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Join the community, trending research, automated unit test improvement using large language models at meta.

Codium-ai/cover-agent • 14 Feb 2024

This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests.

Software Engineering

The Sky Above The Clouds

Technology ecosystems often undergo significant transformations as they mature.

Distributed, Parallel, and Cluster Computing

LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.

Sound Multimedia Audio and Speech Processing

rustworkx: A High-Performance Graph Library for Python

qiskit/rustworkx • 28 Oct 2021

In rustworkx, we provide a high-performance, flexible graph library for Python.

Data Structures and Algorithms E.1

FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry

hku-mars/fast-livo • 2 Mar 2022

The LIO subsystem registers raw points (instead of feature points on e. g., edges or planes) of a new scan to an incrementally-built point cloud map.

Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs

In this letter, we address the problem of enabling LLMs to comprehend Area Graph, a text-based map representation, in order to enhance their applicability in the field of mobile robotics.

PyOptInterface: Design and implementation of an efficient modeling language for mathematical optimization

metab0t/PyOptInterface • 16 May 2024

This paper introduces the design and implementation of PyOptInterface, a modeling language for mathematical optimization embedded in Python programming language.

Mathematical Software

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

morgancthomas/molscore • 22 Jun 2022

Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design.

Computational Engineering, Finance, and Science Biomolecules

Prompt Fuzzing for Fuzz Driver Generation

Crafting high-quality fuzz drivers not only is time-consuming but also requires a deep understanding of the library.

Cryptography and Security Software Engineering

CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking

Time delay neural network (TDNN) has been proven to be efficient for speaker verification.

Sound Audio and Speech Processing

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You should be reading academic computer science papers

You read documentation and tutorials to become a better programmer, but if you really want to be cutting-edge, academic research is where it's at.

[Ed. note: While we take some time to rest up over the holidays and prepare for next year, we are re-publishing our top ten posts for the year. This is our number one post of 2022! Thanks for reading and we’ll see you in the new year. ]

As working programmers, you need to keep learning all the time. You check out tutorials, documentation, Stack Overflow questions, anything you can find that will help you write code and keep your skills current. But how often do you find yourself digging into academic computer science papers to improve your programming chops?

While the tutorials can help you write code right now, it’s the academic papers that can help you understand where programming came from and where it’s going. Every programming feature, from the null pointer (aka the billion dollar mistake ) to objects (via Smalltalk ) has been built on a foundation of research that stretches back to the 1960s (and earlier). Future innovations will be built on the research of today.

We spoke to three of the members of the Papers We Love team, an online repository of their favorite computer science scholarship.

Zeeshan Lakhani, an engineering director at BlockFi, Darren Newton, an engineering team lead at Datadog, and David Ashby, a staff engineer at SageSure, all met while working at a company called Arc90. They found that none of them had formal training in computer science, but they all wanted to learn more. All three came from humanities and arts disciplines: Ashby has an English degree with a history minor, Newton went to art school twice, and Lakhani went to film school for undergrad before getting a master’s degree in music and audio engineering. All of those fields of study rely heavily on reading texts that built the foundation of the discipline as to understand the theory that underlies all practice.

Like any good student of the humanities, they went looking for answers in the archives. “I had a latent librarian inside,” said Newton. “So I'm always interested in the historical source material for the things that I do.”

Surveying history

As part of learning more about the history of programming, Ashby was reading Tracy Kidder’s Soul of a New Machine , about the race to design a 32-bit microcomputer in the late 70s. It covered both the engineering culture at the time and the problems and concepts those engineers wrestled with. This was before the time of mass-market CPUs and standard motherboard components, so a lot of what we take for granted today was still being worked out.

In Kidder’s book, Lakhani, Newton, and Ashby saw a whole history of computer science that they had no connection with, so they decided to try reading a foundational paper: Tony Hoare ’s “ Communicating Sequential Processes ” from 1978. They were working on Clojure and Clojurescript at the time, so this seemed relevant. When they sat down to discuss the paper, they realized they didn’t even know how to approach understanding it. “It was like, I can't understand half of this formalism, but maybe the intro is pretty good,” said Lakhani. “But we need someone like David Nolen to explain this to us.”

Nolen was an acquaintance who worked for The New York Times . He gave a talk there about Clojure and other Lisp-like languages, referencing a lot of John McCarthy’s early papers. Hearing this explanation with the academic context started turning a few gears in their minds. That’s when the idea of Papers We Love was born.

