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Top 10 Must-Read Data Science Research Papers in 2022

Are you a data science enthusiast? If yes, then this Data Science Research Paper listing is for you

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The Research Papers Includes

Documentation matters: human-centered ai system to assist data science code documentation in computational notebooks, assessing the effects of fuel energy consumption, foreign direct investment and gdp on co2 emission: new data science evidence from europe & central asia, impact on stock market across covid-19 outbreak, exploring the political pulse of a country using data science tools, situating data science, veridicalflow: a python package for building trustworthy data science pipelines with pcs, from ai ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience, building an effective data science practice, detection of road traffic anomalies based on computational data science, data science data governance [ai ethics].

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Join the community, search results, data interpreter: an llm agent for data science.

1 code implementation • 28 Feb 2024

Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness.

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

3 code implementations • 20 Mar 2016

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.

Automating biomedical data science through tree-based pipeline optimization

1 code implementation • 28 Jan 2016

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.

Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

1 code implementation • 29 Jul 2016

In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.

Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows

1 code implementation • 30 Apr 2021

Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data.

Databases Human-Computer Interaction

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

2 code implementations • 12 Feb 2020

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.

Xorbits: Automating Operator Tiling for Distributed Data Science

1 code implementation • 29 Dec 2023

However, existing systems often struggle with processing large datasets due to Out-of-Memory (OOM) problems caused by poor data partitioning.

Distributed, Parallel, and Cluster Computing

Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization. The Journal of Financial Data Science

1 code implementation • Journal of Financial Data Science 2020

This is the second in a series of articles dealing with machine learning in asset management.

Syft 0.5: A Platform for Universally Deployable Structured Transparency

1 code implementation • 26 Apr 2021

We present Syft 0. 5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.

A generic framework for privacy preserving deep learning

4 code implementations • 9 Nov 2018

We detail a new framework for privacy preserving deep learning and discuss its assets.

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Data Science in Healthcare: COVID-19 and Beyond

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available ‘big’ data [ 1 ]. The recent advances in data science and AI have had a major impact on healthcare already, as can be seen in the recent biomedical literature [ 2 ]. Improved sharing and analysis of medical data results in earlier and better diagnoses, and more patient-tailored treatments. This increased data sharing, in combination with advances in health data management, works hand-in-hand with trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated healthcare delivery. Using data science and AI, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population level [ 3 ]. AI can be applied in all three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation [ 4 ]. ML algorithms can make predictions on how a disease will develop or respond to treatment, deep learning algorithms can find malignant tumors in magnetic resonance (MR) images and digital pathology images, and natural language-processing (NLP) algorithms can analyze unstructured documents with high speed and accuracy. These are just a few examples of what data science can do. This Special Issue focuses on how data science and AI are used in healthcare, and on related topics such as data sharing and data management. Since this Special Issue contains papers from 2020 to 2022, naturally there are a few papers about the COVID-19 pandemic: one on the determination of potential risk factors for the case fatality rate, one on the analysis of Arabic Twitter data to detect government pandemic measures and public concerns, and one on an enhanced sentinel surveillance system for outbreak prediction. There are also papers about data-sharing initiatives, depression treatment, the relationship between depression and metabolic status, cardiac thoracic pain, hand-foot-and-mouth disease infection, arteriovenous fistula (AVF) failure, chronic kidney disease (CKD) and breast cancer diagnosis.

“Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country” by Pan et al. [ 5 ], “COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data using Distributed Machine Learning” by Alomari et al. [ 6 ] and “Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network” by Bellocchio et al. [ 7 ] all present research around the COVID-19 pandemic. Pan et al. [ 5 ] identified 24 potential risk factors driving variation in SARS-CoV-2 case fatality rate (CFR). Their model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and the percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR. Alomari et al. [ 6 ] proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) ML and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. Using the tool, they collected a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February to 1 June 2020. They detected 15 government pandemic measures and public concerns, and six macro-concerns (economic sustainability, social sustainability, etc.), and formulated their information-structural, temporal, and spatio-temporal relationships. Bellocchio et al. [ 7 ] present a sentinel surveillance system supported by an ML prediction model, whereby the occurrence of COVID-19 cases in a clinic propagates distance-weighted risk estimates to adjacent dialysis units. The system allows for a prompt risk assessment and a timely response to the challenges posed by the COVID-19 epidemic throughout Fresenius Medical Care (FMC) European dialysis clinics.

