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Portfolio selection based on market states acquired via price and non-price data

Discover Data 2025年第1期3卷 1-21页

作者： Chung, Andy Tanaka-Ishii, Kumiko Yairi, Takehisa Department of Advanced Interdisciplinary Studies Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Computer Science and Engineering School of Fundamental Science and Engineering Waseda University Tokyo Japan

Financial markets exhibit quasi-stationary patterns, known as market states, which previous studies have identified through the correlation structure of asset returns using clustering techniques. In this paper, we propose a novel approach to the portfolio selection problem by leveraging market states through an unsupervised method that extends beyond conventional correlation analysis. We introduce market distance metrics for both price and non-price data to estimate market states. Our proposed model allocates assets based on portfolio choices optimized within specified market states. In addition to using asset return correlations, we experiment with two non-price data sources: (1) a text-based market distance metric derived from Federal Open Market Committee (FOMC) policy statements, and (2) the correlation of multivariate time series search volumes from Google Trends. Using a multi-asset portfolio encompassing major U.S. asset classes, we trained our model from 2016 to 2019 and evaluated its out-of-sample performance from 2020 to 2023. Our findings demonstrate the superiority of our ensemble solution, incorporating multiple market distance metrics with price and non-price data, over established baselines with statistically significant results.

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Laser assisted phase transformation of carbon-based transition metal oxide electrode for high-performance supercapacitor application

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science China Chemistry 2025年

作者： Mingzhen Xiu Jianghong Zhang Yu Lu Xun Cao Kang Huang Siyu Zhu Bowei Zhang Yu Liang Chunwang Zhao Junsheng Wu Yizhong Huang School of Materials Science and Engineering Nanyang Technological University Energy Research Institute Interdisciplinary Graduate Programme Nanyang Technological University Institute of Sustainability for Chemicals Energy and Environment Agency for Science Technology and ResearchSingapore Institute of Advanced Materials and Technology University of Science and Technology Beijing School of Materials Science and Technology Shenyang University of Chemical Technology School of Materials Science and Hydrogen Energy Foshan University

Transitional metal oxides have a wide range of applications due to their superior *** enhanced performance of metal oxide relies on their desirable oxidation states,which is challenging to achieve and requires complex *** study demonstrates a facile strategy enabling rapid phase transformation of transition metal *** approach involves synthesis of a tetragonal Mn3O4/carbon cloth composite using the liquid plasma discharge deposition （LPDD） technique,followed by laser irradiation with the presence of ethylene glycol under an N2gas *** resulting face-centered cubic MnO/carbon cloth electrode exhibits an excellent capacitance of 1,125 F g-1at 2 A g-1,with remarkable capacitance retention of 95%after10,000 *** approach can be extended to other transition metals including Co,Cu,Cr,Fe,Ni,and *** simulations reveal the significance of ethylene glycol and the underlying mechanism associated with the phase transformation of transition metal *** present study offers a powerful technique for the phase transformation of transition metal oxide which can guide the development of new electrode materials with enhanced electrochemical properties for high-performance energy storage applications.

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VR-Fi: Positioning and Recognizing Hand Gestures via VR-embedded Wi-Fi Sensing

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Hongbo Li, Xin Li, Jiachun Zhu, Haojin Luo, Jun Collaborative InitiativeInterdisciplinary Graduate Programme 639798 Singapore College of Computing and Data Science 639798 Singapore Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

Accurate gesture-based interactions are crucial for enhancing the immersive experience in VR (virtual reality) systems;they in turn necessitate gesture positioning and recognition in physical world. However, existing VR gesture recognition methods are predominantly vision-based, incurring high computational demands and raising privacy concerns. Meanwhile, Wi-Fi-based gesture recognition methods, deemed as promising complement to vision-based ones, typically lack gesture positioning capabilities. To this end, we propose VR-Fi, a gesture positioning and recognition system leveraging VR(-headset)-embedded Wi-Fi. To position gestures across different areas, VR-Fi innovates in a frequency-hopping bandwidth expansion (FHBE) technique to improve spatial resolution for locating a target. Additionally, VR-Fi innovates in neural models to process the FHBE-enhanced Wi-Fi CSI (channel state information) and enable the multi-task requirements of the joint positioning and recognition of hand gestures. Extensive experimental results demonstrate that VR-Fi achieves a positioning accuracy of 94.47%, a recognition accuracy of 92.13%, and a joint accuracy of 89.47%. © 2002-2012 IEEE.

