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Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

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Deterministic remote entanglement using a chiral quantum interconnect

arXiv

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arXiv 2024年

作者： Almanakly, Aziza Yankelevich, Beatriz Hays, Max Kannan, Bharath Assouly, Réouven Greene, Alex Gingras, Michael Niedzielski, Bethany M. Stickler, Hannah Schwartz, Mollie E. Serniak, Kyle Wang, Joel I.-J. Orlando, Terry P. Gustavsson, Simon Grover, Jeffrey A. Oliver, William D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Quantum interconnects facilitate entanglement distribution between non-local computational nodes. For superconducting processors, microwave photons are a natural means to mediate this distribution. However, many existing architectures limit node connectivity and directionality. In this work, we construct a chiral quantum interconnect between two nominally identical modules in separate microwave packages. We leverage quantum interference to emit and absorb microwave photons on demand and in a chosen direction between these modules. We optimize the protocol using model-free reinforcement learning to maximize absorption efficiency. By halting the emission process halfway through its duration, we generate remote entanglement between modules in the form of a four-qubit W state with 62.4±1.6% (leftward photon propagation) and 62.1±1.2% (rightward) fidelity, limited mainly by propagation loss. A chiral quantum network comprising many modules provides a platform for the exploration of novel many-body physics and quantum simulation. This quantum network architecture enables all-to-all connectivity between non-local processors for modular and extensible quantum computation. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum entanglement

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Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Shuai Chen, Zhengyang Lee, Kong Aik Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab The Department of Computer Science and Engineering The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Roles of UAVs, Robots, and AI in Earthquake Disaster Management

Roles of UAVs, Robots, and AI in Earthquake Disaster Managem...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Mohamed S. Abdalzaher Mostafa Shaaban Moez Krichen Raafat Aburukba Electrical Engineering Department American University of Sharjah Sharjah UAE Seismology Department National Research Institute of Astronomy and Geophysics Cairo Egypt Energy Water and Sustainable Environment Research Center American University of Sharjah Al-Baha University Al-Baha Saudi Arabia ReDCAD Laboratory University of Sfax Sfax Tunisia Computer Science and Engineering Department American University of Sharjah Sharjah UAE

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Earthquakes are among the most destructive natural disasters, causing widespread loss of life, property, and infrastructure. Effective disaster management is essential to minimize their impact. This article examines the critical roles of Unmanned Aerial Vehicles (UAVs), robots, and Artificial Intelligence (AI) in earthquake disaster management across the phases of pre-disaster preparedness, real-time response, during the event, and after the disaster. The paper explores various types of UAVs and robots utilized for earthquake monitoring, highlights their advantages, and addresses the challenges associated with their deployment. Besides, the types of robots are also addressed to contribute to earthquake disaster management and to alleviating the consequences. By leveraging these advanced technologies, disaster response and recovery efforts can be significantly enhanced, offering new possibilities for resilience and efficiency in managing earthquake-related emergencies.

关键词： Power engineering Disasters Earthquakes Disaster management Real-time systems Risk management Artificial intelligence Robots Monitoring Resilience

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Mechatronic Design for Multi Robots-Insect Swarms Interactions

Mechatronic Design for Multi Robots-Insect Swarms Interactio...

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2023 IEEE International Conference on Mechatronics (ICM)

作者： Fatemeh Rekabi-Bana Martin Stefanec Jiří Ulrich Erhan E. Keyvan Tomáš Rouček George Broughton Bilal Y. Gündeǧer Ömer Sahin Ali E. Turgut Erol Şahin Tomáš Krajník Thomas Schmickl Farshad Arvin Department of Computer Science Swarm & Computational Intelligence Laboratory (SwaCIL) Durham University UK Artificial Life Lab University of Graz Austria Faculty of Electrical Engineering Czech Technical University Czechia Middle East Technical University Türkiye

This paper presents the concept of a robotic system collaborating with a swarm of social insects inside their hive. This robot consists of a micro-and macro-manipulator and a tracking system. The micro-manipulator uses bio-mimetic agents to interact with an individual specimen. The macromanipulator positions and keeps the micro-manipulator’s base around the given individual while moving in the hive. This individual is tracked by a fiducial marker-based visual detection and localisation system, which also provides positions of the biomimetic agents. The base of the system was experimentally verified in a honeybee observation hive, where it flawlessly tracked the honeybee queen for several hours, gathering sufficient data to extract the behaviours of honeybee workers in the queen’s vicinity. These data were then used in simulation to verify if the micro-manipulator’s bio-mimetic agents could mimic some of the honeybee workers’ behaviours.

关键词： Visualization Mechatronics Tracking Databases Machine vision Insects Data models

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All-passive upconversion of incoherent near-infrared light at intensities down to ~10-7 W/cm2

arXiv

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arXiv 2024年

作者： Hamid, Rabeeya Feng, Demeng Narayanan, Pournima Edwards, Justin S. Hu, Manchen Belliveau, Emma Kim, Minjeong Deshpande, Sanket Wan, Chenghao Pucurimay, Linda Czaplewski, David A. Congreve, Daniel N. Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison53706 United States Department of Chemistry Stanford University StanfordCA94305 United States Department of Physics University of Wisconsin-Madison Madison53706 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Center for Nanoscale Materials Argonne National Laboratory ArgonneIL60439 United States