“Sharing Is Caring-Data Sharing Initiatives in Healthcare” by Hulsen [ 8 ] shows an analysis of the current literature around data sharing, and discusses five aspects of data sharing in the medical domain, namely publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. With federated data, there is no need for a centralized source database (with all its privacy issues), because the algorithm is brought to the data instead of the other way around. The author also discusses some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

“Digital Training for Non-Specialist Health Workers to Deliver a Brief Psychological Treatment for Depression in Primary Care in India: Findings From a Randomized Pilot Study” by Muke et al. [ 9 ] evaluates the feasibility and acceptability of a digital program for training non-specialist health workers to deliver a brief psychological treatment for depression. This study, performed in Sehore (a rural district in Madhya Pradesh, India) adds to mounting efforts aimed at leveraging digital technology to increase the availability of evidence-based mental health services in low-resource primary care settings in.

“Association of Metabolically Healthy Obesity and Future Depression; Using National Health Insurance System Data in Korea from 2009–2017” by Seo et al. [ 10 ] investigates if depression and metabolic status are relevant by classifying them into the following four categories by their metabolic status and body mass index: (1) metabolically healthy non-obese (MHN); (2) metabolically healthy obese (MHO); (3) metabolically unhealthy non-obese (MUN); and (4) metabolically unhealthy obese (MUO). Their results show that the MHN ratio in women is higher than in men. In both men and women, depression incidence was the highest among MUO participants. In female participants, MHO is also related to a higher risk of depressive symptoms. This indicates that MHO is not an entirely benign condition in relation to depression in women. Therefore, reducing the number of metabolic syndrome and obesity patients in Korea will likely reduce the incidence of depression.

“Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico” by Rojas-Mendizabal et al. [ 11 ] aims to determine the correlated variables with thoracic pain of cardiac origin. Their analysis of 258 geriatric patients from Medical Norte Hospital in Tijuana (Baja California, Mexico) uses two ML techniques, i.e., tree classification and cross-validation. Their results suggest that among the main factors related to cardiac thoracic pain are dyslipidemia, chronic kidney failure, hypertension, diabetes, smoking habits, and troponin levels at the time of admission.

“Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model” by Lin et al. [ 12 ] discusses the high number of recent infections of hand-foot-and-mouth disease (HFMD). Previous research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of this disease is ignored. Existing early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to provide early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses ML methods to establish an HFMD epidemic prediction model with a high accuracy. Both incidence data and environmental (mostly weather) data are used.

“Development and Validation of a Machine Learning Model Predicting Arteriovenous Fistula Failure in a Large Network of Dialysis Clinics” by Ricardo et al. [ 13 ] derived and validated an arteriovenous fistula failure model (AVF-FM) based on ML. The model was trained in the derivation set (70% of initial cohort) by exploiting the information routinely collected in the Nephrocare European Clinical Database (EuCliD; 13,369 patients). Model performance was tested by concordance statistic and calibration charts in the remaining 30% of records. Feature importance was computed using the SHapley Additive exPlanations (SHAP) method. The model achieved good discrimination and calibration properties by combining routinely collected clinical and sensor data, requiring no additional effort by healthcare staff. Therefore, it can potentially facilitate risk-based personalization of AVF surveillance strategies.

In “Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD)” by Ricardo et al. [ 14 ] a novel algorithm predicting end-stage kidney disease (ESKD) is described, named PROGRES-CKD. This Naïve-Bayes classifier accurately predicts kidney failure onset among chronic kidney disease (CKD) patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows for the explicit assessment of prediction robustness in case of missing values. The algorithm may efficiently assist physicians’ prognostic reasoning in real-life applications.

Finally, Rasool et al. [ 15 ] discuss in “Improved Machine Learning-based Predictive Models for Breast Cancer Diagnosis” four different predictive models to improve breast-cancer diagnostic accuracy, as well as data exploratory techniques (DET) such as feature distribution, correlation, elimination and hyperparameter optimization. The Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets were used as input. They report a significant improvement in the models’ diagnostic capability with their DET. Therefore, the techniques can help to improve breast cancer diagnosis.