Knowing the history of the computing concepts that you use every day unlocks a lot of understanding into how they work at a practical level. The tools that you use, from databases to programming languages, are built on a foundation of academic research. “Understanding the roots of the things you're working on unlocks a lot of knowledge that you're not going to get purely just by using every day because you don't understand the paths that they didn't go down,” said Ashby.

There’s a talk they love that Bret Victor gave in 2013 called “ The Future of Programming .” He’s dressed like an engineer from the 70s, white button-up, khakis, pocket protector. He starts giving his talk using an overhead projector that has the name of the talk. He adjusts the slide and it reveals that the date is 1973. He goes on to talk about all the great things coming out of research, all the things that are going to shake up computer science. And they’re all things that the audience is still dealing with, like the move from sequential execution to concurrent models.

“The top theme was that it takes a long time,” said Lakhani. “There's a lot of things that are old that are new again, over and over and over.” The same problems are still relevant, whether because the problems are harder than once thought or because the research into those problems has been widely shared.

The trio behind Papers We Love aren’t alone in discovering a love for computing’s history. There is an increased interest in retrocomputing , engineers looking at the systems of the past to learn more about the practice of technology. It’s the flipside of looking at older papers; you look at the old hardware and software programmers used and work on it with a present-day mindset. “A lot of people are spinning up these ancient operating systems on Raspberry PIs and working with them,” said Newton. “Like spinning up an old Smalltalk VM on a Raspberry PI or recreating a PDP-10.”

When you see these issues in their initial contexts, like reading the research papers that tried to address them, you can get a better perspective on where you are now. That can lead to all sorts of epiphanies. “Oh, objects do the things they do because of Smalltalk back in the 80s,” said Ashby. “And that's why big systems look like that. And that's why Java looks like that.”

That new understanding can help you solve the problems that you face now.

The future of programming (today)

There’s more to reading research papers than understanding history; you can find new ways to solve problems by reading current research. “The idea of Stack Overflow is: someone else has had your problem before,” said Ashby. “Academic papers are: someone else has thought about this problem before.”

If your work involves building variations of the same old CRUD app in new spaces, then maybe research papers won’t help you. But if you are trying to solve the unique problems of your industry, then some of the research in those problem spaces may help you overcome them. “I find papers to expand the idea of what's possible with the work you do,” said Ashby. “They can help you appreciate that there are other ways to solve these problems.”

For Newton and his colleagues at Datadog, academic papers are an integral part of their work. Their monitoring software has to process a lot of information in real time to give engineers a view of their applications and the stack they run on. “We are very concerned with performance algorithms and better ways to do statistics on large volumes of data ,” said Newton. “We need to rely on academic research for some of that.”

Just because research exists, of course, it doesn’t mean your problems are automatically solved. Sometimes a single paper only gets you part of the solution. “I was at Comcast where we wanted to leverage load balancing work that we do in terms of routing,” said Lakhani. “We ended up applying three different kinds of papers that didn't know each other. We put semantics into network packets, routed them based on another paper via a specific protocol, and implemented a bunch of IETF specs. Part of this work now lives in a Rust library people can run today.” It's finding threads in academic work and braiding them together to solve the problems at hand.

Without reading those papers, Lakhani’s team wouldn’t have been able to design such an effective solution. Perhaps they would have gotten there on their own. But imagine the amount of work to research those three concepts; there’s no need to redo their work if it’s already been done. It’s standing on the shoulders of giants, as the saying goes, and if you’re on top of the research in your field, you know exactly which giants to stand on.

A map of the giants’ shoulders

Naturally, being a graduate of the humanities myself, I wanted to know which were the giants of computer science, those papers that would be on the syllabus if you were to construct a humanities-style curricula for a class. Think of it as a map of which giant shoulders you could stand on to get ahead.

It turns out, I’m not the first to wonder what’s in the computer science canon. In 1996, Phillip Laplante wrote Great Papers in Computer Science , which might be a bit outdated at this point. For a more recent take on the same thing, the trio recommend Ideas That Created the Future , published last year. Lakhani, who is now doing a PhD in computer science at Carnegie Mellon University (my alma mater), points out that there was a course when he arrived that covered the important papers of the field.

In a way, this canon is exactly what the Papers We Love repo aims to create. It contains papers and links to papers organized by topic. The group welcomes new pull requests with academic papers that you all love and want to see spotlighted.

Here are a few papers (and talks) that they recommended to anyone wanting to get started reading the research:

• Dynamo: Amazon’s Highly Available Key-value Store
• A Unified Theory of Garbage Collection
• Communicating Sequential Processes
• Out of the Tar Pit

Of course, there are many more.

