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7th Information Systems International Conference, ISICO 2023

作者： Afia, Nur Purnomo, Agung Maulana, Fairuz Iqbal Prasetyo, Yogi Tri Gumasing, Ma Janice J. Rosyidah, Elsa Persada, Satria Fadil Institute for Research and Community Services Universitas Nahdlatul Ulama Sidoarjo Sidoarjo61234 Indonesia English Language Education Universitas Nahdlatul Ulama Sidoarjo Sidoarjo61234 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila1002 Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan32003 Taiwan

Patent intellectual property in social informatics with entrepreneurship support creates greater opportunities to improve human life. It has the potential to open enormous innovation opportunities in various fields, such as technology, health, services, and society. This research was conducted to review the patent landscape and the main trends of patents on social informatics in all countries. Researchers applied a patent landscape analysis using data on 487 patent documents over 16 years from the *** database. The study results show yearly patented development patterns related to social informatics. There is a royalty-free opportunity by using 15 patent discontinued. Patent related to social informatics was dominated by company ownership and inventors from the United States. The patent physics (G) section overwhelmed patents related to social informatics. There were 334 simple family patents on social informatics in nine jurisdictions;the United States was the highest. © 2023 The Authors. Published by Elsevier B.V.

关键词： Patents and inventions

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Design and Optimization of the Porous Metamaterial EM Absorbers in X-band

Design and Optimization of the Porous Metamaterial EM Absorb...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Kanat Anurakparadorn Alan Taub Eric Michielssen Macromolecular Science and Engineering Program Department of Material Science and Engineering University of Michigan Ann Arbor Michigan USA Department of Mechanical Engineering University of Michigan Ann Arbor Michigan USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

A strategy that combines experiment and simulation to design and optimize electromagnetic (EM) metamaterial absorbers containing a periodic porous structure is described. The approach provides the ability to produce absorbers that meet multiple user-specified objectives. Using the measured intrinsic properties of the baseline materials as an input to EM-field based computational modelling and optimization, absorption by the studied metamaterials measured by their reflection loss (RL) increases significantly. The resulting metamaterials have the potential for lower cost and lighter weight while providing greater protection than traditional metal gaskets and foams.

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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve

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IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 286-298页

作者： Lin, Yuan-Jin Chen, Shih-Lun Mao, Yi-Cheng Chen, Tsung-Yi Peng, Cheng-Hao Tsai, Tzu-Hsiang Li, Kuo-Chen Chen, Chiung-An Tu, Wei-Chen Abu, Patricia Angela R. National Cheng Kung University Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan City701401 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City32023 Taiwan Taoyuan Chang Gang Memorial Hospital Department of Operative Dentistry Taoyuan City33305 Taiwan Feng Chia University Department of Electronic Engineering Taichung city40724 Taiwan Chung Yuan Christian University Taoyuan City32023 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City32023 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243303 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan city701401 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Objective: Extraction of the third molar of the mandible is one of the most common oral surgical procedures. Preoperative monitoring and assessment are crucial to mitigate neurological risks. Identifying whether the third molar in the mandible compresses the inferior alveolar nerve still relies on dental professionals, a task that is repetitive and time-consuming. Thus, the primary objective is to utilize dental panoramic radiography for image processing and classify whether the third molar compresses the inferior alveolar nerve, aiming to reduce the demand for CT images in symptom diagnosis and mitigate the risks associated with high-dose radiation. Technology or Method: This study proposes an innovative dental panoramic radiography segmentation technique to locate the third molar position. Subsequently, an innovative edge masking enhancement method is used to extract features of the inferior alveolar nerve and the third molar. Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. Results: The third molar position localization method achieved an accuracy rate of 97.92%, compared to recent research at least improved by 3.6% accuracy. Subsequently, innovative edge masking and image enhancement methods improve classification accuracy by 4.3%, when supplemented with computed tomography scan images for further evaluation, the maximum accuracy reached 98.45%, representing a 4.5% improvement compared to previous studies. Conclusions: The third molar position detection results will impact the identification of the inferior alveolar nerve compressed by the third molar. Through the innovative edge region segmentation algorithm can effectively distinguish this object, and the overall evaluation accuracy can be improved by approximately 3.8%. © 2013 IEEE.

