we develop a novel attention module, called Universal RPE-based (URPE) Attention, which satisfies the conditions. Therefore, the corresponding URPE-based Transformers become universal function approximators. Extensive experiments covering typical architectures and tasks demonstrate that our model is parameter-efficient and can achieve superior performance to strong baselines in a wide range of applications. The code will be made publicly available at https://***/lsj2408/URPE. © 2022 Neural information processing systems foundation. All rights reserved.

关键词： Encoding (symbols)

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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An Enchanced Approach for Early Chronic Kidney Disease Prediction Using Advanced machine learning and data Processing Techniques

An Enchanced Approach for Early Chronic Kidney Disease Predi...

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International Conference on Inventive Computation Technologies (ICICT)

作者： S. Poongodi K. Sasirekha Sheela Rini Jeyapriya. S G.Dona Rashmi Department of Data Science PSGR Krishnammal College for Women Coimbatore India Department of Computer Science(Artificial Intelligence and Data Science) Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Deparrtment of Computer Science(PG) PSGR Krishnammal College for Women Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Artificial Intelligence and Machine Learning Kongunadu Arts and Science College Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic renal disease is a medical disorder in which the kidneys gradually slow down, reducing their ability to filter blood. Diabetes, high blood pressure, heart disease, and a family history of renal failure are the primary risk factors for this condition. The prognosis for patients with chronic kidney disease (CKD) varies according to the stage at which they are diagnosed. Stages 0 and 1 represent the early stages of the disease, whereas stage 4 represents the severe stages, where the kidneys are nearly at the point of failure. Early diagnosis is therefore essential for the patient's survival and the appropriate therapies. Blood tests, biopsies, urine tests, and imaging tests are among the medical diagnostic techniques that can be used to diagnose chronic kidney disease (CKD). However, these medical diagnostic techniques have certain drawbacks and restrictions. They are employed to identify diseases in their later stages and can occasionally yield erroneous results; they are also expensive. Consequently, the researcher has discovered a novel technique for the early identification of CKD that use machine learning algorithms to overcome these complications because machine learning algorithms are becoming increasingly significant in health care datasets. These methods also have issues with overfitting, class imbalance, biased results, problems with dataset interpretability, and high computing costs. Therefore, improved preprocessing techniques and machine learning algorithms are used in this research work to overcome the inadequacies of the existing approaches for CKD prediction. The model is tested using the Kaggle dataset on chronic renal disease. Class imbalance and missing attribute values are issues with this dataset. Additionally, this dataset has several categorical attribute values and additional variances in attribute values. A few preprocessing techniques are used to improve the data quality, including K-Nearest Neighbour for filling in missing attribute v

关键词： Adaptation models Accuracy machine learning algorithms Computational modeling machine learning Predictive models Chronic kidney disease Encoding Optimization Diseases

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Intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Jay Patil Suvarna Joshi Rahesha Mulla Kushal Yadav Department of Computer Science and Eng. MIT School of Computing MIT ADT University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Data Science Indian Institute of Technology Madras India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods.

关键词： Weight measurement Accuracy Ultrasonic variables measurement Ergonomics Real-time systems Acoustics Intelligent sensors Monitoring Wearable sensors Testing

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Best Arm Identification under Additive Transfer Bandits 55

Best Arm Identification under Additive Transfer Bandits

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55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021

作者： Neopane, Ojash Ramdas, Aaditya Singh, Aarti Carnegie Mellon University Machine Learning Department PittsburghPA United States Carnegie Mellon University Department of Statistics and Data Science PittsburghPA United States

ISBN: (纸本)9781665458283

We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms. In this paper, we study the setting when, despite the means being unknown, there is a known additive relationship between the source and target MAB instances. We show how our framework covers a range of previously studied pure exploration problems and additionally captures new problems. We propose and theoretically analyze an LUCB-style algorithm to identify an e-optimal target arm with high probability. Our theoretical analysis highlights aspects of this transfer learning problem that do not arise in the typical BAI setup, and yet recover the LUCB algorithm for single domain BAI as a special case. © 2021 IEEE.

关键词： Additives

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On Proximal Policy Optimization's Heavy-tailed Gradients 38

On Proximal Policy Optimization's Heavy-tailed Gradients

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38th International Conference on machine learning, ICML 2021

作者： Garg, Saurabh Zhanson, Joshua Parisotto, Emilio Prasad, Adarsh Zico Kolter, J. Lipton, Zachary C. Balakrishnan, Sivaraman Salakhutdinov, Ruslan Ravikumar, Pradeep Machine Learning Department Carnegie Mellon University United States Computer Science Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