If you’re intimidated by starting on a paper, then check out some of Papers We Love’s presentations , which offer a primer on how to understand a paper. The whole idea of these talks is borne out of that first frustration with a paper, then finding a path through it with someone else’s help. “They've gotten the CliffsNotes,” says Lakhani. “Now they can attack the paper and really understand it.”

The Papers We Love community continues to try to build a bridge between industry and academia. Everyone benefits—the industry gets access to new solutions without having to wait for someone else to implement and open-source them, and academics get to see their ideas tested and implemented in real situations.

“One of the goals of Papers We Love is to make it where you find out about stuff a little bit faster,” said Lakhani. “Maybe that changes things.”

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Title: lexmatcher: dictionary-centric data collection for llm-based machine translation.

Abstract: The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation. However, the area of data collection for instruction fine-tuning in machine translation remains relatively underexplored. In this paper, we present LexMatcher, a simple yet effective method for data collection that leverages bilingual dictionaries to generate a dataset, the design of which is driven by the coverage of senses found in these dictionaries. The dataset comprises a subset retrieved from an existing corpus and a smaller synthesized subset which supplements the infrequent senses of polysemous words. Utilizing LLaMA2 as our base model, our approach outperforms the established baselines on the WMT2022 test sets and also exhibits significant performance improvements in tasks related to word sense disambiguation and specialized terminology translation. These results underscore the effectiveness of LexMatcher in enhancing LLM-based machine translation.

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Research trend prediction in computer science publications: a deep neural network approach

• Published: 20 January 2022
• Volume 127 , pages 849–869, ( 2022 )

Cite this article

• Soroush Taheri 1 &
• Sadegh Aliakbary   ORCID: orcid.org/0000-0001-5773-1136 1  

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Thousands of research papers are being published every day, and among all these research works, one of the fastest-growing fields is computer science (CS). Thus, learning which research areas are trending in this particular field of study is advantageous to a significant number of scholars, research institutions, and funding organizations. Many scientometric studies have been done focusing on analyzing the current CS trends and predicting future ones from different perspectives as a consequence. Despite the large datasets from this vast number of CS publications and the power of deep learning methods in such big data problems, deep neural networks have not yet been used to their full potential in this area. Therefore, the objective of this paper is to predict the upcoming years’ CS trends using long short-term memory neural networks. Accordingly, CS papers from 1940 and their corresponding fields of study from the microsoft academic graph dataset have been exploited for solving this research trend prediction problem. The prediction accuracy of the proposed method is then evaluated using RMSE and coefficient of determination ( R 2 ) metrics. The evaluations show that the proposed method outperforms the baseline approaches in terms of the prediction accuracy in all considered time periods. Subsequently, adopting the proposed method’s predictions, we investigate future trending areas in computer science research from various viewpoints.

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Taheri, S., Aliakbary, S. Research trend prediction in computer science publications: a deep neural network approach. Scientometrics 127 , 849–869 (2022). https://doi.org/10.1007/s11192-021-04240-2

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DOI : https://doi.org/10.1007/s11192-021-04240-2

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• Published: 26 June 2023

GREENER principles for environmentally sustainable computational science

• Loïc Lannelongue   ORCID: orcid.org/0000-0002-9135-1345 1 , 2 , 3 , 4 ,
• Hans-Erik G. Aronson   ORCID: orcid.org/0000-0002-1702-1671 5 ,
• Alex Bateman 6 ,
• Ewan Birney 6 ,
• Talia Caplan   ORCID: orcid.org/0000-0001-8990-1435 7 ,
• Martin Juckes   ORCID: orcid.org/0000-0003-1770-2132 8 ,
• Johanna McEntyre 6 ,
• Andrew D. Morris 5 ,
• Gerry Reilly 5 &
• Michael Inouye 1 , 2 , 3 , 4 , 9 , 10 , 11  

Nature Computational Science volume  3 ,  pages 514–521 ( 2023 ) Cite this article

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The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

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Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Green chemistry as just chemistry

Estimating a social cost of carbon for global energy consumption

Scientific research and development have transformed and immeasurably improved the human condition, whether by building instruments to unveil the mysteries of the universe, developing treatments to fight cancer or improving our understanding of the human genome. Yet, science can, and frequently does, impact the environment, and the magnitude of these impacts is not always well understood. Given the connection between climate change and human health, it is becoming increasingly apparent to biomedical researchers in particular, as well as their funders, that the environmental effects of research should be taken into account 1 , 2 , 3 , 4 , 5 .