Frequency upconversion, which converts low-energy photons into higher-energy ones, typically requires intense coherent illumination to drive nonlinear processes or the use of externally driven optoelectronic devices. Here, we demonstrate an upconversion system that converts low-intensity (down to ~10-7 W/cm2) incoherent near-infrared (NIR) light into the visible, reaching intensities perceptible by the human eye, without the use of any external power input. Our upconverting element is enabled by the following ingredients: (1) photon upconversion via triplet-triplet annihilation in a bulk heterojunction of the organic semiconductors Y6 and rubrene;(2) plasmonic enhancement of absorption and field intensity in the heterojunction layer;(3) collection enhancement using a dichroic thin-film assembly. To enable high-resolution imaging, the upconverting element is inserted at an intermediate image plane of a dual-wavelength telescope system, which preserves the relative directionality of rays between the incident NIR light and output visible light. Our all-passive upconversion imaging system will enable NIR imaging and sensing in low-light environments under energy constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

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EC2Seq2Sql: Patient-Trial Matching with LLM Agents

SSRN

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SSRN 2025年

作者： Yang, Liu Han, Yongzhong Liu, Liang Jiang, Xiaoyan Li, Ying Jihan, Huang Su, Qianmin School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Clinical Research Unit Institute of Clinical Science Zhongshan Hospital Fudan University Shanghai China Department of Hepatology Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai China Center for Drug Clinical Research Shanghai University of Traditional Chinese Medicine Shanghai China Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

Objective: The success of clinical trials hinges upon the timely identification of patients who satisfy the stipulated eligibility criteria. However, eligibility criteria are frequently articulated in natural language, and their complexity and flexibility render traditional manual screening methods not only inefficient but also susceptible to errors. Nevertheless, eligibility criteria are commonly articulated in natural language, and their complexity and flexibility render traditional manual screening methods not only inefficient but also prone to errors. To address this challenge, we propose the EC2Seq2Sql method, which is designed to automatically translate complex eligibility criteria into SQL queries, thereby facilitating efficient and accurate patient screening. Materials and Methods: EC2Seq2Sql consists of two stages. In the first stage, employing the BART model to perform semantic parsing on the natural language text of clinical trial eligibility criteria, converting the complex text into lightweight and structured pattern sequences. In the second stage, by employing an agent to handle these lightweight structured sequences, the model is further guided to transform the structured sequences into SQL statements through the integration of system-designed and human-curated prompts. Finally, the generated SQL queries can be applied to the EHR database to identify patients who meet the eligibility criteria. This approach also offers valuable insights into the future structuring of clinical trial eligibility criteria and the development of systems that align these criteria with EHR data. Results: The experimental results demonstrate that the BART model achieved scores of 0.8105 and 0.8427 on the ROUGE and BLEU evaluation metrics, respectively, significantly outperforming the comparison models, including T5-base, T5-small, and GPT-2. During the SQL generation process, the agent, guided by prompt engineering, transforms the lightweight and structured information extra

关键词： Semantics

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Quantum advantage for differential equation analysis

引用

Physical Review A 2022年第2期105卷 022415-022415页

作者： Bobak Toussi Kiani Giacomo De Palma Dirk Englund William Kaminsky Milad Marvian Seth Lloyd Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Giacomo De Palma Scuola Normale Superiore 56126 Pisa Italy QuEra Computing Boston Massachusetts 02143 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.

关键词： Machine learning Quantum algorithms Quantum computation Markovian processes Numerical techniques

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A Hypergraph Approach to Distributed Broadcast

arXiv

引用

arXiv 2024年

作者： Cao, Qi Shao, Yulin Yang, Fan Dobre, Octavia A. Xidian-Guangzhou Research Institute Xidian University Guangzhou China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China The Faculty of Engineering and Applied Science Memorial University St. John'sNLA1B 3X5 Canada

This paper explores the distributed broadcast problem within the context of network communications, a critical challenge in decentralized information dissemination. We put forth a novel hypergraph-based approach to address this issue, focusing on minimizing the number of broadcasts to ensure comprehensive data sharing among all network users. The key contributions of this work include the establishment of a general lower bound for the problem using the min-cut capacity of hypergraphs, and a distributed broadcast for quasi-trees (DBQT) algorithm tailored for the unique structure of quasi-trees, which is proven to be optimal. This paper advances both network communication strategies and hypergraph theory, with implications for a wide range of real-world applications, from vehicular and sensor networks to distributed storage systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Bibliographies

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Suppressing Counter-Rotating Errors for Fast Single-Qubit Gates with Fluxonium

arXiv

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arXiv 2024年

作者： Rower, David A. Ding, Leon Zhang, Helin Hays, Max An, Junyoung Harrington, Patrick M. Rosen, Ilan T. Gertler, Jeffrey M. Hazard, Thomas M. Niedzielski, Bethany M. Schwartz, Mollie E. Gustavsson, Simon Serniak, Kyle Grover, Jeffrey A. Oliver, William D. Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States

Qubit decoherence unavoidably degrades the fidelity of quantum logic gates. Accordingly, realizing gates that are as fast as possible is a guiding principle for qubit control, necessitating protocols for mitigating error channels that become significant as gate time is decreased. One such error channel arises from the counter-rotating component of strong, linearly polarized drives. This error channel is particularly important when gate times approach the qubit Larmor period and represents the dominant source of infidelity for sufficiently fast single-qubit gates with low-frequency qubits such as fluxonium. In this work, we develop and demonstrate two complementary protocols for mitigating this error channel. The first protocol realizes circularly polarized driving in circuit quantum electrodynamics (QED) through simultaneous charge and flux control. The second protocol—commensurate pulses—leverages the coherent and periodic nature of counter-rotating fields to regularize their contributions to gates, enabling single-qubit gate fidelities reliably exceeding 99.997%. This protocol is platform independent and requires no additional calibration overhead. This work establishes straightforward strategies for mitigating counter-rotating effects from strong drives in circuit QED and other platforms, which we expect to be helpful in the effort to realize high-fidelity control for fault-tolerant quantum computing. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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