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Most implemented papers, optuna: a next-generation hyperparameter optimization framework.

We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.

A System for Massively Parallel Hyperparameter Tuning

Modern learning models are characterized by large hyperparameter spaces and long training times.

FedML: A Research Library and Benchmark for Federated Machine Learning

chaoyanghe/Awesome-Federated-Learning • 27 Jul 2020

Federated learning (FL) is a rapidly growing research field in machine learning.

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

gingsmith/cocoa • 7 Nov 2016

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.

Billion-scale Network Embedding with Iterative Random Projection

ZW-ZHANG/RandNE • 7 May 2018

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.

Orchestral: a lightweight framework for parallel simulations of cell-cell communication

Aratz/orchestral • 28 Jun 2018

By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells.

Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering

zhanglabtools/dbmd • 10 Feb 2020

Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.

Computing High Accuracy Power Spectra with Pico

marius311/pypico • 2 Dec 2007

This paper presents the second release of Pico (Parameters for the Impatient COsmologist).

Online Asynchronous Distributed Regression

ryadzenine/dolphin • 16 Jul 2014

Distributed computing offers a high degree of flexibility to accommodate modern learning constraints and the ever increasing size of datasets involved in massive data issues.

MLitB: Machine Learning in the Browser

software-engineering-amsterdam/MLitB • 8 Dec 2014

Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large.

Distributed Systems and Parallel Computing

No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.

From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model .

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Reliability of Trust Management Systems in Cloud Computing

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

Guest Editorial: Special Section on Parallel and Distributed Computing Techniques for Non-Von Neumann Technologies

Asynchronous rpc interface in distributed computing system, developing an efficient secure query processing algorithm on encrypted databases using data compression.

Abstract Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

Deep distributed computing to reconstruct extremely large lineage trees

Distributed computing and artificial intelligence, volume 2: special sessions 18th international conference, software engineering, artificial intelligence, networking and parallel/distributed computing, chinese keyword extraction model with distributed computing, on allocation of systematic blocks in coded distributed computing, export citation format, share document.

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Reliable and efficient RAR-based distributed model training in computing power network

Ling Chen, Yajie Li, Carlos Natalino, Yongcheng Li, Boxin Zhang, Yingbo Fan, Wei Wang, Yongli Zhao, and Jie Zhang

Author Affiliations

Ling Chen, 1 Yajie Li, 1, 2, * Carlos Natalino, 3 Yongcheng Li, 4 Boxin Zhang, 1 Yingbo Fan, 1 Wei Wang, 1 Yongli Zhao, 1 and Jie Zhang 1

1 State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China

2 School of Information Science and Technology, Tibet University, Lhasa, 850000, China

3 Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

4 School of Electronic and Information Engineering, Soochow University, Soochow, 215021, China

* Corresponding author: [email protected]

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• Original Manuscript: November 6, 2023
• Revised Manuscript: March 13, 2024
• Manuscript Accepted: March 19, 2024
• Published: April 22, 2024
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The computing power network (CPN) is a novel network technology that integrates computing power from the cloud, edge, and terminals using IP/optical cross-layer networks for distributed computing. CPNs can provide an effective solution for distributed model training (DMT). As a bandwidth optimization architecture based on data parallelism, ring all-reduce (RAR) is widely used in DMT. However, any node or link failure on the ring can interrupt or block the requests deployed on the ring. Meanwhile, due to the resource competition of batch RAR-based DMT requests, inappropriate scheduling strategies will also lead to low training efficiency or congestion. As far as we know, there is currently no research that considers the survivability of rings in scheduling strategies for RAR-based DMT. To fill this gap, we propose a scheduling scheme for RAR-based DMT requests in CPNs to optimize the allocation of computing and wavelength resources considering the time dimension while ensuring reliability. In practical scenarios, service providers may focus on different performance metrics. We formulate an integer linear programming (ILP) model and a RAR-based DMT deployment algorithm (RDDA) to solve this problem considering four optimization objectives under the premise of the minimum blocking rate: minimum computing resource consumption, minimum wavelength resource consumption, minimum training time, and maximum reliability. Simulation results demonstrate that our model satisfies the reliability requirements while achieving corresponding optimal performance for DMT requests under four optimization objectives.

