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arXiv 2024年

作者： Johnson, Samuel G.B. Karimi, Amir-Hossein Bengio, Yoshua Chater, Nick Gerstenberg, Tobias Larson, Kate Levine, Sydney Mitchell, Melanie Rahwan, Iyad Schölkopf, Bernhard Grossmann, Igor University of Waterloo Department of Psychology Canada University of Waterloo Department of Electrical and Computer Engineering Canada Université de Montréal Department of Computer Science and Operations Research Canada Warwick Business School Behavioural Science Group United Kingdom Stanford University Department of Psychology United States University of Waterloo Cheriton School of Computer Science Canada Allen Institute for Artificial Intelligence United States Santa Fe Institute United States Max Planck Institute for Human Development Germany Max Planck Institute for Intelligent Systems Germany

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack transparency in their reasoning (explainability), face challenges in communication and commitment (cooperation), and pose risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems—those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive—through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition—the ability to reflect on and regulate one’s thought processes—is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one’s knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values—a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations. © 2024, CC BY.

关键词： Artificial intelligence

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Co-Fix3D: Enhancing 3D Object Detection With Collaborative Refinement

arXiv

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arXiv 2024年

作者： Li, Wenxuan Zou, Qin Chen, Chi Du, Bo Chen, Long Zhou, Jian Yu, Hongkai The School of Computer Science Wuhan University Wuhan430072 China The State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan430079 China The Institute of Automation Chinese Academy of Sciences Beijing130028 China The Department of Electrical and Computer Engineering Cleveland State University ClevelandOH44115 United States

3D object detection in driving scenarios faces the challenge of complex road environments, which can lead to the loss or incompleteness of key features, thereby affecting perception performance. To address this issue, we propose an advanced detection framework called Co-Fix3D. Co-Fix3D integrates Local and Global Enhancement (LGE) modules to refine Bird’s Eye View (BEV) features. The LGE module uses Discrete Wavelet Transform (DWT) for pixel-level local optimization and incorporates an attention mechanism for global optimization. To handle varying detection difficulties, we adopt multi-head LGE modules, enabling each module to focus on targets with different levels of detection complexity, thus further enhancing overall perception capability. Experimental results show that on the nuScenes dataset’s LiDAR benchmark, Co-Fix3D achieves 69.4% mAP and 73.5% NDS, while on the multimodal benchmark, it achieves 72.3% mAP and 74.7% NDS. The source code is publicly available at https://***/rubbish001/Co-Fix3d. © 2024, CC BY.

关键词： Discrete wavelet transforms

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Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

arXiv

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arXiv 2025年

作者： Polking, Mark J. Xu, Haowei Sankar, Raman Grossklaus, Kevin Li, Ju Advanced Materials and Microsystems Group Massachusetts Institute of Technology Lincoln Laboratory 02421 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology 02139 United States Institute of Physics Academia Sinica 11529 Taiwan Department of Electrical and Computer Engineering Tufts University 02155 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 02139 United States

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band (8-12 μm, 0.1-0.15 eV) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials (10 larger than that of HgxCd1-xTe (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen p and metal d band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of Pb0.7Sn0.3Se measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of 1.7-20 μm (0.73-0.062 eV). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range. © 2025, CC BY.

关键词： Single crystals

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CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction

arXiv

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arXiv 2023年

作者： Ekanayake, Mevan Chen, Zhifeng Harandi, Mehrtash Egan, Gary Chen, Zhaolin Monash Biomedical Imaging Monash University Australia Department of Electrical and Computer Systems Engineering Monash University Australia Department of Data Science and AI Monash University Australia School of Psychological Sciences Monash University Australia

