检索结果-内蒙古大学图书馆

Low-temperature spin Seebeck effect in nonmagnetic vanadium dioxide

physical Review B 2024年第2期110卷 024415-024415页

作者： Renjie Luo Tanner J. Legvold Liyang Chen Henry Navarro Ali C. Basaran Deshun Hong Changjiang Liu Anand Bhattacharya Ivan K. Schuller Douglas Natelson Department of Physics and Astronomy Applied Physics Graduate Program Rice Quantum Institute Department of Physics and Center for Advanced Nanoscience Materials Science Division Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination.

关键词： Spin Seebeck effect Paramagnets Strongly correlated systems

来源：评论

学校读者我要写书评

暂无评论

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Enhancing Geometric Ontology Embeddings for EL++ with Negative Sampling and Deductive Closure Filtering

arXiv

引用

arXiv 2024年

作者： Mashkova, Olga Zhapa-Camacho, Fernando Hoehndorf, Robert Computer Science Program Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Ontology embeddings map classes, relations, and individuals in ontologies into Rn, and within Rn similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL++, several embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations;they do not distinguish between statements that are unprovable and provably false, and therefore they may use entailed statements as negatives. Furthermore, they do not utilize the deductive closure of an ontology to identify statements that are inferred but not asserted. We evaluated a set of embedding methods for EL++ ontologies based on high-dimensional ball representation of concept descriptions, incorporating several modifications that aim to make use of the ontology deductive closure. In particular, we designed novel negative losses that account both for the deductive closure and different types of negatives. We demonstrate that our embedding methods improve over the baseline ontology embedding in the task of knowledge base or ontology completion. © 2024, CC BY.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Functional Equation Proving: A Benchmark Dataset and A Domain-Specific In-Context Agent

arXiv

引用

arXiv 2024年

作者： Buali, Mahdi Hoehndorf, Robert Computer Science Program Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Automated Theorem Proving (ATP) faces significant challenges due to the vast action space and the computational demands of proof generation. Recent advances have utilized Large Language Models (LLMs) for action selection in ATP, but these methods often require substantial computational resources. This study introduces the Functional Equation Automated Solver (FEAS), an agent that builds on the COPRA in-context learning framework within the Lean environment. FEAS innovates by refining prompt generation and response parsing mechanisms, integrating domain-specific heuristics for functional equations, and introducing the FunEq dataset—a rigorously curated collection of functional equation problems categorized into three difficulty levels. The agent’s performance is evaluated against established baselines using this dataset, demonstrating improvements in theorem proving accuracy, particularly with the integration of functional equation-specific heuristics. Our results highlight the effectiveness of FEAS in generating and formalizing high-level proof strategies into Lean proofs, emphasizing the potential of tailored approaches in domain-specific ATP challenges. © 2024, CC BY.

关键词： Theorem proving

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives

arXiv

引用

arXiv 2023年

作者： Krestinskaya, Olga Zhang, Li Salama, Khaled Nabil Electrical and Computer Engineering Program the Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided. © 2023, CC BY.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

engineering of dendritic dopant-free hole transport molecules:enabling ultrahigh fill factor in perovskite solar cells with optimized dendron construction

引用

science China Chemistry 2021年第1期64卷 41-51页

作者： Wei Chen Yang Wang Bin Liu Yajun Gao Ziang Wu Yongqiang Shi Yumin Tang Kun Yang Yujie Zhang Weipeng Sun Xiyuan Feng Frédéric Laquai Han Young Woo Aleksandra B.Djurisic Xugang Guo Zhubing He Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Physics The University of Hong KongPokfulamHong KongChina King Abdullah University of Science and Technology KAUST Solar CenterPhysical Sciences and Engineering DivisionMaterial Science and Engineering ProgramThuwal 23955-6900Kingdom of Saudi Arabia Department of Chemistry Korea UniversitySeoul 02841South Korea

Developing dopant-free hole-transporting materials(HTMs)for high-performance perovskite solar cells(PVSCs)has been a very active research topic in recent years since HTMs play a critical role in optimizing interfacial charge carrier kinetics and in turn determining device ***,a novel dendritic engineering strategy is first utilized to design HTMs with a D-A type molecular framework,and diphenylamine and/or carbazole is selected as the building block for constructing *** HTMs show good thermal stability and excellent film morphology,and the key optoelectronic properties could be fine-tuned by varying the dendron *** them,MPA-Cz-BTI and MCz-Cz-BTI exhibit an improved interfacial contact with the perovskite active layer,and non-radiative recombination loss and charge transport loss can be effectively ***,high power conversion efficiencies(PCEs)of 20.8%and 21.35%are achieved for MPA-Cz-BTI and MCz-Cz-BTI based devices,respectively,accompanied by excellent long-term storage *** encouragingly,ultrahigh fill factors of 85.2%and 83.5%are recorded for both devices,which are among the highest values reported to *** work demonstrates the great potential of dendritic materials as a new type of dopant-free HTMs for high-performance PVSCs with excellent FF.

