Tagging Efficiency of 99mTc-SC Radiolabeled Alternative Gastric Emptying Meals: A Quantitative Study by Deepak Kumar Pal, Dhananjay Kumar Singh, Satyawati Deswal, Anurag Pathak

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Quantitative Comparison of Age‐Related Development of Oral Functions During Growing Age by Kiichiro Mizokami, Syunnosuke Tohyama, Hiroyuki Kanzaki, Yoji Sasaki, Mao Katayama, Minami Seki, Haruna Rikitake, Syoutaro Ueda, Toshiko Sekiya, Hiroshi Tomonari

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Quantized Feedback Control of Active Suspension Systems Based on Event Trigger by Jinwei Sun, Jingyu Cong, Weihua Zhao, Yonghui Zhang

“…In addition, the trigger mechanism can improve the working efficiency of actuators effectively.…”
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Optimizing binary neural network quantization for fixed pattern noise robustness by Francisco Javier Andreo-Oliver, Gines Domenech-Asensi, Jose Angel Diaz-Madrid, Ramon Ruiz-Merino, Juan Zapata-Perez

“…Abstract This work presents a comprehensive analysis of how extreme data quantization and fixed pattern noise (FPN) from CMOS imagers affect the performance of deep neural networks for image recognition tasks. …”
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Efficient gingival health screening using biofluorescence of anterior dental biofilms by Hyo-Jung Kim, Eun-Song Lee, Baek-Il Kim

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Automated and efficient Bangla signboard detection, text extraction, and novel categorization method for underrepresented languages in smart cities by Tanmoy Mazumder, Fariha Nusrat, Abu Bakar Siddique Mahi, Jolekha Begum Brishty, Rashik Rahman, Tanjina Helaly

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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

“…To solve the problem that that no dataset is available for model quantization in data-sensitive scenarios, a model quantization method without using data sets was proposed.Firstly, according to the parameters of batch normalized layer and the distribution characteristics of image data, the simulated input data was obtained by error minimization method.Then, by studying the characteristics of data rounding, a factor dynamic rounding method based on loss minimization was proposed.Through quantitative experiments on classification models such as GhostNet and target detection models such as M2Det, the effectiveness of the proposed quantification method for image classification and target detection models was verified.The experimental results show that the proposed quantization method can reduce the model size by about 75%, effectively reduce the power loss and improve the computing efficiency while basically maintaining the accuracy of the original model.…”
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Decentralized non-convex online optimization with adaptive momentum estimation and quantized communication by Yunshan Lv, Hailing Xiong, Fuqing Zhang, Shengying Dong, Xiangguang Dai

“…To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. …”
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Convolution Smooth: A Post-Training Quantization Method for Convolutional Neural Networks by Yongyuan Chen, Zhendao Wang

“…Convolutional neural network (CNN) quantization is an efficient model compression technique primarily used for accelerating inference and optimizing resources. …”
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Quantitative Study of Spodumene by Time-of-Flight Secondary Ion Mass Spectrometry (tof-SIMS) by Xijuan Tan

“…In this paper, the quantitative feasibility of time-of-flight secondary ion mass spectrometry (tof-SIMS) for major and minor elements in spodumene was evaluated in terms of calibration method with a matrix-matched or non-matrix-matched standard and an internal standard element using Al or Si. …”
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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. …”
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks by Hang Wei, Zulin Wang, Yuanhan Ni

“…However, limited satellite platform resources present a significant challenge. Post-training quantization (PTQ) provides an efficient method for pre-training neural networks to effectively reduce memory and computational resources without retraining. …”
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NeuBridge: bridging quantized activations and spiking neurons for ANN-SNN conversion by Yuchen Yang, Jingcheng Liu, Chengting Yu, Chengyi Yang, Gaoang Wang, Aili Wang

“…Spiking neural networks (SNNs) offer a promising avenue for energy-efficient computations on neuromorphic hardware, leveraging the unique advantages of spike-based signaling. …”
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Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning by Ce Feng, Parv Venkitasubramaniam

“…Through rigorous theoretical analysis and extensive experiments on benchmark datasets, we demonstrate that these methods significantly enhance the utility-privacy trade-off and computational efficiency in both ML-at-the-edge and FL systems. RQP-SGD is evaluated on MNIST and the Breast Cancer Diagnostic dataset, showing an average 10.62% utility improvement over the deterministic quantization-based projected DP-SGD while maintaining (1.0, 0)-DP. …”
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Enriched HARQ Feedback for Link Adaptation in 6G: Optimizing Uplink Overhead for Enhanced Downlink Spectral Efficiency by Aritra Mazumdar, Abolfazl Amiri, Stefano Paris, Klaus I. Pedersen, Ramoni Adeogun

“…First, our learning-driven adaptive quantization (LAQ) employs a-priori statistics to refine delta MCS quantization within fixed-size UE feedback. …”
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A Novel Approach to Quantitatively Account on Deposition Efficiency by Direct Energy Deposition: Case of Hardfacing-Coated AISI 304 SS by Gabriele Grima, Kamal Sleem, Alberto Santoni, Gianni Virgili, Vincenzo Foti, Marcello Cabibbo, Eleonora Santecchia

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Sociological variety and the transmission efficiency of Mycobacterium tuberculosis: a secondary analysis of qualitative and quantitative data from 15 communities in Zambia by Richard G White, Virginia Bond, Peter J Dodd, Ben Marais, Helen Ayles, Emma J Murray, Kwame Shanaube, Albertus Schaap

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CLINICAL EFFICIENCY OF LEVOCETIRIZINE IN CHILDREN WITH SEASONAL ALLERGIC RHINITIS AND ITS IMPACT ON THE QUANTITATIVE COMPOSITION AND MORPHOLOGICAL INDICATORS OF EOSINOCYTES IN PERIPHERAL BLOOD by E.A. Ruzhitskaya, O.N. Potapova, T.V. Vinogradova, E.E. Varlamov, A.N. Pampura

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FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification by Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Bhavani Sankar Panda, Hardik Doshi, Nafeesh Ahmad, Vivek Bhardwaj, Gaurav Sharma

“…This study proposes a resource efficient, privacy preserving, optimized memory framework by incorporating two approaches: Federated learning and quantized neural network (FL-QNNs) for peripheral blood cell (PBC) image classification. …”
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Dynamic Integration of Shading and Ventilation: Novel Quantitative Insights into Building Performance Optimization by Silvia Mazzetto

“…Buildings consume nearly 40% of global energy, necessitating innovative strategies to balance energy efficiency and occupant comfort. While shading and ventilation are critical to sustainable design, they are often studied independently, leaving gaps in understanding their combined potential. …”
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