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Lactose prediction in dry milk with hyperspectral imaging: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2024"

Lactose prediction in dry milk with hyperspectral imaging: A...

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作者： Frizzarin, Maria Caponigro, Vicky Domijan, Katarina Molle, Arnaud Aderinola, Timilehin Nguyen, Thach Le Serramazza, Davide Ifrim, Georgiana Konkolewska, Agnieszka Irish Cattle Breeding Federation Highfield House Bandon Ireland School of Biosystems and Food Engineering Belfield Dublin Ireland Department of Pharmacy University of Salerno Fisciano Italy Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Department of Veterinary Science University of Parma Parma Italy School of Computer Science University College Dublin Belfield Dublin Ireland Insight SFI Research Centre for Data Analytics University College Dublin Belfield Dublin Ireland

In April 2024, the Vistamilk SFI Research Centre organized the fourth edition of the "International Workshop on Spectroscopy and Chemometrics — Spectroscopy meets modern Statistics". Within this event, a data challenge was organized among workshop participants, focusing on hyperspectral imaging (HSI) of milk samples. Milk is a complex emulsion comprising of fats, water, proteins, and carbohydrates. Due to the widespread prevalence of lactose intolerance, precise lactose quantification in milk samples became necessary for the dairy industry. The dataset provided to the participants contained spectral data extracted from HSI, without the spatial information, obtained from 72 samples with reference laboratory values for lactose concentration [mg/mL]. The winning strategy was built using ROCKET, a convolutional-based method that was originally designed for time series classification, which achieved a Pearson correlation of 0.86 and RMSE of 9.8 on the test set. The present paper describes the approaches and statistical methods adopted by all the participants to analyse the data and develop the lactose prediction models. © 2024

关键词： Hyperspectral imaging

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Mixture of Gradient: A Unified Enhancing Approach for Deep Learning-Based Wireless Network Optimization

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IEEE Internet of Things Journal 2025年

作者： Cheng, Nan Ma, Longfei Dai, Yanpeng Wang, Xiucheng Li, Qihao Quan, Wei Liang, Hui Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xidian University State Key Laboratory of ISN and School of Telecommunications Engineering Xi’an710071 China Dalian Maritime University School of Information Science and Technology Dalian116026 China Jilin University College of Communication Engineering Jilin Changchun130021 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Peng Cheng Laboratory Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates. © 2014 IEEE.

关键词： Self-supervised learning

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Downlink and Uplink ISAC in Continuous-Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Zhao, Boqun Ouyang, Chongjun Zhang, Xingqi Shin, Hyundong Liu, Yuanwei Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronics and Information Convergence Engineering Kyung Hee University Gyeonggido Yongin-si17104 Korea Republic of Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

A continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. Within this framework, continuous operator-based signal models are employed to describe the sensing and communication processes. The performance of communication and sensing is analyzed using two information-theoretic metrics: the communication rate (CR) and the sensing rate (SR). 1) For downlink ISAC, three continuous beamforming designs are proposed: i) the communications-centric (C-C) design that maximizes the CR, ii) the sensing-centric (S-C) design that maximizes the SR, and iii) the Pareto-optimal design that characterizes the Pareto boundary of the CR-SR region. A signal subspace-based approach is proposed to derive the closed-form optimal beamformers for the considered designs. On this basis, closed-form expressions are derived for the achievable CRs and SRs, and the downlink rate region achieved by CAPAs is characterized. 2) For uplink ISAC, the C-C and SC successive interference cancellation (SIC)-based methods are proposed to manage inter-functionality interference. Using the subspace approach along with the time-sharing technique, closed-form expressions for the optimal beamformers are derived, and the achievable CRs, SRs, and rate region are analyzed. Numerical results demonstrate that, for both downlink and uplink, CAPA-based ISAC achieves higher CRs and SRs as well as larger CR-SR regions compared to conventional spatially discrete array (SPDA)-based ISAC. Copyright © 2025, The Authors. All rights reserved.

关键词： Beamforming

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Malaysia Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of IT Washington University of Science and Technology (WUST) Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN’s hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model’s potential for accurate real-time air quality forecasting, offering a robust, sustainable solution.

关键词： Accuracy Atmospheric modeling Artificial neural networks Predictive models Air quality Data models Real-time systems Environmental monitoring Forecasting Public healthcare

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Leveraging Machine Learning for Dynamic Resource Allocation in Cloud Automation

Leveraging Machine Learning for Dynamic Resource Allocation ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing Automation Machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

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Development of a Machine Learning-Based Robotic System for Medical-Targeted Drug Delivery System 8th

Development of a Machine Learning-Based Robotic System for M...