Recent studies have begun to elucidate the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories 6 . The 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 metric tonnes of CO 2 equivalent (tCO 2 e), equivalent to the average weekly emissions of the city of Edinburgh, UK 7 (CO 2 e, or CO 2 -equivalent, summarizes the global warming impacts of a range of greenhouse gases (GHGs) and is the standard metric for carbon footprints, although its accuracy is sometimes debated 8 ) The annual meeting of the Society for Neuroscience was estimated to emit 22,000 tCO 2 e, approximately the annual carbon footprint of 1,000 medium-sized laboratories 9 . The life-cycle impact (including construction and usage) of university buildings has been estimated at ~0.125 tCO 2 e m −2  yr −1 (ref. 10 ), and the yearly carbon footprint of a typical life-science laboratory at ~20 tCO 2 e (ref. 9 ). The Laboratory Efficiency Assessment Framework (LEAF) is a widely adopted standard to monitor and reduce the carbon footprint of laboratory-based research 11 . Other recent frameworks can help to raise awareness: GES 1point5 12 provides an open-source tool to estimate the carbon footprint of research laboratories and covers buildings, procurement, commuting and travel, and the Environmental Responsibility 5-R Framework provides guidelines for ecologically conscious research 13 .

With the increasing scale of high-performance and cloud computing, the computational sciences are susceptible to having silent and unintended environmental impacts. The sector of information and communication technologies (ICT) was responsible for between 1.8% and 2.8% of global GHG emissions in 2020 14 —more than aviation (1.9% 15 )—and, if unchecked, the ICT carbon footprint could grow exponentially in coming years 14 . Although the environmental impact of experimental ‘wet’ laboratories is more immediately obvious, with their large pieces of equipment and high plastic and reagent usage, the impact of algorithms is less clear and often underestimated. The risks of seeking performance at any cost and the importance of considering energy usage and sustainability when developing new hardware for high-performance computing (HPC) was raised as early as 2007 16 . Since then, continuous improvements have been made by developing new hardware, building lower-energy data centers and implementing more efficient HPC systems 17 , 18 . However, it is only in the past five years that these concerns have reached HPC users, in particular researchers. Notably, the field of artificial intelligence (AI) has first taken note of its environmental impacts, in particular those of the very large language models developed 19 , 20 , 21 , 22 , 23 . It is unclear, however, to what extent this has led the field towards more sustainable research practices. A small number of studies have also been performed in other fields, including bioinformatics 24 , astronomy and astrophysics 25 , 26 , 27 , 28 , particle physics 29 , neuroscience 30 and computational social sciences 31 . Health data science is starting to address the subject, but a recent systematic review found only 25 publications in the field over the past 12 years 32 . In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centers’ water usage and land footprint 33 . Notably, computational science, in particular AI, has the potential to help fight climate change, for example, by improving the efficiency of wind farms, by facilitating low-carbon urban mobility and by better understanding and anticipating severe weather events 34 .

In this Perspective we highlight the nascent field of environmentally sustainable computational science (ESCS)—what we have learned from the research so far, and what scientists can do to mitigate their environmental impacts. In doing so, we present GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research; Fig. 1 ), a set of principles for how the computational science community could lead the way in sustainable research practices, maximizing computational science’s benefit to both humanity and the environment.

The GREENER principles enable cultural change (blue arrows), which in turn facilitates their implementation (green arrows) and triggers a virtuous circle.

Environmental impacts of the computational sciences

The past three years have seen increased concerns regarding the carbon footprint of computations, and only recently have tools 21 , 35 , 36 , 37 and guidelines 38 been widely available to computational scientists to allow them to estimate their carbon footprint and be more environmentally sustainable.

Most calculators that estimate the carbon footprint of computations are targeted at machine learning tasks and so are primarily suited to Python pipelines, graphics processing units (GPUs) and/or cloud computing 36 , 37 , 39 , 40 . Python libraries have the benefit of integrating well into machine learning pipelines or online calculators for cloud GPUs 21 , 41 . Recently, a flexible online tool, the Green Algorithms calculator 35 , enabled the estimation of the carbon footprint for nearly any computational task, empowering sustainability metrics across fields, hardware, computing platforms and locations.