In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. However, image quality is a crucial factor that influences the accuracy of clinical diagnosis;hence, high-quality image reconstruction from undersampled measurements has been a key area of research. Recently, deep learning (DL) methods have emerged as the state-of-the-art for MRI reconstruction, typically involving deep neural networks to transform undersampled MRI images into high-quality MRI images through data-driven processes. Nevertheless, there is clear and significant room for improvement in undersampled DL MRI reconstruction to meet the high standards required for clinical diagnosis, in terms of eliminating aliasing artifacts and reducing image noise. In this paper, we introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled DL MRI reconstruction. We use contrastive learning to transform the MRI image representations into a latent space that maximizes mutual information among different undersampled representations and optimizes the information content at the input of the downstream DL reconstruction models. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets, both quantitatively and qualitatively. Furthermore, our extended experiments validate the proposed framework's robustness under adversarial conditions, such as measurement noise, different k-space sampling patterns, and pathological abnormalities, and also prove the transfer learning capabilities on MRI datasets with completely different anatomy. Additionally, we conducted experiments to visualize and analyze the properties of the proposed MRI contrastive learning latent space. Code available here. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Pneumonia Classification using deep learning: a comparative study

Pneumonia Classification using deep learning: a comparative ...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Mohamed Lakhdar Tiar Nadjiba Terki Juan Jose Dominguez-Jimenez IL3CUB Laboratory University of Mohamed Khider Biskra Algeria Electrical Engineering Department University of Mohamed Khider Biskra Algeria Dept. Computer Languages and Systems University of Cadiz Escuela Superior de Ingenieria Cadiz Spain

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders.

关键词： Deep learning Visualization Pneumonia Lung Signal processing algorithms Medical services Signal processing

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Spatial Modulation with Energy Detection: Diversity Analysis and Experimental Evaluation

arXiv

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arXiv 2023年

作者： Faddoul, Elio Kraidy, Ghassan M. Psomas, Constantinos Chatzinotas, Symeon Krikidis, Ioannis The IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Gjøvik Norway University of Luxembourg L-1855 Luxembourg

In this paper, we present a non-coherent energy detection scheme for spatial modulation (SM) systems. In particular, the use of SM is motivated by its low-complexity implementation in comparison to multiple-input multiple-output (MIMO) systems, achieved through the activation of a single antenna during transmission. Moreover, energy detection-based communications restrict the channel state information to the magnitude of the fading gains. This consideration makes the design applicable for low-cost low-powered devices since phase estimation and its associated circuitry are avoided. We derive an energy detection metric for a multi-antenna receiver based on the maximum-likelihood (ML) criterion. By considering a biased pulse amplitude modulation, we develop an analytical framework for the SM symbol error rate at high signal-to-noise ratios. Numerical results show that the diversity order is proportional to half the number of receive antennas;this result stems from having partial receiver channel knowledge. In addition, we compare the performance of the proposed scheme with that of the coherent ML receiver and show that the SM energy detector outperforms its coherent counterpart in certain scenarios, particularly when utilizing non-negative constellations. Ultimately, we implement an SM testbed using software-defined radio devices and provide experimental error rate measurements that validate our theoretical contribution. © 2023, CC BY-NC-SA.

关键词： Signal to noise ratio

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Clinical resistance predictors to first-line VEGFR-TKI monotherapy for metastatic renal cell carcinoma: a retrospective multicenter real-life case series

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Journal of Cancer Metastasis and Treatment 2023年第1期9卷 445-458页

作者： Gaetano Pezzicoli Silvana Quaglini Valentina Tibollo Melissa Bersanelli Camillo Porta Mimma Rizzo Department of Interdisciplinary Medicine University of Bari"Aldo Moro"Bari 70124Italy Department of Electrical Computer and Biomedical EngineeringUniversity of PaviaPavia 27100Italy Laboratory of Informatics and Systems Engineering for Clinical Research Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital of ParmaParma 43121Italy Division of Translational Oncology Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital-Polyclinic of BariBari 70124Italy

