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arXiv 2024年

作者： Park, Core Francisco Okawa, Maya Lee, Andrew Tanaka, Hidenori Lubana, Ekdeep Singh CBS-NTT Program in Physics of Intelligence Harvard University United States Department of Physics Harvard University United States Physics & Informatics Laboratories NTT Research Inc. Japan EECS Department University of Michigan Ann Arbor United States

Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model learns, the order in which it learns them, and its ability to manipulate those concepts? To address these questions, we propose analyzing a model’s learning dynamics via a framework we call the concept space, where each axis represents an independent concept underlying the data generating process. By characterizing learning dynamics in this space, we identify how the speed at which a concept is learned, and hence the order of concept learning, is controlled by properties of the data we term concept signal. Further, we observe moments of sudden turns in the direction of a model’s learning dynamics in concept space. Surprisingly, these points precisely correspond to the emergence of hidden capabilities, i.e., where latent interventions show the model possesses the capability to manipulate a concept, but these capabilities cannot yet be elicited via naive input prompting. While our results focus on synthetically defined toy datasets, we hypothesize a general claim on emergence of hidden capabilities may hold: generative models possess latent capabilities that emerge suddenly and consistently during training, though a model might not exhibit these capabilities under naive input prompting. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Training Swinging Door Trending Compression Algorithm for IoT devices

Training Swinging Door Trending Compression Algorithm for Io...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Juan David Arias Correa Alex Sandro Roschildt Pinto Carlos Montez Post-graduate Program of Automation Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device’s resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms.

关键词： Training Performance evaluation Communication systems Data compression Prediction algorithms Market research Internet of Things Computational complexity Compression algorithms

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Review Paper on Bluetooth Based Home Automation Using Android Phone

Review Paper on Bluetooth Based Home Automation Using Androi...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Bhardwaj, Priyanka Roy, Manidipa Srivastava, Shilpa Singh, Sanjay Kumar Gupta, Vidushi Novita, Mega Amirudin, Muhammad Harjanto, Imadudin ABES Engineering College U.P Ghaziabad India Universitas Pgri Semarang Postgraduate Program of Science Education J1. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Electrical Engineering J1. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia

ISBN: (纸本)9798350329186

Electronic devices and appliances have become quintessential part of households with the recent development and integration of a home automation system with numerous client modules, including a remote mobile host controller, as in offices, home appliances etc.. Through Bluetooth, the client modules communicate with the host controller such as an Android smart phone. This paper presents a prototype can be implemented in practice with maximum efficiency keeping the security feature in mind. © 2023 IEEE.

关键词： Bluetooth

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LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection

LiwTERM: A Lightweight Transformer-Based Model for Dermatolo...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Luis A. Souza André G. C. Pacheco Gabriel G. de Angelo Thiago Oliveira-Santos Christoph Palm João P. Papa Graduate Program of Informatics Federal University of Espírito Santo Vitória Brazil OTH Regensburg Regensburg Medical Image Computing (ReMIC) Regensburg Germany Department of Computing São Paulo State Univesity São Paulo Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient's clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient's history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.

关键词： Graphics Computational modeling Computer architecture Transformers Skin Lesions Clinical diagnosis Reliability History Skin cancer

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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COMPETITION DYNAMICS SHAPE ALGORITHMIC PHASES OF IN-CONTEXT LEARNING

arXiv

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arXiv 2024年

作者： Park, Core Francisco Lubana, Ekdeep Singh Pres, Itamar Tanaka, Hidenori CBS-NTT Program in Physics of Intelligence Harvard University United States Department of Physics Harvard University United States Physics and Informatics Lab. NTT Research Inc. EECS Department University of Michigan Ann Arbor United States

In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. This has motivated a series of papers that analyze tractable synthetic domains and postulate precise mechanisms that may underlie ICL. However, the use of relatively distinct setups that often lack a sequence modeling nature to them makes it unclear how general the reported insights from such studies are. Motivated by this, we propose a synthetic sequence modeling task that involves learning to simulate a finite mixture of Markov chains. As we show, models trained on this task reproduce most well-known results on ICL, hence offering a unified setting for studying the concept. Building on this setup, we demonstrate we can explain a model’s behavior by decomposing it into four broad algorithms that combine a fuzzy retrieval vs. inference approach with either unigram or bigram statistics of the context. These algorithms engage in a competition dynamics to dominate model behavior, with the precise experimental conditions dictating which algorithm ends up superseding others: e.g., we find merely varying context size or amount of training yields (at times sharp) transitions between which algorithm dictates the model behavior, revealing a mechanism that explains the transient nature of ICL. In this sense, we argue ICL is best thought of as a mixture of different algorithms, each with its own peculiarities, instead of a monolithic capability. This also implies that making general claims about ICL that hold universally across all settings may be infeasible. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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On the Evaluation of Access-point Handovers for UAVs in Long-distance Missions