关键词： dendritic molecules hole-transporting materials dopant-free ultrahigh fill factor perovskite solar cells

来源：评论

学校读者我要写书评

暂无评论

Sequence-Defined Nanotubes Assembled from IR780-Conjugated Peptoids for Chemophototherapy of Malignant Glioma

引用

Research 2021年第1期2021卷 1379-1390页

作者： Xiaoli Cai Mingming Wang Peng Mu Tengyue Jian Dong Liu Shichao Ding Yanan Luo Dan Du Yang Song Chun-Long Chen Yuehe Lin School of Mechanical and Materials Engineering Washington State UniversityPO Box 642920 PullmanWashington 99164USA Physical Sciences Division Pacific Northwest National LaboratoryRichlandWA 99352USA Department of Mechanical Engineering and Materials Science and Engineering Program State University of New YorkBinghamtonNew York 13902USA

Near-infrared(NIR)laser-induced phototherapy through NIR agents has demonstrated the great potential for cancer ***,insufficient tumor killing due to the nonuniform heat or cytotoxic singlet oxygen(1 O2)distribution over tumors from phototherapy results in tumor recurrence and inferior *** achieve high tumor killing efficacy,one of the solutions is to employ the combinational treatment of phototherapy with other modalities,especially with chemotherapeutic *** this paper,a simple and effective multimodal therapeutic system was designed via combining chemotherapy,photothermal therapy(PTT),and photodynamic therapy(PDT)to achieve the polytherapy of malignant glioma which is one of the most aggressive tumors in the ***-780(IR780)dye-labeled tube-forming peptoids(PepIR)were synthesized and self-assembled into crystalline nanotubes(PepIR nanotubes).These PepIR nanotubes showed an excellent efficacy for PDT/PTT because the IR780 photosensitizers were effectively packed and separated from each other within crystalline nanotubes by tuning IR780 density;thus,a self-quenching of these IR780 molecules was significantly ***,the efficient DOX loading achieved due to the nanotube large surface area contributed to an efficient and synergistic chemotherapy against glioma *** the unique properties of peptoids and peptoid nanotubes,we believe that the developed multimodal DOX-loaded PepIR nanotubes in this work offer great promises for future glioma therapy in clinic.

关键词： chemotherapy IR780 modal

来源：评论

学校读者我要写书评

暂无评论

Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

引用

Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Steering Customized AI Architectures for HPC Scientific Applications 38th

Steering Customized AI Architectures for HPC Scientific Appl...

引用

38th International Conference on High Performance Computing, ISC High Performance 2023

作者： Ltaief, Hatem Hong, Yuxi Dabah, Adel Alomairy, Rabab Abdulah, Sameh Goreczny, Chris Gepner, Pawel Ravasi, Matteo Gratadour, Damien Keyes, David Division of Computer Electrical and Mathematical Sciences and Engineering Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah23955 Saudi Arabia Division of Physical Sciences and Engineering Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah23955 Saudi Arabia Graphcore Gdańsk Poland Warsaw University of Technology Warsaw Poland Paris Observatory Paris France

ISBN: (纸本)9783031320408

AI hardware technologies have revolutionized computational science. While they have been mostly used to accelerate deep learning training and inference models for machine learning, HPC scientific applications do not seem to directly benefit from these specific hardware features unless AI-based components are introduced into their simulation workflows, for instance, as a replacement of their numerical solvers. This paper proposes to take another direction in an attempt to democratize customized AI architectures for HPC scientific computing. The main idea consists in demonstrating how legacy applications can leverage these AI engines after a necessary algorithmic redesign. It is critical that the resulting software implementations map onto the underlying memory-austere hardware architectures to extract the expected performance. To facilitate this process, we promote the matricization technique for restructuring codes (1) by exploiting data sparsity via algebraic compression and (2) by expressing the critical computational phases in terms of tile low-rank matrix-vector multiplications (TLR-MVM) and batch matrix-matrix multiplications (batch GEMM). Algebraic compression enables to reduce memory footprint and to fit into small local cache/memory, while batch execution ensures high occupancy. We highlight how we can steer the Graphcore AI-focused Wafer-on-Wafer Intelligence Processing Units (IPUs) to deliver high performance for both operations. We conduct a performance benchmarking campaign of these two matrix operations that account for most of the elapsed times of four real applications in computational astronomy, seismic imaging, wireless communications, and climate/weather predictions. We report bandwidth and execution rates with speedup factors up to 150X/14X/25X/40X, respectively, on IPUs compared to other systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Spin Orbit Torque Tunable Skyrmion Neuromorphic Devices

Spin Orbit Torque Tunable Skyrmion Neuromorphic Devices

引用

International Conference on Emerging Electronics (ICEE)

作者： Aijaz H. Lone Xuecui Zou Glen Isaac Maciel Garcia Xiaohang Li Hossein Fariborzi Electrical and Computer Engineering Division King Abdullah University of Science and Technology Saudi Physical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Electrical and Computer Engineering Division and Physical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

Skyrmionic devices are promising candidates for energy efficient and highly integrated data storage and computing applications, owing to their small size, topological protection, and low drive current. In this abstract we propose novel spin orbit torque (SOT) controlled skyrmion device structures and their application in neuromorphic computing. We propose a spin orbit torque-driven SOT skyrmion magnetic tunnel junction (MTJ) device showing Leaky Integrate and Fire (LIF) neuron behaviour, and a SOT-controlled MTJ synaptic device in which the skyrmion size depends on the thickness of the free layer. Furthermore, we implement the spiking neural network and artificial neural network based on these devices for MNIST data classification.

关键词： Torque Neuromorphic engineering Neurons Memory Artificial neural networks Orbits Micromagnetics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：