Aim:For many years,systemic treatment of metastatic Renal Cell Carcinoma(mRCC)was based on sequential targeted agent *** this real-life case series,we evaluated easily accessible clinical factors useful for disease course ***:We exploited patients'clinical pathological characteristics and systemic treatment outcomes in a real-world population of 365 mRCC patients who received sequential monotherapies in the targeted therapy era,and we identified an early progressors subpopulation,resistant to first-line VEGFR-TKI monotherapy in less than 6 ***:Early progressors(n=124)show a far worse OS compared with patients progressing beyond the sixth month of therapy(13.5 vs.44.8 months,P-value<0.0001,HR=0.41,95%CI:0.29-0.53).However,these patients did not show far worse performance in second and third-line settings compared to first-line *** the univariate analysis,IMDC risk class,sarcomatoid features,and Systemic Inflammation Index(SII)were correlated with first-line therapy Progression-Free Survival(PFS1).In multivariate analysis,variables correlated with first-line outcome were IMDC risk class,histotype,and number of metastatic sites at the ***:Real-world data can contribute to developing easy-to-use prognostic factors associated with refractory disease that could support clinicians in identifying the most appropriate treatment strategy for each patient.

关键词： Renal cell carcinoma targeted therapy tyrosine kinase inhibitor drug resistance early progressor rapidly progressive prognostic factor real-world data

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An Investigation Method Incorporates with Contextual Information and Transformer Residual BiLSTM to Improve the Accuracy of Detecting Sensitive Content in Various Contexts

An Investigation Method Incorporates with Contextual Informa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Department of Electrical Engineering Masters in Electrical Engineering Sr. Principal Systems Engineer in C&RS Platform Services DT Amtrak University of South Alabama USA Senior Software Engineer in Cloud Platform Services City Of Philadelphia Pennsylvania USA Vice President US AL. Department of Computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Comcast Troy University Montgomery Campus USA Department of Information Technology GEICO - Chevy Chase MD United States

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the modern era, the internet is an emerging technology with a rapidly growing user. Sensitive content uploaders consider this platform as a way to transmit disturbing visual content, such as animated cartoon videos to circulate unsuitable content among youngsters. Previous researches show that the interaction of online sensitive information has substantial offline effects on historically impoverished communities. As a result, it is strongly recommended that social media platforms have an automatic real-time sensitive content filtering method for detecting contextual information. In this proposed model, a novel deep-learning framework is developed for detecting sensitive content from contextual information. For this, the suggested architecture performs under three stages (i) text collection, (ii) preprocessing, and (iii) detection. Initially, the required texts from the contextual information are gathered from the standard available data resource. The collected text is subjected to text preprocessing, where the text is cleaned and prepared for further analysis. Finally, in the detection phase, the developed Transformer Residual Bidirectional Long Short-Term Memory (TR-BiLSTM) is employed to learn the effective representation from the preprocessed text and perform the sensitive content detection. Furthermore, performance comparisons are carried out between the proposed frameworks with other standard approaches. The developed architecture achieved better results in detecting the sensitive content from the text.

关键词： Visualization Accuracy Recurrent neural networks Noise Transformers Real-time systems Long short term memory Standards Context modeling Videos

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A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

A Sample-Based Algorithm for Approximately Testing r-Robustn...

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IEEE Conference on Decision and Control

作者： Yuhao Yi Yuan Wang Xingkang He Stacy Patterson Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Electrical Engineering University of Notre Dame Notre Dame IN USA Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0

关键词： Network topology Approximation algorithms Robustness Testing

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Biomimetic Frontend for Differentiable Audio Processing

arXiv

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arXiv 2024年

作者： Famularo, Ruolan Leslie Zotkin, Dmitry N. Shamma, Shihab A. Duraiswami, Ramani Perceptual Interfaces and Reality Lab. Computer Science & UMIACS University of Maryland College Park United States Department of Linguistics University of Maryland College Park United States Program in Neuroscience & Cognitive Science University of Maryland College Park United States Institute of Systems Research & Electrical and Computer Engineering University of Maryland College Park United States

While models in audio and speech processing are becoming deeper and more end-to-end, they as a consequence need expensive training on large data, and are often brittle. We build on a classical model of human hearing and make it differentiable, so that we can combine traditional explainable biomimetic signal processing approaches with deep-learning frameworks. This allows us to arrive at an expressive and explainable model that is easily trained on modest amounts of data. We apply this model to audio processing tasks, including classification and enhancement. Results show that our differentiable model surpasses black-box approaches in terms of computational efficiency and robustness, even with little training data. We also discuss other potential applications. © 2024, CC BY-NC-ND.

关键词： Deep learning

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