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Huangxu Ma, Jiawei Zhang, Zhiqun Gu, Daniel C. Kilper, and Yuefeng Ji J. Opt. Commun. Netw. 15 (11) 788-803 (2023)

Bowen Chen, Ling Liu, Yuexuan Fan, Weidong Shao, Mingyi Gao, Hong Chen, Weiguo Ju, Pin-Han Ho, Jason P. Jue, and Gangxiang Shen J. Opt. Commun. Netw. 16 (2) 142-158 (2024)

Ruikun Wang, Jiawei Zhang, Zhiqun Gu, Shuangyi Yan, Yuming Xiao, and Yuefeng Ji J. Opt. Commun. Netw. 14 (10) 828-839 (2022)

Wenzhe Li, Guojun Yuan, Zhan Wang, Guangming Tan, Peiheng Zhang, and George N. Rouskas J. Opt. Commun. Netw. 16 (3) 342-357 (2024)

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Computer Science > Networking and Internet Architecture

Title: multi-objective offloading optimization in mec and vehicular-fog systems: a distributed-td3 approach.

Abstract: The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions.

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Advances in Distributed Computing and Machine Learning

Proceedings of ICADCML 2020

• Conference proceedings
• © 2021
• Asis Kumar Tripathy   ORCID: https://orcid.org/0000-0003-2685-9860 0 ,
• Mahasweta Sarkar 1 ,
• Jyoti Prakash Sahoo   ORCID: https://orcid.org/0000-0002-6273-6174 2 ,
• Kuan-Ching Li 3 ,
• Suchismita Chinara 4

Vellore Institute of Technology, Vellore, India

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San Diego State University, San Diego, USA

Institute of technical education and research (iter), siksha ‘o’ anusandhan (soa) deemed to be university, bhubaneswar, india, providence university, taichung, taiwan, national institute of technology, rourkela, rourkela, india.

• Presents research in the field of distributed computing and machine learning
• Includes the outcomes of ICADCML 2020, held at the School of Information Technology and Engineering, VIT, Vellore, India
• Serves as a reference resource for practitioners and researchers in academia and industry

Part of the book series: Lecture Notes in Networks and Systems (LNNS, volume 127)

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Table of contents (50 papers)

Front matter, distributed computing trends, issues, and applications, customized score-based security threat analysis in vanet.

• Alekha Kumar Mishra, Asis Kumar Tripathy, Maitreyee Sinha

FM—MANETs: A Novel Fuzzy Mobility on Multi-path Routing in Mobile Ad hoc Networks for Route Selection

• T. Sudhakar, H. Hannah Inbarani

Black Hole Detection and Mitigation Using Active Trust in Wireless Sensor Networks

• Venkata Abhishek Kanthuru, Kakelli Anil Kumar

Power Allocation-Based QoS Guarantees in Millimeter-Wave-Enabled Vehicular Communications

• Satyabrata Swain, Jyoti Prakash Sahoo, Asis Kumar Tripathy

Renewable Energy-Based Resource Management in Cloud Computing: A Review

• Sanjib Kumar Nayak, Sanjaya Kumar Panda, Satyabrata Das

A Multi-objective Optimization Scheduling Algorithm in Cloud Computing

• Madhu Bala Myneni, Siva Abhishek Sirivella

Addressing Security and Computation Challenges in IoT Using Machine Learning

• Bhabendu Kumar Mohanta, Utkalika Satapathy, Debasish Jena

A Survey: Security Issues and Challenges in Internet of Things

• Balaji Yedle, Gunjan Shrivastava, Arun Kumar, Alekha Kumar Mishra, Tapas Kumar Mishra

Low-Cost Real-Time Implementation of Malicious Packet Dropping Detection in Agricultural IoT Platform

• J. Sebastian Terence, Geethanjali Purushothaman

IoT-Based Air Pollution Controlling System for Garments Industry: Bangladesh Perspective

• Marzan Tasnim Oyshi, Moushumi Zaman Bonny, Syed Ahmed Zaki, Susmita Saha

Automated Plant Robot

• U. Nanda, A. Biswas, K. L. G. Prathyusha, S. Gaurav, V. S. L. Samhita, S. S. Mane et al.

IoT Security Issues and Possible Solution Using Blockchain Technology

• Marzan Tasnim Oyshi, Moushumi Zaman Bonny, Susmita Saha, Zerin Nasrin Tumpa

A Review of Distributed Supercomputing Platforms Using Blockchain

• Kiran Kumar Kondru, R. Saranya, Annamma Chacko

An E-Voting Framework with Enterprise Blockchain

• Mohammed Khaled Mustafa, Sajjad Waheed

Survey of Blockchain Applications in Database Security

• Vedant Singh, Vrinda Yadav

Prevention and Detection of SQL Injection Attacks Using Generic Decryption

• R. Archana Devi, D. Hari Siva Rami Reddy, T. Akshay Kumar, P. Sriraj, P. Sankar, N. Harini

Prevention and Detection of SQL Injection Using Query Tokenization

• R. Archana Devi, C. Amritha, K. Sai Gokul, N. Ramanuja, L. Yaswant

Investigating Peers in Social Networks: Reliable or Unreilable

• M. R. Neethu, N. Harini, K. Abirami
• Distributed Computing
• Cloud Computing
• Machine Learning

About this book

This book presents recent advances in the field of distributed computing and machine learning, along with cutting-edge research in the field of Internet of Things (IoT) and blockchain in distributed environments. It features selected high-quality research papers from the First International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2020), organized by the School of Information Technology and Engineering, VIT, Vellore, India, and held on 30–31 January 2020.