The manuscripts in this Special Issue give us only a brief overview of the wide use of data science in healthcare, and offer a glimpse into the future, where even faster computers and more advanced AI algorithms will make many more applications possible. For example, whereas many AI algorithms only use data from specific data types, this can be expanded to a combination of a wide range of patient-related (structured or unstructured) data, including clinical data, imaging data, digital pathology data, genomics data, data from wearables, and much more, to optimize the result for the patient. AI systems will not replace clinicians on a large scale, but rather will support their care for patients [ 16 ]. For example, AI can also be used to optimize the workflow in the hospital, or to create intelligent chatbots to help patients while reducing the workload for the clinicians. Furthermore, AI algorithms created in these times of COVID-19 might be of good use when managing similar pandemics in the future. It is probably safe to say that in ten years from now, there will not be a ‘Data Science in Healthcare’ Special Issue, because by that time almost everything in healthcare will be influenced by data science.

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

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Data Science is an interdisciplinary journal that addresses the development that data has become a crucial factor for a large number and variety of scientific fields. This journal covers aspects around scientific data over the whole range from data creation, mining, discovery, curation, modeling, processing, and management to analysis, prediction, visualization, user interaction, communication, sharing, and re-use. We are interested in general methods and concepts, as well as specific tools, infrastructures, and applications. The ultimate goal is to unleash the power of scientific data to deepen our understanding of physical, biological, and digital systems, gain insight into human social and economic behavior, and design new solutions for the future. The rising importance of scientific data, both big and small, brings with it a wealth of challenges to combine structured, but often siloed data with messy, incomplete, and unstructured data from text, audio, visual content such as sensor and weblog data. New methods to extract, transport, pool, refine, store, analyze, and visualize data are needed to unleash their power while simultaneously making tools and workflows easier to use by the public at large. The journal invites contributions ranging from theoretical and foundational research, platforms, methods, applications, and tools in all areas. We welcome papers which add a social, geographical, and temporal dimension to data science research, as well as application-oriented papers that prepare and use data in discovery research.

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Semantic Publishing Data Science encourages authors to provide (meta)data with formal semantics, as a step towards the vision of semantic publishing to integrate, combine, organize, and reuse scientific knowledge. Data Science plans to experiment with different such approaches, and we will announce more details soon.

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Tobias Kuhn VU University Amsterdam The Netherlands

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Victor de Boer VU University Amsterdam The Netherlands

Philip E. Bourne University of Virginia USA

Alison Callahan Stanford University USA

Thomas Chadefaux Trinity College Dublin Ireland

Christine Chichester Nestle Institute of Health Sciences Switzerland

Tim Clark University of Virginia USA

Oscar Corcho Universidad Politécnica de Madrid Spain

Gargi Datta SomaLogic USA

Brian Davis NUI Galway Ireland

Manisha Desai Stanford University USA

Emilio Ferrara University of Southern California USA

Pascale Gaudet SIB Swiss Institute of Bioinformatics Switzerland

Olivier Gevaert Stanford University USA

Yolanda Gil University of Southern California USA

Frank van Harmelen VU University Amsterdam The Netherlands

Rinke Hoekstra VU University Amsterdam The Netherlands

Robert Hoehndorf KAUST Saudi Arabia

Lawrence Hunter University of Colorado Denver USA

Toshiaki Katayama Database Center for Life Science Japan

Michael Krauthammer Yale University USA

Thomas Maillart UC Berkeley USA

Richard Mann Leeds University United Kingdom

Michael Mäs University of Groningen The Netherlands

Jamie McCusker RPI USA

Pablo Mendes IBM USA

Izabela Moise ETH Zurich Switzerland

Matjaz Perc University of Maribor Slovenia

Silvio Peroni University of Bologna Italy

Steve Pettifer Manchester United Kingdom

Evangelos Pournaras ETH Zurich Switzerland

Núria Queralt Rosinach The Scripps Research Institute USA

Jodi Schneider University of Illinois at Urbana-Champaign USA

Manik Sharma DAV University Jalandhar India

Ruben Verborgh Ghent University Belgium

Karin Verspoor University of Melbourne Australia

Mark Wilkinson UPM Madrid Spain

Olivia Woolley Meza ETH Zurich Switzerland

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Authors should closely follow the guidelines below before submitting a manuscript.