On the Evaluation of Access-point Handovers for UAVs in Long...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Fagundes, Leonardo A. Souza, Vitor Barbosa Brandao, Alexandre S. Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa Brazil Federal University of Viçosa Graduate Program on Computer Science Department of Informatics Viçosa Brazil

ISBN: (纸本)9780738131153

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Control Stations (GCS). Due to the complexity and difficulty of performing long distance experiments with these robots, simulation becomes a viable and necessary alternative. However, focusing either on the modeling of UAV operations or communication dynamics, available simulation tools are not able to capture the complex interdependencies between these two aspects, especially in applications where extensive communications are required. The main contribution of this work is the modeling and implementation of strategies for enhancing connectivity of a UAV performing long-distance navigation tasks, taking into account applications' requirements. The proposed strategies aim at assessing the impact of frequent handovers between wireless access points in terms of packet loss and available bandwidth. The first strategy prioritizes maintaining a connection until the signal-to-noise ratio (SNR) observed by the robot reaches a predefined threshold. For this, it is assumed that the data to be transmitted is small enough so that the robot may change its modulation to obtain smaller bandwidths while improving connection time. The second one prioritizes bandwidth availability, thus, it always searches for a network that is able to offer data rates higher than a minimum threshold. A hybrid model is validated by considering the characteristics of each individual strategy. The capabilities of the simulation tool designed for this work are illustrated through some case-study scenarios. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Effect of Tailored Coaching on Physicians’ Electronic Health Record Proficiency and User Experience: A Randomized Crossover Study

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Mayo Clinic Proceedings: Digital Health 2023年第2期1卷 94-104页

作者： Khairat, Saif Chourasia, Prabal Kwong, Elizabeth Choi, Ji Min Seashore, Carl Carolina Health Informatics Program University of North Carolina at Chapel Hill Chapel Hill NC United States School of Nursing University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill NC United States Division of General Pediatrics and Adolescent Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States

Objective: To examine the effect of a tailored EHR coaching intervention, informed by audit log data, on physicians’ EHR proficiency, and user experience among pediatricians. Patients and Methods: A 12-month (August 2020 – August 2021) randomized crossover quality improvement study of a tailored EHR coaching was conducted on 34 pediatric physicians at a major medical center. Participants were randomized into Group AB or Group BA. The intervention was a single 1-hour, one-on-one coaching session. An Epic certified pediatrician tailored each coaching session to meet each physician needs using their EHR audit log data. We analyzed EHR audit log data for 3 months pre- and post-intervention. Results: Out of the 34 physicians, 15 (44%) were primary care pediatricians, 19 (56%) were female, 24 (70%) practiced at the medical center. During the initial intervention, the average proficiency score for the Group AB increased by 8.9% (pre-post difference: 0.37, 95% CI: −0.35 to 1.09;P-value=.381). For the crossover intervention, the average proficiency score for Group BA significantly increased by 11.2% (pre-post difference: 0.31, 95% CI: −0.11 to 0.74;P-value=.05). The average perceived EHR workload decreased post coaching sessions compared to pre-session (50.89 vs 46.66, P-value=.06). Post-coaching intervention, the average score for perceived EHR usability improved compared to pre-intervention (4.10 vs 4.34;P-value=.1). Conclusion: Electronic health record audit log data can be used to inform tailored coaching to improve physicians’ EHR proficiency levels and user experience. Tailored EHR coaching can increase awareness about efficiency tools available in the EHR. These findings are relevant to decisionmakers and learning health systems interested in provider well-being by optimizing EHR use. © 2023 The Authors

关键词： CRT classroom training EHR electronic health record NASA-TLX NASA Task Load Index SD standard deviation USE Usability Satisfaction and Ease of Use

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Towards a Reliable and Lightweight Onboard Fault Detection in Autonomous Unmanned Aerial Vehicles

Towards a Reliable and Lightweight Onboard Fault Detection i...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sai Srinadhu Katta Eduardo Kugler Viegas Secure Systems Research Center (SSRC) Technology Innovation Institute (TII) Abu Dhabi United Arab Emirates Graduate Program in Informatics (PPGIa) Pontifícia Universidade Catolica do Paraná (PUCPR) Brazil

This paper proposes a new model for onboard physical fault detection on autonomous unmanned aerial vehicles (UAV) through machine learning (ML) techniques. The proposal performs the detection task with high accuracies and minimal processing requirements while signaling an unreliable ML model to the operator, implemented in two main phases. First, a wrapper-based feature selection is performed to de-crease the feature extraction computational costs, coped with a classification assessment technique to identify ML model unreliability. Second, physical UAV faults are signaled through a multi-view rationale that evaluates a variety of UAV sensors while triggering alerts based on a sliding window scheme. Experiments performed on a real quadcopter UAV with a broken propeller use case shows the proposal's feasibility. Our model can decrease the false-positive rates up to only 0.4%, while also decreasing the computational costs by at least 43 % when compared to traditional techniques. Notwithstanding, it can identify ML model unreliability, signaling the UAV operator when model fine-tuning is needed.

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