Editors and Affiliations

Asis Kumar Tripathy

Mahasweta Sarkar

Jyoti Prakash Sahoo

Kuan-Ching Li

Suchismita Chinara

About the editors

Asis Kumar Tripathy is an Associate Professor in the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has more than ten years of teaching experience. He completed his Ph.D. from the National Institute of Technology, Rourkela, India, in 2016. His areas of research interests include wireless sensor networks, cloud computing, Internet of things and advanced network technologies. He has several publications in refereed journals, reputed conferences and book chapters to his credit. He has served as a program committee member in several conferences of repute. He has also been involved in many professional and editorial activities.

Mahasweta Sarkar  is currently working as a Professor of the Department of Electrical and Computer Engineering in San Diego State University in 2006. Her M.S. and Ph.D. degrees were completed at the University of California, San Diego (UCSD), in 2003 and 2005, respectively. She received her B.S. degree in Computer Science & Engineering (Summa Cum Laude) in May 2000 from San Diego State University. Dr. Sarkar is a recipient of the “President's Leadership Award for Faculty Excellence” for the year 2010. She delivered invited lectures and keynotes in different universities spread all over the globe. The talks were on Wireless Body Area Networks and Brain–Computer Interface. Her research interest lies in the area of MAC layer power management algorithms and Quality-of-Service issues and protocols in WLANs, WMANs, WBANs, sensor networks and wireless ad hoc networks. She has published over eighty research papers in these fields in various international journals and conferences of high repute. 

Jyoti Prakash Sahoo  is working as an Assistant Professor in the Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, India. He is having more than 12 years of academicand research experience in Computer science and engineering education. He has published several research papers in various international journals and conferences. He is also serving many journals and conferences as an editorial or reviewer board member. He is having expertise in the field of Cloud computing and Machine learning. He served as publicity chair and organizing member of technical program committees of many national and international conferences. Being a WIPRO Certified Faculty, he has also contributed to industry-academia collaboration, student enablement, and pedagogical learning. Furthermore, he is associated with various educational and research societies like IEEE, IET, IACSIT, IAENG, etc.

Kuan-Ching Li  is currently appointed as Distinguished Professor at Providence University, Taiwan. He is a recipient of awards and funding support from several agencies and high-tech companies, as also received distinguished chair professorships from universities inseveral countries. He has been actively involved in many major conferences and workshops in program/general/steering conference chairman positions and as a program committee member, and has organized numerous conferences related to high-performance computing and computational science and engineering. Professor Li is the Editor-in-Chief of technical publications Connection Science (Taylor & Francis), International Journal of Computational Science and Engineering (Inderscience) and International Journal of Embedded Systems (Inderscience), and serves as associate editor, editorial board member and guest editor for several leading journals. Besides publication of journal and conference papers, he is the co-author/co-editor of several technical professional books published by CRC Press, Springer, McGraw-Hill, and IGI Global. His topics of interest include parallel and distributed computing, Big Data, and emerging technologies. He is a Member of the AAAS, a Senior Member of the IEEE, and a Fellow of the IET. 

Suchismita Chinara  is currently working as an Assistant Professor in the Department of Computer Science and Engineering, National Institute of Technology, India. She has received her M.E. degree in 2001 and Ph.D. in 2011 from the Department of Computer Science and Engineering, National Institute of Technology, Rourkela. She has authored and co-authored multiple peer-reviewed scientific papers and presented works at many national and international conferences. Her contributions have acclaimed recognition from honorable subject experts around the world. Her academic career is decorated with several reputed awards and funding. Her research interest includes wireless network, ad hoc network and MANET.