All papers have to be written in English.

Paper Types

Data Science is open for submissions of the following types:

• Research Papers : We accept as main category research papers that report on original research. Results previously published at conferences or workshops may be submitted as extended versions.
• Position Papers : We accept position papers presenting discussions and viewpoints around data science topics. These papers do not need an evaluation, but need to present relevant and novel discussion points in a thorough manner.
• Survey Papers : We also publish survey papers of the state of the art of topics central to the journal’s scope. Survey articles should be comprehensive and balanced, and should have the potential to become well-known introductory and overview texts.
• Resource Papers : Resource papers introduce and describe a resource of value for further research, including but not limited to datasets, benchmarks, software tools/frameworks/services, methodologies, and protocols.

By submitting your manuscript you agree that it will be made available on the journal website as a preprint, and it will remain available after acceptance or rejection together with the reviews. Removal of a manuscript during or after review is not possible.

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The following length limits apply for the different paper types:

• Research papers: 12,000 words
• Position papers: 8,000 words
• Survey papers: 16,000 words
• Reports: 5,000 words

Note that these word counts are not targets but maximum values. Papers may be significantly shorter. Exceptions for longer papers are possible if well justified (contact the editors-in-chief before submitting papers that exceed the stated word limits).

These word counts include the abstract, tables, and figure and table captions. Author lists and references, however, are not counted. Each figure counts for an additional 300 words.

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Any author included in the author list should have contributed significantly to the paper, and no person who has made a significant contribution should be omitted from the list of authors. Please read the  IOS Press authorship policy  for further information.

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We encourage authors to submit their papers in HTML. There are various tools and templates for that, such as RASH , dokieli , and Authorea .

The Research Articles in Simplified HTML (RASH) ( doc, paper ) is a markup language that restricts the use of HTML elements to only 32 elements for writing academic research articles. It is possible to includes also RDFa annotations within any element of the language and other RDF statements in Turtle, JSON-LD and RDF/XML format by using the appropriate tag script. Authors can start from this generic template , which can be also found in the convenient ZIP archive ZIP archive containing the whole RASH package. Alternatively, these guidelines for OpenOffice and Word explain how to write a scholarly paper by using the basic features available in OpenOffice Writer and Microsoft Word, in a way that it can be converted into RASH by means of the RASH Online Conversion Service ( ROCS ) ( src, paper ).

As a second alternative, dokieli is a client-side editor for decentralized article publishing in HTML+RDFa, annotations and social interactions, compliant with the Linked Research initiative. There are a variety of examples in the wild , including the LNCS and ACM author guidelines as templates.

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This is optional, but we would like to encourage you to provide semantic (meta-)data with your scientific papers, but unfortunately no accepted standards, best practices, or nice tools exist for that yet. We are working to fix this. In the meantime, and if you are a bit experienced with RDF, we are very happy to receive your RDFa-enriched paper or a submission with separate RDF statements. We are also happy to help you with that, if you are not experienced with RDF.

We hope to be able to provide more general and more user-friendly guidelines for semantic publishing in the near future.

All relevant data that were used or produced for conducting the work presented in a paper must be made FAIR and compliant with the PLOS data availability guidelines prior to submission. See in particular the list of recommended data repositories . (We might provide our own data availability guidelines in the future, but we borrow the excellent PLOS guidelines for now.) In a nutshell, data have to be made openly accessible and freely reusable via established institutions and standards, unless privacy concerns forbid such a publication. In any case, metadata have to be made publicly accessible and visible.

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Overall recommendation

The review of a paper should suggest one of the following overall recommendations:  

• Accept. The article is accepted as is, or only minor problems must be addressed by the authors that do not require another round of reviewing but can be verified by the editorial and publication team.
• Undecided. Authors must revise their manuscript to address specific concerns before a final decision is reached. A revised manuscript will be subject to second round of peer review in which the decision will be either Accept or Reject and no further review will be performed.
• Reject. The work cannot be published based on the lack of interest, lack of novelty, insufficient conceptual advance or major technical and/or interpretational problems.