Some publications, such as ref. 38 , have listed simple actions that computational scientists can take regarding their environmental impact, including estimating the carbon footprint of running algorithms, both a posteriori to acknowledge the impact of a project and before starting as part of a cost–benefit analysis. A 2020 report from The Royal Society formalizes this with the notion of ‘energy proportionality’, meaning the environmental impacts of an innovation must be outweighed by its environmental or societal benefits 34 . It is also important to minimize electronic waste by keeping devices for longer and using second-hand hardware when possible. A 2021 report by the World Health Organization 42 warns of the dramatic effect of e-waste on population health, particularly children. The unregulated informal recycling industry, which handles more than 80% of the 53 million tonnes of e-waste, causes a high level of water, soil and air pollution, often in low- and middle-income countries 43 . The up to 56 million informal waste workers are also exposed to hazardous chemicals such as heavy metals and persistent organic pollutants 42 . Scientists can also choose energy-efficient hardware and computing facilities, while favoring those powered by green energy. Writing efficient code can substantially reduce the carbon footprint as well, and this can be done alongside making hardware requirements and carbon footprints clear when releasing new software. The Green Software Foundation ( https://greensoftware.foundation ) promotes carbon-aware coding to reduce the operational carbon footprint of the softwares used in all aspects of society. There is, however, a rebound effect to making algorithms and hardware more efficient: instead of reducing computing usage, increased efficiency encourages more analyses to be performed, which leads to a revaluation of the cost–benefit but often results in increased carbon footprints. The rebound effect is a key example of why research practice should adapt to technological advances so that they lead to carbon footprint reductions.

GREENER computational science

ESCS is an emerging field, but one that is of rapidly increasing importance given the climate crisis. In the following, our proposed set of principles (Fig. 1 ) outlines the main axes where progress is needed, where opportunities lie and where we believe efforts should be concentrated.

Governance and responsibility

Everyone involved in computational science has a role to play in making the field more sustainable, and many do already, from grassroots movements to large institutions. Individual and institutional responsibility is a necessary step to ensure transparency and reduction of GHG emission. Here we highlight key stakeholders alongside existing initiatives and future opportunities for involvement.

Grassroots initiatives led by graduate students, early career researchers and laboratory technicians have shown great success in tackling the carbon footprint of laboratory work, including Green Labs Netherlands 44 , the Nottingham Technical Sustainability Working Group or the Digital Humanities Climate Coalition 45 . International coalitions such as the Sustainable Research (SuRe) Symposium, initially set up for wet laboratories, have started to address the impact of computing as well. IT teams in HPC centers are naturally key, both in terms of training and ensuring that the appropriate information is logged so that scientists can follow the carbon footprints of their work. Principal investigators can encourage their teams to think about this issue and provide access to suitable training when needed.

Simultaneously, top–down approaches are needed, with funding bodies and journals occupying key positions in both incentivizing carbon-footprint reduction and in promoting transparency. Funding bodies can directly influence the researchers they fund and those applying for funding via their funding policies. They can require estimates of carbon footprints to be included in funding applications as part of ‘environmental impacts statements’. Many funding bodies include sustainability in their guidelines already; see, for example, the UK’s NIHR carbon reduction guidelines 1 , the brief mention of the environment in UKRI’s terms and conditions 46 , and the Wellcome Trust’s carbon-offsetting travel policy 47 .

Although these are important first steps, bolder action is needed to meet the urgency of climate change. For example, UKRI’s digital research infrastructure scoping project 48 , which seeks to provide a roadmap to net zero for its digital infrastructure, sends a clear message that sustainable research includes minimizing the GHG emissions from computation. The project not only raises awareness but will hopefully result in reductions in GHG emissions.

Large research institutes are key to managing and expanding centralized data infrastructures and trusted research environments (TREs). For example, EMBL’s European Bioinformatics Institute manages more than 40 data resources 49 , including AlphaFold DB 50 , which contains over 200,000,000 predicted protein structures that can be searched, browsed and retrieved according to the FAIR principles (findable, accessible, interoperable, reusable) 51 . As a consequence, researchers do not need to run the carbon-intensive AlphaFold algorithm for themselves and instead can just query the database. AlphaFold DB was queried programmatically over 700 million times and the web page was accessed 2.4 million times between August 2021 and October 2022. Institutions also have a role in making procurement decisions carefully, taking into account both the manufacturing and operational footprint of hardware purchases. This is critical, as the lifetime footprint of a computational facility is largely determined by the date it is purchased. Facilities could also better balance investment decisions, with a focus on attracting staff based on sustainable and efficient working environments, rather than high-powered hardware 52 .

However, increases in the efficiencies of digital technology alone are unlikely to prove sufficient in ensuring sustainable resource use 53 . Alongside these investments, funding bodies should support a shift towards more positive, inclusive and green research cultures, recognizing that more data or bigger models do not always translate into greater insights and that a ‘fit for purpose’ approach can ultimately be more efficient. Organizations such as Health Data Research UK and the UK Health Data Research Alliance have a key convening role in ensuring that awareness is raised around the climate impact of both infrastructure investment and computational methods.