关键词： Frequency hopping

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A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud-Edge-End Networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1363-1394页

作者： Shi, Wanxin Li, Qing Yu, Qian Wang, Fulin Shen, Gengbiao Jiang, Yong Xu, Yang Ma, Lianbo Muntean, Gabriel-Miro Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Fudan University School of Computer Science Shanghai200433 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518066 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China Tecorigin Software Department Shanghai200120 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Northeastern University College of Software Shenyang110819 China Dublin City University Performance Engineering Laboratory School of Electronic Engineering DublinD09 DD7R Ireland

The digital age has brought a significant increase in video traffic. This traffic growth, driven by rapid Internet advancements and a surge in multimedia applications, presents both challenges and opportunities to video transmissions. Users seek high-quality video content, prompting service providers to offer high-definition options to improve user experience and increase profits. However, traditional end-to-end best-effort networks struggle to meet the demands of extensive video streaming and ensure good user Quality of Experience (QoE), especially in high user mobility scenarios or fluctuating network conditions. Addressing some of these challenges, content delivery networks (CDN) are instrumental in delivering video content, but they are under increased pressure to support high quality and reduce their deployment and maintenance costs. Currently, cloud-edge-end fusion technologies have become one of the optimization directions for network services due to their flexibility and scalability. At the same time, in the context of the recent advancements in computing-focused network paradigms, intelligent enhancement techniques (e.g., super-resolution), commonly utilized in image optimization, have been adopted as a pivotal solution for increasing video delivery quality. To illustrate the essence and employment of the intelligent enhancement solutions for video streaming, this paper first outlines the video streaming process, discusses relevant evaluation metrics, and examines aspects related to the intelligent solutions. Then the paper presents the intelligent enhancement process of video streaming, analyzes various typical intelligent models for content enhancement and highlights their distinct characteristics. This exploration delves deeper into various intelligent quality-improved solutions, scrutinizing their applicability across different transmission scenarios like Video on Demand (VoD) and live streaming, and shedding light on their strengths and weaknesses from

关键词： Video streaming

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The use of an oxidized carbon nanotube film to control Zn deposition and eliminate dendrite formation in a Zn ion battery

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新型炭材料(中英文) 2025年第1期40卷 154-166页

作者： LI Pin-xiang YI Zhe-han WANG Ye-xing HE Chang LIANG Ji HOU Feng Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education School of Materials Science and Engineering Tianjin UniversityTianjin 300072China Interdisciplinary Graduate Programme-Collaborative Initiative Graduate CollegeNanyang Technological UniversitySingapore 637371Singapore School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Tianjin Key Laboratory of Low-dimensional Electronic Materials and Advanced Instrumentation Tianjin 300072China

Aqueous zinc ion batteries are regarded as one of the most promising candidates for large-scale energy stor-age due to their high safety,cost-effectiveness,and environ-mental ***,uncontrolled zinc dendrite growth and side reactions of the zinc anode decrease the sta-bility of Zn *** report the synthesis of an air-oxid-ized carbon nanotube(O-CNT)film by chemical vapor de-position followed by heat treatment in air which is used as a protective layer on the Zn foil to suppress zinc dendrite *** increase in the hydrophilicity of the O-CNT film caused by air oxidation facilitates zinc deposition between the film and the anode instead of deposition on the film *** porous structure of the O-CNT film homogenizes the Zn^(2+)ion flux and the electric field on the surface of the Zn foil,leading to the uniform deposition of *** a result,a O-CNT@Zn symmetric cell has a much better cycling stability with a life of more than 3000 h at 1 mA cm^(−2) with a capacity of 1 mAh cm^(−2),and values of more than 2000 h and 1 mAh cm^(−2) at 5 mA cm^(−2).In addition,a O-CNT@Zn||Mn^(2+)inserted hydrated vanadium pentoxide(MnVOH)full cell has a better rate per-formance than a Zn||MnVOH cell,achieving a high discharge capacity of 194 mAh g^(−1) at a high current density of 8 A g^(−1).In a long-term cycling test,the O-CNT@Zn||MnVOH full cell has a capacity retention of 58.8%after 2000 cycles at a current density of 5 A·g^(−1).

关键词： Carbon nanotubes Zn metal anodes Dendrite-free Hydrophilic Surface functionalization

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Data-Driven Incentive Mechanisms for Federated Learning in Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Fathi, Faeze Montazeri, Mina N.araabi, Babak Du, Hongyang Niyato, Dusit Kebriaei, Hamed University of Tehran School of Ece College of Engineering Tehran Iran Urban Energy Systems Laboratory Dübendorf8600 Switzerland Nanyang Technological University School of Computer Science and Engineering The Energy Research Institute @ Ntu Interdisciplinary Graduate Program Singapore Nanyang Technological University School of Computer Science and Engineering Singapore School of Computer Science Tehran Iran