The review should evaluate the paper with respect to the following criteria.

Significance:

• Does the work address an important problem within the research fields covered by the journal?

Background:

• Is the work appropriately based on and connected to the relevant related work?
• For research papers: Does the work provide new insights or new methods of a substantial kind?
• For position papers: Does the work provide a novel and potentially disruptive view on the given topic?
• For survey papers: Does the work provide an overview that is unique in its scope or structure for the given topic?

Technical quality:

• For research papers: Are the methods adequate for the addressed problem, are they correctly and thoroughly applied, and are their results interpreted in a sound manner?
• For position papers: Is the advocated position supported by sound and thorough arguments?
• For survey papers: Is the topic covered in a comprehensive and well balanced manner, are the covered approaches accurately described and compared, and are they placed in a convincing common framework?

Presentation:

• Are the text, figures, and tables of the work accessible, pleasant to read, clearly structured, and free of major errors in grammar or style?
• Is the length of the manuscript appropriate for what it presents?

Data availability:

• Are all used and produced data are openly available in established data repositories, as mandated by FAIR and the data availability guidelines ?

Summary and Comments

• Summary of paper in a few sentences
• Reasons to accept
• Reasons to reject
• Further comments (optional)

IOS Pre-press This journal publishes all its articles in the IOS Press Pre-Press module. By publishing articles ahead of print the latest research can be accessed much quicker. The pre-press articles are the corrected proof versions of the article and are published online shortly after the proof is created and author corrections implemented. Pre-press articles are fully citable by using the DOI number. As soon as the pre-press article is assigned to an issue, the final bibliographic information will be added. The pre-press version will then be replaced by the updated, final version.

Archiving Data Science deposits all published articles in trusted digital archiving services. These include CLOCKSS and the e-depot of the National Library of the Netherlands. This ensures that articles are preserved and always remain available and openly accessible to everyone.

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Data Science Peer Review Policy

Data Science relies on an open and transparent peer review process. Papers submitted to the journal are quickly pre-screened by the Editors-in-Chief and if deemed suitable for formal review they are immediately published as pre-prints on the journal’s website . Please visit our  reviewer guidelines  for further information about how to conduct a review.

Reasons to reject a paper in the pre-screening process could be because the work does not fall within the aims and scope, the writing is of poor quality, the instructions to authors were not followed or the presented work is not novel.

Papers that are suitable for review are posted on the journal's website and are publicly available. In addition to solicited reviews by members of the editorial board, public reviews and comments are welcome by any researcher and can be uploaded using the journal website. All reviews and responses from the authors are posted on the website as well. All involved reviewers and editors will be acknowledged in the final published version.

Reviewers are by default identified by name although all reviewers do have the option to remain anonymous. All review reports are made openly available under CC-BY licenses after a decision has been made for the submission (independent of whether the decision was accept or reject). In addition to solicited reviews, any researcher is welcome to submit additional reviews and comments for papers that are under review. Editors and non-anonymous reviewers will be mentioned in the published articles.

Each paper that undergoes peer review is assigned a handling editor who will be responsible for inviting reviewers to comment on the paper.

The reviewer of a paper is asked to submit one of the following overall recommendations:

• Accept . The article is accepted as is, or only minor problems must be addressed by the authors that do not require another round of reviewing but can be verified by the editorial and publication team.
• Undecided . Authors must revise their manuscript to address specific concerns before a final decision is reached. A revised manuscript will be subject to second round of peer review in which the decision will be either Accept or Reject and no further review will be performed.
• Reject . The work cannot be published based on the lack of interest, lack of novelty, insufficient conceptual advance or major technical and/or interpretational problems.