Journals may incentivize authors to acknowledge and indeed estimate the carbon footprint of the work presented. Some authors already do this voluntarily (for example, refs. 54 , 55 , 56 , 57 , 58 , 59 ), mostly in bioinformatics and machine learning so far, but there is potential to expand it to other areas of computational science. In some instances, showing that a new tool is greener can be an argument in support of a new method 60 .

International societies in charge of organizing annual conferences may help scientists reduce the carbon footprint of presenting their work by offering hybrid options. The COVID-19 pandemic boosted virtual and hybrid meetings, which have a lower carbon footprint while increasing access and diversity 7 , 61 . Burtscher and colleagues found that running the annual meeting of the European Astronomical Society online emitted >3,000-fold less CO 2 e than the in-person meeting (0.582 tCO 2 e compared to 1,855 tCO 2 e) 25 . Institutions are starting to tackle this; for example, the University of Cambridge has released new travel guidelines encouraging virtual meetings whenever feasible and restricting flights to essential travel, while also acknowledging that different career stages have different needs 62 .

Industry partners will also need to be part of the discussion. Acknowledging and reducing computing environmental impact comes with added challenges in industry, such as shareholder interests and/or public relations. While the EU has backed some initiatives helping ICT-reliant companies to address their carbon footprint, such as ICTfootprint.eu, other major stakeholders have expressed skepticism regarding the environmental issues of machine learning models 63 , 64 . Although challenging, tech industry engagement and inclusion is nevertheless essential for tackling GHG emissions.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations is an essential step towards sustainable research as it identifies inefficiencies and opportunities for improvement. User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. In some HPC situations, particularly in academia, the financial cost of running computations is negligible and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Although progress has been made in estimating energy usage and carbon footprints over the past few years, there are still barriers that prevent the routine estimation of environmental impacts. From task-agnostic, general-purpose calculators 35 and task-specific packages 36 , 37 , 65 to server-side softwares 66 , 67 , each estimation tool is a trade-off between ease of use and accuracy. A recent primer 68 discusses these different options in more detail and provides recommendations as to which approach fits a particular need.

Regardless of the calculator used, for these tools to work effectively and for scientists to have an accurate representation of their energy consumption, it is important to understand the power management for different components. For example, the power usage of processing cores such as central processing units (CPUs) and GPUs is not a readily available metric; instead, thermal design power (meaning, how much heat the chip can be expected to dissipate in a normal setting) is used. Although an acceptable approximation, it has also been shown to substantially underestimate power usage in some situations 69 . The efficiency of data centers is measured by the power usage effectiveness (PUE), which quantifies how much energy is needed for non-computing tasks, mainly cooling (efficient data centers have PUEs close to 1). This metric is widely used, with large cloud providers reporting low PUEs (for example, 1.11 for Google 70 compared to a global average of 1.57 71 ), but discrepancies in how it is calculated can limit PUE interpretation and thus its impact 72 , 73 , 74 . A standard from the International Organization for Standardization is trying to address this 75 . Unfortunately, the PUE of a particular data center, whether cloud or institutional, is rarely publicly documented. Thus, an important step is the data science and infrastructure community making both hardware and data centers’ energy consumption metrics available to their users and the public. Ultimately, tackling unnecessary carbon footprints will require transparency 34 .

Tackling energy and embodied impacts through new collaborations

Minimizing carbon intensity (meaning the carbon footprint of producing electricity) is one of the most immediately impactful ways to reduce GHG emissions. Carbon intensities depend largely on geographical location, with up to three orders of magnitude between the top and bottom performing high-income countries in terms of low carbon energies (from 0.10 gCO 2 e kWh −1 in Iceland to 770 gCO 2 e kWh −1 in Australia 76 ). Changing the carbon intensity of a local state or national government is nearly always impractical as it would necessitate protracted campaigns to change energy policies. An alternative is to relocate computations to low-carbon settings and countries, but, depending on the type of facility or the sensitivity of the data, this may not always be possible. New inter-institutional cooperation may open up opportunities to enable access to low-carbon data centers in real time.

It is, however, essential to recognize and account for inequalities between countries in terms of access to green energy sources. International cooperation is key to providing scientists from low- and middle-income countries (LMICs), who frequently only have high-carbon-intensity options available to them, access to low-carbon computing infrastructures for their work. In the longer term, international partnerships between organizations and nations can help build low-carbon computing capacity in LMICs.