关键词： Neurophysiology

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Forward and Reverse Converters for the Moduli-Set {22q+1, 2q + 2q−1 ± 1}

arXiv

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arXiv 2024年

作者： Jaberipur, Ghassem Nadimi, Bardia Kazemi, R. Lee, Jeong-A The Brain Pool Program for Chosun University Gwangju Korea Republic of The University of South Florida TampaFL United States Computer Engineering The Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran The Department of Computer Engineering Chosun University Korea Republic of

Modulo-(2q + 2q−1 ± 1) adders have recently been implemented using the regular parallel prefix (RPP) architecture, matching the speed of the widely used modulo-(2q ± 1) RPP adders. Consequently, we introduce a new moduli set τ+ = {22q+1, 2q + 2q−1 ± 1}, with over (2q+2)× dynamic range and adder speeds comparable to the conventional τ = {2q, 2q ± 1} set. However, to fully leverage τ+ in residue number system applications, a complete set of circuitries is necessary. This work focuses on the design and implementation of the forward and reverse converters for τ+. These converters consist of four and seven levels of carry-save addition units, culminating in a final modulo-(2q + 2q−1 ± 1) and modulo-(22q+1 + 22q−2 − 1) adder, respectively. Through analytical evaluations and circuit simulations, we demonstrate that the overall performance of a sequence of operations-including residue generation, k additions, and reverse conversion-using τ+ surpasses that of τ when k exceeds a certain practical threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Adders

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Learning to Escape: Multi-mode Policy Learning for the Traveling Salesmen Problem

Learning to Escape: Multi-mode Policy Learning for the Trave...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Myoung Hoon Ha Seunggeun Chi Sang Wan Lee Center for Neuroscience-inspired AI KAIST Daejeon Korea School of Electrical & Computer Engineering Purdue University West Lafayette USA Department of Brain Cognitive Science KAIST Daejeon Korea

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The traveling salesmen problem (TSP)-one of the most fundamental NP-hard problems in combinatorial optimization-has received considerable attention owing to its direct applicability to real-world routing. Recent studies on TSP have adopted a deep policy network to learn a stochastic acceptance rule. Despite its success in some cases, the structural and functional complexity of the deep policy networks makes it hard to explore the problem space while performing a local search at the same time. We found in our empirical analyses that searching processes are often stuck in the local region, leading to severe performance degradation. To tackle this issue, we propose a novel method for multi-mode policy learning. In the proposed method, a conventional exploration-exploitation scheme is reformulated as the problem of learning to escape from a local search area to induce exploration. We present a multi-mode Markov decision process, followed by policy and value design for local search and escaping modes. Experimental results show that the performance of the proposed method is superior to that of various baseline models, suggesting that the learned escaping policy allows the model to initiate a new local search in promising regions efficiently.

关键词： Degradation NP-hard problem Markov decision processes Stochastic processes Search problems Routing Space exploration

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Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method

arXiv

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arXiv 2024年

作者： Liu, Ze Zhang, Jin Feng, Chao Lian, Defu Wang, Jie Chen, Enhong The School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China The School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The Department of Electronic Engineering and Information Science University of Science and Technology of China Anhui Hefei230027 China

With the advancement of deep learning, deep recommendation models have achieved remarkable improvements in recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods still suffer from low recommendation efficiency. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. To guarantee the effectiveness of beam search for recommendation accuracy, these models strive to ensure that the tree adheres to the max-heap assumption, where a parent node's preference should be the maximum among its children's preferences. However, they employ a one-versus-all strategy, framing the training task as a series of independent binary classification objectives for each node, which limits their ability to fully satisfy the max-heap assumption. To this end, we propose a Deep Tree-based Retriever (DTR for short) for efficient recommendation. DTR frames the training task as a softmax-based multi-class classification over tree nodes at the same level, enabling explicit horizontal competition and more discriminative top-k selection among them, which mimics the beam search behavior during training. To mitigate the suboptimality induced by the labeling of non-leaf nodes, we propose a rectification method for the loss function, which further aligns with the max-heap assumption in expectation. As the number of tree nodes grows exponentially with the levels, we employ sampled softmax to approximate optimization and thereby enhance efficiency. Furthermore, we propose a tree-based sampling method to reduce the bias inherent in sampled softmax. Theoretical results reveal DTR's generalization capability, and both the rectification method and tree-based sampling contribute to improved generalization. The experiments are conducted on four real-world datasets, validating the effectivenes

关键词： Deep reinforcement learning

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Stock Price Prediction and Recommendation Approach Based on Machine Learning 4

Stock Price Prediction and Recommendation Approach Based on ...

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4th IEEE Eurasia Conference on IoT, Communication and engineering, ECICE 2022

作者： Chang, Chin-Chih Luo, Kei-Wen Hsiao, Sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

Investment is a lifetime work. Investing in stocks is a popular measure. However, investing in stocks is not easy, and losing money is not uncommon. We investigate if there is a systematic way to find buyable stocks and more importantly when to buy and sell them. Nowadays, with the progress of hardwarel, artificial intelligence (AI) has become a feasible solution to many issues. Machine learning and deep learning are two widely adopted methods in AI. In this study, we show how to apply machine learning to stock prediction and recommendation. After trials, we found that LightGBM shows its potential to predict and recommend stocks. We used the data of the Taiwan stock market to validate the method. We compared the prediction and recommendation with the wellknown Taiwan ETFs 0050 and 0056. The results show that the method is feasible, effective, efficient, and has better performance than Taiwan ETFs 0050 and 0056. © 2022 IEEE.

关键词： Deep learning

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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