Bibliographic Information

Book Title : Advances in Distributed Computing and Machine Learning

Book Subtitle : Proceedings of ICADCML 2020

Editors : Asis Kumar Tripathy, Mahasweta Sarkar, Jyoti Prakash Sahoo, Kuan-Ching Li, Suchismita Chinara

Series Title : Lecture Notes in Networks and Systems

DOI : https://doi.org/10.1007/978-981-15-4218-3

Publisher : Springer Singapore

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Softcover ISBN : 978-981-15-4217-6 Published: 12 June 2020

eBook ISBN : 978-981-15-4218-3 Published: 11 June 2020

Series ISSN : 2367-3370

Series E-ISSN : 2367-3389

Edition Number : 1

Number of Pages : XXII, 523

Number of Illustrations : 98 b/w illustrations, 165 illustrations in colour

Topics : Computational Intelligence , Machine Learning , Communications Engineering, Networks

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A simple 'twist' improves the engine of clean fuel generation

Researchers have found a way to super-charge the 'engine' of sustainable fuel generation -- by giving the materials a little twist.

The researchers, led by the University of Cambridge, are developing low-cost light-harvesting semiconductors that power devices for converting water into clean hydrogen fuel, using just the power of the sun. These semiconducting materials, known as copper oxides, are cheap, abundant and non-toxic, but their performance does not come close to silicon, which dominates the semiconductor market.

However, the researchers found that by growing the copper oxide crystals in a specific orientation so that electric charges move through the crystals at a diagonal, the charges move much faster and further, greatly improving performance. Tests of a copper oxide light harvester, or photocathode, based on this fabrication technique showed a 70% improvement over existing state-of-the-art oxide photocathodes, while also showing greatly improved stability.

The researchers say their results, reported in the journal Nature , show how low-cost materials could be fine-tuned to power the transition away from fossil fuels and toward clean, sustainable fuels that can be stored and used with existing energy infrastructure.

Copper (I) oxide, or cuprous oxide, has been touted as a cheap potential replacement for silicon for years, since it is reasonably effective at capturing sunlight and converting it into electric charge. However, much of that charge tends to get lost, limiting the material's performance.

"Like other oxide semiconductors, cuprous oxide has its intrinsic challenges," said co-first author Dr Linfeng Pan from Cambridge's Department of Chemical Engineering and Biotechnology. "One of those challenges is the mismatch between how deep light is absorbed and how far the charges travel within the material, so most of the oxide below the top layer of material is essentially dead space."

"For most solar cell materials, it's defects on the surface of the material that cause a reduction in performance, but with these oxide materials, it's the other way round: the surface is largely fine, but something about the bulk leads to losses," said Professor Sam Stranks, who led the research. "This means the way the crystals are grown is vital to their performance."

To develop cuprous oxides to the point where they can be a credible contender to established photovoltaic materials, they need to be optimised so they can efficiently generate and move electric charges -- made of an electron and a positively-charged electron 'hole' -- when sunlight hits them.

One potential optimisation approach is single-crystal thin films -- very thin slices of material with a highly-ordered crystal structure, which are often used in electronics. However, making these films is normally a complex and time-consuming process.

Using thin film deposition techniques, the researchers were able to grow high-quality cuprous oxide films at ambient pressure and room temperature. By precisely controlling growth and flow rates in the chamber, they were able to 'shift' the crystals into a particular orientation. Then, using high temporal resolution spectroscopic techniques, they were able to observe how the orientation of the crystals affected how efficiently electric charges moved through the material.

"These crystals are basically cubes, and we found that when the electrons move through the cube at a body diagonal, rather than along the face or edge of the cube, they move an order of magnitude further," said Pan. "The further the electrons move, the better the performance."

"Something about that diagonal direction in these materials is magic," said Stranks. "We need to carry out further work to fully understand why and optimise it further, but it has so far resulted in a huge jump in performance." Tests of a cuprous oxide photocathode made using this technique showed an increase in performance of more than 70% over existing state-of-the-art electrodeposited oxide photocathodes.

"In addition to the improved performance, we found that the orientation makes the films much more stable, but factors beyond the bulk properties may be at play," said Pan.

The researchers say that much more research and development is still needed, but this and related families of materials could have a vital role in the energy transition.

"There's still a long way to go, but we're on an exciting trajectory," said Stranks. "There's a lot of interesting science to come from these materials, and it's interesting for me to connect the physics of these materials with their growth, how they form, and ultimately how they perform."

The research was a collaboration with École Polytechnique Fédérale de Lausanne, Nankai University and Uppsala University. The research was supported in part by the European Research Council, the Swiss National Science Foundation, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Sam Stranks is Professor of Optoelectronics in the Department of Chemical Engineering and Biotechnology, and a Fellow of Clare College, Cambridge.

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Materials provided by University of Cambridge . The original text of this story is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . Note: Content may be edited for style and length.

Journal Reference :

• Pan, L., Dai, L., Burton, O.J. et al. High carrier mobility along the [111] orientation in Cu2O photoelectrodes . Nature , 2024 DOI: 10.1038/s41586-024-07273-8

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