Centralized machine learning algorithms in vehicular networks face privacy and resource constraints. Federated Learning (FL) addresses these by enabling collaborative model training without sharing raw data. To incentivize vehicle participation despite resource constraints, we propose a contract between Roadside Units (RSUs) and vehicles, specifying required computation resources and rewards based on local data quality. The contract is designed to maximize the utility of the RSU, subject to constraints that ensure the participation of the vehicles in the task and truthful reporting of their data quality parameter. However, designing an optimal contract with classical methods is limited to specific utility functions. To extend the design methodology of optimal contracts to a broader range of utility functions, our study proposes a deep learning-based solution. Specifically, we present a novel application of Deep Neural Networks (DNNs), marking the first instance of applying DNNs in contract theory-based incentive mechanisms to achieve nearoptimal contracts. We design the DNN's structure to inherently satisfy specific constraints of the optimization problem, enhancing its efficiency and accuracy in determining the optimal contract. We utilize an augmented Lagrangian method to approximate the optimal contract in federated learning tasks. A case study on traffic sign recognition demonstrates the applicability and effectiveness of our method. Comparing our approach with the well-known generative diffusion model shows that in the FL scenarios, the DNN results are closer to the optimal contract compared to those of the generative diffusion model. © 1967-2012 IEEE.

关键词： Traffic signs

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Lithium recovery from seawater desalination brines using ion-sieve electrospun nanofibrous membranes: the role of nanofiber design

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Chemical engineering Journal 2025年 513卷

作者： Nadzri, Naeem Lim, Yu Jie Liao, Xiangjun Liang, Yejin Goh, Kunli Liao, Yuan Goto, Atsushi Wang, Rong Interdisciplinary Graduate Programme Graduate College Nanyang Technological University Singapore Singapore Membrane Technology Center Nanyang Environment and Water Research Institute Nanyang Technological University Singapore College of Environmental Science and Engineering Nankai University Tianjin300350 China School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore School of Civil and Environmental Engineering Nanyang Technological University Singapore

The increasing demand for lithium (Li) necessitates sustainable extraction from alternative sources such as desalination brines. This study developed and evaluated electrospun nanofibrous membranes with lithium ion-sieves, H1.6Mn1.6O4 (HMO) and H2TiO3 (HTO), for lithium recovery from seawater reverse osmosis (SWRO) brines. Three distinct nanofiber morphologies, namely – direct-blend, mesoporous, and electrospray – were fabricated to optimize adsorbent distribution and accessibility. Mesoporous membranes enhanced adsorption by creating accessible pores via sacrificial polymer elution, achieving 13.5 % capacity increase over powder adsorbents. Electrospray membranes, despite maximizing adsorbent surface exposure, showed a 25.6 % capacity reduction due to particle agglomeration. Static tests demonstrated HTO's higher lithium selectivity, but slower kinetics compared to HMO. Dynamic experiments revealed mesoporous HMO/PAN membranes as optimal, achieving a 82.7-fold lithium enrichment (from 0.34 to 28.11 mg L−1) and a dynamic capacity of 2.11 ± 0.16 mg g−1 over six cycles. Mesoporous HMO/PAN membranes were able to concentrate 3.7 times more lithium in the first cycle compared to the HMO packed bed configuration (7.78 mg L−1 eluted compared to 2.12 mg L−1). High separation factors (4198 for Na+, 4131 for Mg2+, 1829 for Ca2+, and 1113 for K+) underscored exceptional selectivity of the ion-sieve embedded membranes. Continuous adsorption–desorption tests confirmed the membranes’ robustness and reusability. These results demonstrate the critical role of nanofiber design in optimizing adsorption capacity, selectivity, and kinetics, enabling scalable lithium recovery from desalination brines. © 2025 Elsevier B.V.

关键词： Mesopores

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Intuitive User-Guided Portrait Image Editing with Asymmetric Conditional GAN

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Computational Visual Media 2025年第2期11卷 361-379页

作者： Linlin Liu Qian Fu Fei Hou Ying He Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group Singapore Singapore Data61 Commonwealth Scientific and Industrial Research Organisation Sydney Australia Key Laboratory of System Software (CAS) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

We propose PortraitACG, a novel framework for user-guided portrait image editing that leverages an asymmetric conditional generative adversarial network (GAN), which supports the fine-grained editing of geometries, colors, lights, and shadows using a single neural network model. Existing conditional GAN-based approaches usually feed the same conditional information into generators and discriminators, which is sub-optimal because these two modules are designed for different purposes. To facilitate flexible user-guided editing, we propose a novel asymmetric conditional GAN, where the generators take the transformed conditional inputs, such as edge maps, color palettes, sliders, and masks, that can be directly edited by the user, and the discriminators take the conditional inputs in a way that can guide controllable image generation more effectively. This allows image editing operations to be performed in a simpler and more intuitive manner. For example, the user can directly use a color palette to specify the desired colors of hair, skin, eyes, lips, and background and use a slider to blend colors. Moreover, users can edit the lights and shadows by modifying their corresponding masks.

关键词： Image color analysis Image edge detection Generative adversarial networks Generators Training Geometry Feature extraction Hair Feeds Image synthesis

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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