Furthermore, the footprint of user devices should not be forgotten. In one estimate, the energy footprint of streaming a video to a laptop is mainly on the laptop (72%), with 23% used in transmission and a mere 5% at the data center 77 . Zero clients (user devices with no compute or storage capacity) can be used in some research use cases and drastically reduce the client-side footprint 78 .

It can be tempting to reduce the environmental impacts of computing to electricity needs, as these are the easiest ones to estimate. However, water usage, ecological impacts and embodied carbon footprints from manufacturing should also be addressed. For example, for personal hardware, such as laptops, 70–80% of the life-cycle impact of these devices comes from manufacturing only 79 , as it involves mining raw materials and assembling the different components, which require water and energy. Moreover, manufacturing often takes place in countries that have a higher carbon intensity for power generation and a slower transition to zero-carbon power 80 . Currently, hardware renewal policies, either for work computers or servers in data centers, are often closely dependent on warranties and financial costs, with environmental costs rarely considered. For hardware used in data centers, regular updates may be both financially and environmentally friendly, as efficiency gains may offset manufacturing impacts. Estimating these environmental impacts will allow HPC teams to know for sure. Reconditioned and remanufactured laptops and servers are available, but growth of this sector is currently limited by negative consumer perception 81 . Major suppliers of hardware are making substantial commitments, such as 100% renewable energy supply by 2030 82 or net zero by 2050 83 .

Another key consideration is data storage. Scientific datasets are now measured in petabytes (PB). In genomics, the popular UK Biobank cohort 84 is expected to reach 15 PB by 2025 85 , and the first image of a black hole required the collection of 5 PB of data 86 . The carbon footprint of storing data depends on numerous factors, but based on some manufacturers’ estimations, the order of magnitude of the life-cycle footprint of storing 1 TB of data for a year is ~10 kg CO 2 e (refs. 87 , 88 ). This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Centralized and collaborative computing resources (such as TREs) holding both data and computing hardware may help alleviate redundant resources. TRE efforts in the UK span both health (for example, NHS Digital 89 ) and administrative data (for example, the SAIL databank on the UK Secure Research Platform 90 and the Office for National Statistics Secure Research Service 91 ). Large (hyperscale) data centers are expected to be more energy-efficient 92 , but they may also encourage unnecessary increases in the scale of computing (rebound effect).

The importance of dedicated education and research efforts for ESCS

Education is essential to raise awareness with different stakeholders. In lieu of incorporating some aspects into more formal undergraduate programs, integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. An example is the ‘Green Computing’ Workshop on Education at the 2022 conference on Intelligent Systems for Molecular Biology.

Investing in research that will catalyze innovation in the field of ESCS is a crucial role for funders and institutions to play. Although global data centers’ workloads have increased more than sixfold between 2010 and 2018, their total electricity usage has been approximately stable due to the use of power-efficient hardware 93 , but environmentally sustainable investments will be needed to perpetuate this trend. Initiatives like Wellcome’s Research Sustainability project 94 , which look to highlight key gaps where investment could deliver the next generation of ESCS tools and technology, are key to ensuring that growth in energy demand beyond current efficiency trends can be managed in a sustainable way. Similarly, the UKRI Data and Analytics Research Environments UK program (DARE UK) needs to ensure that sustainability is a key evaluation criterion for funding and infrastructure investments for the next generation of TREs.

Recent studies found that the most widely used programming languages in research, such as R and Python 95 , tend to be the least energy-efficient ones 96 , 97 , and, although it is unlikely that forcing the community to switch to more efficient languages would benefit the environment in the short term (due to inefficient coding for example), this highlights the importance of having trained research software engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints. Algorithms also come with high memory requirements, sometimes using more energy than processors 98 . Unfortunately, memory power usage remains poorly optimized, as speed of access is almost always favored over energy efficiency 99 . Providing users and software engineers with the flexibility to opt for energy efficiency would present an opportunity for a reduction in GHG emissions 100 , 101 .

Cultural change

In parallel to the technological reductions in energy usage and carbon footprints, research practices will also need to change to avoid rebound effects 38 . Similar to the aviation industry, there is a tendency to count on technology to solve sustainability concerns without having to change usage 102 (that is, waiting on computing to become zero-carbon rather than acting on how we use it). Cultural change in the computing community to reconsider how we think about computing costs will be necessary. Research strategies at all levels will need to consider environmental impacts and corresponding approaches to carbon footprint minimization. The upcoming extension of the LEAF standard for computational laboratories will provide researchers with tangible tools to do so. Day to day, there is a need to solve trade-offs between the speed of computation, accuracy and GHG emissions, keeping in mind the goal of GHG reduction. These changes in scientific practices are challenging, but, importantly, there are synergies between open computational science and green computing 103 . For example, making code, data and models FAIR so that other scientists avoid unnecessary computations can increase the reach and impact of a project. FAIR practices can result in highly efficient code implementations, reduce the need to retrain models, and reduce unnecessary data generation/storage, thus reducing the overall carbon footprint. As a result, green computing and FAIR practices may both stimulate innovation and reduce financial costs.