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8th National Conference on New Trends in Information and Communications Technology Applications, NTICT 2024

作者： Simango, Doubt Manyanye, Trish Aldhaibani, Jaafar A. Yahya, Abid Mushiri, Tawanda Ndaimani, Augustine Department of Mechatronic Engineering School of Engineering Sciences and Technology Private Bag Off-Chirundu Road Chinhoyi7724 Zimbabwe College of Engineering University of Information Technology and Communication Baghdad Iraq Botswana International University of Science and Technology Private Bag 16 Palapye Botswana Department of Industrial and Mechatronics Engineering Faculty of Engineering and the Built Environment University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe Department of Biomedical Informatics and Biomedical Engineering University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe

ISBN: (纸本)9783031870750

Medical robotics is a field that combines engineering and artificial intelligence to create robotic systems used in healthcare. This study aims to delve into the exciting field of developing medical-targeted drug delivery systems, which hold immense potential for revolutionizing how we administer therapeutic substances. By precisely delivering drugs to specific sites within the body, these innovative systems offer improved treatment outcomes, reduced side effects, and enhanced patient compliance. Therefore, an image processing algorithm for visible target detection was designed, a system to determine the coordinates of the exact location of identified regions of the target was developed, and a trajectory plan for the robotic arm end effector for precise drug delivery was also designed. The integration of robotic systems into MRI scanners, the development of image processing algorithms, and also real-time feedback control systems are mechatronics-based processes. Medical image navigation technology has many uses in minimally invasive robots, mainly image acquisition techniques such as Magnetic Resonance Imaging (MRI) to acquire equivalent image data;however, medical imaging techniques have inefficient visibility in robotic-assisted surgeries of small and confined incisions. Therefore, image processing methods are applied to precisely regulate the operation’s location and visibility and improve the procedure’s efficacy, accuracy, and safety. To develop an advanced medical-targeted drug delivery system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： End effectors

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Gendering Robots in Human-Robot Interaction: An Interdisciplinary Approach 25

Gendering Robots in Human-Robot Interaction: An Interdiscipl...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Neziha Akalin Maria Arnelid Katherine Harrison Dept. of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden Dept. of Thematic Studies Unit of Gender Studies Linköping University Linköping Sweden

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI—specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI.

关键词： Fluids Buildings Human-robot interaction Collaboration Medical services Robots

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Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling down to 10K in Advanced 7nm FinFETs

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IEEE Transactions on computers 2025年

作者： Ravipati, Divya Praneetha van Santen, Victor M. Parihar, Shivendra Singh Chauhan, Yogesh Singh Panda, Preeti Ranjan Amrouch, Hussam Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Germany Kanpur Nanolab Department of Electrical Engineering Uttar Pradesh India

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model Cryo-CACTI for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. © 1968-2012 IEEE.

关键词： Combinatorial circuits

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Image Inversion: A Survey from GANs to Diffusion and Beyond

arXiv

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arXiv 2025年

作者： Chen, Yinan Zhang, Jiangning Bi, Yali Hu, Xiaobin Hu, Teng Xue, Zhucun Yi, Ran Liu, Yong Tai, Ying College of Control Science and Engineering Zhejiang University China YouTu Lab Tencent China College of Computer and Information Science Southwest University China Department of Computer Science & Engineering Shanghai Jiao Tong University China School of Intelligence Science and Technology Nanjing University China

Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration, and style transfer. This paper provides a comprehensive review of the latest advancements in image inversion techniques, focusing on two main paradigms: Generative Adversarial Network (GAN) inversion and diffusion model inversion. We categorize these techniques based on their optimization methods. For GAN inversion, we systematically classify existing methods into encoder-based approaches, latent optimization approaches, and hybrid approaches, analyzing their theoretical foundations, technical innovations, and practical trade-offs. For diffusion model inversion, we explore training-free strategies, fine-tuning methods, and the design of additional trainable modules, highlighting their unique advantages and limitations. Additionally, we discuss several popular downstream applications and emerging applications beyond image tasks, identifying current challenges and future research directions. By synthesizing the latest developments, this paper aims to provide researchers and practitioners with a valuable reference resource, promoting further advancements in the field of image inversion. We keep track of the latest works at https://***/RyanChenYN/ImageInversion. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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