Reviewers are requested to evaluate a paper with respect to the following criteria:

• Significance . Does the work address an important problem within the research fields covered by the journal?
• Background . Is the work appropriately based on and connected to the relevant related work?
• Novelty . For research papers: Does the work provide new insights or new methods of a substantial kind? For position papers: Does the work provide a novel and potentially disruptive view on the given topic? For survey papers: Does the work provide an overview that is unique in its scope or structure for the given topic? For resource papers: Does the presented resource have significant unique features that can enable novel scientific work?
• Technical quality . For research papers: Are the methods adequate for the addressed problem, are they correctly and thoroughly applied, and are their results interpreted in a sound manner? For position papers: Is the advocated position supported by sound and thorough arguments? For survey papers: Is the topic covered in a comprehensive and well balanced manner, are the covered approaches accurately described and compared, and are they placed in a convincing common framework? For resource papers: Is the presented resource carefully designed and implemented following the relevant best practices, and does it provide sound evidence of its (potential for) reuse?
• Presentation . Are the text, figures, and tables of the work accessible, pleasant to read, clearly structured, and free of major errors in grammar or style?
• Length . Is the length of the manuscript appropriate for what it presents?
• Data availability . Are all used and produced data are openly available in established data repositories, as mandated by FAIR and the data availability guidelines ?

Finally, reviewers are asked to answer the following points:

Accept or reject decisions are made by the Editors-in-Chief, whose decision is final.

APCs Waived : Article processing charges (APCs) are waived for papers submitted to the open access Data Science  (DS) journal before Dec 31, 2022.

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Computer Science > Computer Vision and Pattern Recognition

Title: ferret-ui: grounded mobile ui understanding with multimodal llms.

Abstract: Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grounding, and reasoning capabilities. Given that UI screens typically exhibit a more elongated aspect ratio and contain smaller objects of interest (e.g., icons, texts) than natural images, we incorporate "any resolution" on top of Ferret to magnify details and leverage enhanced visual features. Specifically, each screen is divided into 2 sub-images based on the original aspect ratio (i.e., horizontal division for portrait screens and vertical division for landscape screens). Both sub-images are encoded separately before being sent to LLMs. We meticulously gather training samples from an extensive range of elementary UI tasks, such as icon recognition, find text, and widget listing. These samples are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model's reasoning ability, we further compile a dataset for advanced tasks, including detailed description, perception/interaction conversations, and function inference. After training on the curated datasets, Ferret-UI exhibits outstanding comprehension of UI screens and the capability to execute open-ended instructions. For model evaluation, we establish a comprehensive benchmark encompassing all the aforementioned tasks. Ferret-UI excels not only beyond most open-source UI MLLMs, but also surpasses GPT-4V on all the elementary UI tasks.

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Towards synergy: network facility development, whole process carbon reduction and pollution reduction, and regional disparities

• Research Article
• Published: 17 April 2024

Cite this article

• Xuefeng Zhang 1 , 2 ,
• Hui Sun 1 , 2 ,
• Xuechao Xia 1 , 2 ,
• Zedong Yang 1 , 2 &
• Shusen Zhu 1 , 2  

The in-depth implementation of the “Broadband China Strategy” is of great significance in promoting the synergistic governance of urban carbon reduction and pollution reduction. In this paper, based on the “Broadband China” pilot program implemented in China in 2014 as a quasi-natural experiment, the coupled synergy model is used to measure the carbon and pollution reduction synergy index based on the balanced panel data of 277 prefectural-level cities and above in China from 2006 to 2020, and the staggered and synthetic DID methods are applied to investigate the impact of the Broadband China strategy on carbon and pollution reduction synergy and its mechanism. strategy on carbon and pollution reduction synergy and its mechanism. The conclusions of the study show that (1) the Broadband China strategy significantly improves the synergistic governance of carbon reduction and pollution reduction. (2) The mechanism results show that Broadband China mainly realizes carbon and pollution synergistic governance by promoting source control and process innovation but does not have an effective mediating role in end-of-pipe treatment. (3) The results of heterogeneity analysis show that Broadband China weakens the traditional geographic advantage, narrows the carbon pollution synergistic governance gap at the national and regional levels, and significantly improves the regional carbon reduction and pollution reduction governance level. This paper examines the micro-mechanism of the Broadband China strategy on carbon pollution synergistic governance from the whole process of production activities, which provides a new perspective for the study of carbon pollution synergistic governance, and provides an empirical basis for carbon pollution synergistic governance in China.

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This research was supported by the National Natural Science Foundation of China (71963030), Xinjiang Social Science Foundation of China (21BJY050), and Major Projects of Science and Technology Ministry of China (SQ2021xjkk01800).