Moreover, computational science has downstream effects on carbon footprints in other areas. In the biomedical sciences, developments in machine learning and computer vision impact the speed and scale of medical imaging processing. Discoveries in health data science make their way to clinicians and patients through, for example, connected devices. In each of these cases and many others, environmental impacts propagate through the whole digital health sector 32 . Yet, here too synergies exist. In many cases, such as telemedicine, there may be a net benefit in terms of both carbon and patient care, provided that all impacts have been carefully accounted for. These questions are beginning to be tackled in medicine, such as assessments of the environmental impact of telehealth 104 or studies into ways to sustainably handle large volumes of medical imaging data 105 . For the latter, NHS Digital (the UK’s national provider of information, data and IT systems for health and social care) has released guidelines to this effect 106 . Outside the biomedical field, there are immense but, so far, unrealized opportunities for similar efforts.

The computational sciences have an opportunity to lead the way in sustainability, which may be achieved through the GREENER principles for ESCS (Fig. 1 ): Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. This will require more transparency on environmental impacts. Although some tools already exist to estimate carbon footprints, more specialized ones will be needed alongside a clearer understanding of the carbon footprint of hardware and facilities, as well as more systematic monitoring and acknowledgment of carbon footprints. Measurement is a first step, followed by a reduction in GHG emissions. This can be achieved with better training and sensible policies for renewing hardware and storing data. Cooperation, open science and equitable access to low-carbon computing facilities will also be crucial 107 . Computing practices will need to adapt to include carbon footprints in cost–benefit analyses, as well as consider the environmental impacts of downstream applications. The development of sustainable solutions will need particularly careful consideration, as they frequently have the least benefit for populations, often in LMICs, who suffer the most from climate change 22 , 108 . All stakeholders have a role to play, from funding bodies, journals and institutions to HPC teams and early career researchers. There is now a window of time and an immense opportunity to transform computational science into an exemplar of broad societal impact and sustainability.

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Acknowledgements

L.L. was supported by the University of Cambridge MRC DTP (MR/S502443/1) and the BHF program grant (RG/18/13/33946). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). This work was supported by core funding from the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland) and the British Heart Foundation and Wellcome.

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Lannelongue, L., Aronson, HE.G., Bateman, A. et al. GREENER principles for environmentally sustainable computational science. Nat Comput Sci 3 , 514–521 (2023). https://doi.org/10.1038/s43588-023-00461-y

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Facility for Rare Isotope Beams

At michigan state university, first direct measurement of beryllium-7 electron-capture reported in physical review c paper.

In a recent paper published by  Physical Review C (“First direct  7 Be electron-capture  Q -value measurement toward high-precision searches for neutrino physics beyond the Standard Model”), scientists from Central Michigan University , McGill University , TRIUMF , Colorado School of Mines , and FRIB reported the first direct measurement of the beryllium-7 electron-capture decay value using Penning trap mass spectrometry (PTMS). 

The experiment was performed using the Low Energy Beam and Ion Trap (LEBIT) Penning trap at FRIB and utilized the Batch-Mode Ion-Source (BMIS) to deliver the unstable beryllium-7 samples. 

The result of the experiment yielded a measurement three times more precise than any previous determination. The scientists noted that the improved precision and accuracy of the beryllium-7 decay value is critical for ongoing experiments that measure the recoiling nucleus in this system as a signature to search for beyond the Standard Model neutrino physics using beryllium-7-doped superconducting sensors. 

The experiment extended the capabilities of LEBIT and used the first low-energy beam to be delivered by BMIS at FRIB for PTMS. It also measured the lightest-mass isotope at the time with LEBIT. 

This material is based upon work supported by the U.S. Department of Energy Office of Science Office of Nuclear Physics, the National Science Foundation, Michigan State University, the Facility for Rare Isotope Beams, Central Michigan University, and the Gordon and Betty Moore Foundation.

Michigan State University (MSU) operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. User facility operation is supported by the DOE-SC Office of Nuclear Physics as one of 28 DOE-SC user facilities.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science .

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Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

• RACE & ETHNICITY
• POLITICAL AFFILIATION

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

• Americans’ Social Media Use  Jan. 31, 2024
• Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

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