ISBN: (纸本)9781713845065

Modern policy gradient algorithms such as Proximal Policy Optimization (PPO) rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful learning. These heuristics are reminiscent of techniques from robust statistics, commonly used for estimation in outlier-rich ("heavy-tailed") regimes. In this paper, we present a detailed empirical study to characterize the heavy-tailed nature of the gradients of the PPO surrogate reward function. We demonstrate that the gradients, especially for the actor network, exhibit pronounced heavy-tailedness and that it increases as the agent's policy diverges from the behavioral policy (i.e., as the agent goes further off policy). Further examination implicates the likelihood ratios and advantages in the surrogate reward as the main sources of the observed heavy-tailedness. We then highlight issues arising due to the heavy-tailed nature of the gradients. In this light, we study the effects of the standard PPO clipping heuristics, demonstrating that these tricks primarily serve to offset heavy-tailedness in gradients. Thus motivated, we propose incorporating GMOM, a high-dimensional robust estimator, into PPO as a substitute for three clipping tricks. Despite requiring less hyperparameter tuning, our method matches the performance of PPO (with all heuristics enabled) on a battery of MuJoCo continuous control tasks. Copyright © 2021 by the author(s)

关键词： Optimization

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RATT: Leveraging Unlabeled data to Guarantee Generalization 38

RATT: Leveraging Unlabeled Data to Guarantee Generalization

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38th International Conference on machine learning, ICML 2021

作者： Garg, Saurabh Balakrishnan, Sivaraman Zico Kolter, J. Lipton, Zachary C. Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States Computer Science Department Carnegie Mellon University United States

ISBN: (纸本)9781713845065

To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk;or (ii) validate empirically on holdout data. However, (i) typically yields vacuous guarantees for overparameterized models;and (ii) shrinks the training set and its guarantee erodes with each reuse of the holdout set. In this paper, we leverage unlabeled data to produce generalization bounds. After augmenting our (labeled) training set with randomly labeled data, we train in the standard fashion. Whenever classifiers achieve low error on the clean data but high error on the random data, our bound ensures that the true risk is low. We prove that our bound is valid for 0-1 empirical risk minimization and with linear classifiers trained by gradient descent. Our approach is especially useful in conjunction with deep learning due to the early learning phenomenon whereby networks fit true labels before noisy labels but requires one intuitive assumption. Empirically, on canonical computer vision and NLP tasks, our bound provides non-vacuous generalization guarantees that track actual performance closely. This work enables practitioners to certify generalization even when (labeled) holdout data is unavailable and provides insights into the relationship between random label noise and generalization. Copyright © 2021 by the author(s)

关键词： Gradient methods

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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machine-learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces

arXiv

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arXiv 2025年

作者： Delgado, Frederico P. Simões, Frederico Kronik, Leeor Kaiser, Waldemar Egger, David A. Physics Department TUM School of Natural Sciences Technical University of Munich Germany Department of Molecular Chemistry and Materials Science Weizmann Institute of Science Israel Atomistic Modeling Center Munich Data Science Institute Technical University of Munich Germany Munich Center for Machine Learning Munich Germany

The spectacular performance of halide perovskites in optoelectronic devices is rooted in their favorable tolerance to structural defects. Previous studies showed that defects in these materials generate shallow electronic states that do not degrade device performance. However, how these shallow states persist amid the pronounced thermally-stimulated atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of the prototypical CsPbBr3 variant are energetically distributed at room temperature, akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or ≈8kBT from the valence-band edge. Furthermore, we show that even when surface states appear deeper in the gap, they are not energetically isolated and are less likely to act as traps. We achieve this result by accelerating first-principles calculations via machine-learning techniques and show that the unique atomic dynamics in these materials render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep defect states at dynamic halide perovskite surfaces, which is key to their exceptional performance in devices. © 2025, CC BY.

关键词： Perovskite

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Personalised Speech-Based PTSD Prediction Using Weighted-Instance learning 46

Personalised Speech-Based PTSD Prediction Using Weighted-Ins...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Kathan, Alexander Amiriparian, Shahin Triantafyllopoulos, Andreas Gebhard, Alexander Milkus, Sabrina Hohmann, Jonas Muderlak, Pauline Schottdorf, Jürgen Musil, Richard Schuller, Björn W. University of Augsburg Eihw - Embedded Intelligence for Healthcare and Wellbeing Germany Mri Technical Univsersity of Munich Chi - Health Informatics Germany Mcml - Munich Center for Machine Learning Germany University Hospital Lmu Munich Department of Psychiatry and Psychotherapy Germany Zentrumspraxis Friedberg Germany Imperial College London Glam - Group on Language Audio & Music United Kingdom Mdsi - Munich Data Science Institute Germany

ISBN: (纸本)9798350371499

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples' daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls. In addition, the dataset includes data before and after a clinical intervention so that the prediction can be analysed at different points in time. In our experiments, we demonstrate the superiority of the personalised approach, achieving a best area under the ROC curve (AUC) of 82 % and a best relative improvement of 7 % points compared to the non-personalised model. © 2024 IEEE.

关键词： Prediction models

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