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Xuefeng Zhang, Hui Sun, Xuechao Xia, Zedong Yang & Shusen Zhu

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Xuefeng Zhang: conceptualization, formal analysis, methodology, writing—original draft, software. Hui Sun: funding acquisition. Xuechao Xia: supervision, project administration, review and editing. Zedong Yang: supervision, project administration, review and editing, Shusen Zhu: data curation, validation.

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Zhang, X., Sun, H., Xia, X. et al. Towards synergy: network facility development, whole process carbon reduction and pollution reduction, and regional disparities. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33271-4

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Facility for Rare Isotope Beams

At michigan state university, frib researchers lead team to merge nuclear physics experiments and astronomical observations to advance equation-of-state research, world-class particle-accelerator facilities and recent advances in neutron-star observation give physicists a new toolkit for describing nuclear interactions at a wide range of densities..

For most stars, neutron stars and black holes are their final resting places. When a supergiant star runs out of fuel, it expands and then rapidly collapses on itself. This act creates a neutron star—an object denser than our sun crammed into a space 13 to  18 miles wide. In such a heavily condensed stellar environment, most electrons combine with protons to make neutrons, resulting in a dense ball of matter consisting mainly of neutrons. Researchers try to understand the forces that control this process by creating dense matter in the laboratory through colliding neutron-rich nuclei and taking detailed measurements.

A research team—led by William Lynch and Betty Tsang at FRIB—is focused on learning about neutrons in dense environments. Lynch, Tsang, and their collaborators used 20 years of experimental data from accelerator facilities and neutron-star observations to understand how particles interact in nuclear matter under a wide range of densities and pressures. The team wanted to determine how the ratio of neutrons to protons influences nuclear forces in a system. The team recently published its findings in Nature Astronomy .

“In nuclear physics, we are often confined to studying small systems, but we know exactly what particles are in our nuclear systems. Stars provide us an unbelievable opportunity, because they are large systems where nuclear physics plays a vital role, but we do not know for sure what particles are in their interiors,” said Lynch, professor of nuclear physics at FRIB and in the Michigan State University (MSU) Department of Physics and Astronomy. “They are interesting because the density varies greatly within such large systems.  Nuclear forces play a dominant role within them, yet we know comparatively little about that role.” 

When a star with a mass that is 20-30 times that of the sun exhausts its fuel, it cools, collapses, and explodes in a supernova. After this explosion, only the matter in the deepest part of the star’s interior coalesces to form a neutron star. This neutron star has no fuel to burn and over time, it radiates its remaining heat into the surrounding space. Scientists expect that matter in the outer core of a cold neutron star is roughly similar to the matter in atomic nuclei but with three differences: neutron stars are much larger, they are denser in their interiors, and a larger fraction of their nucleons are neutrons. Deep within the inner core of a neutron star, the composition of neutron star matter remains a mystery. 

  “If experiments could provide more guidance about the forces that act in their interiors, we could make better predictions of their interior composition and of phase transitions within them. Neutron stars present a great research opportunity to combine these disciplines,” said Lynch.

Accelerator facilities like FRIB help physicists study how subatomic particles interact under exotic conditions that are more common in neutron stars. When researchers compare these experiments to neutron-star observations, they can calculate the equation of state (EOS) of particles interacting in low-temperature, dense environments. The EOS describes matter in specific conditions, and how its properties change with density. Solving EOS for a wide range of settings helps researchers understand the strong nuclear force’s effects within dense objects, like neutron stars, in the cosmos. It also helps us learn more about neutron stars as they cool.

“This is the first time that we pulled together such a wealth of experimental data to explain the equation of state under these conditions, and this is important,” said Tsang, professor of nuclear science at FRIB. “Previous efforts have used theory to explain the low-density and low-energy end of nuclear matter. We wanted to use all the data we had available to us from our previous experiences with accelerators to obtain a comprehensive equation of state.”   

Researchers seeking the EOS often calculate it at higher temperatures or lower densities. They then draw conclusions for the system across a wider range of conditions. However, physicists have come to understand in recent years that an EOS obtained from an experiment is only relevant for a specific range of densities. As a result, the team needed to pull together data from a variety of accelerator experiments that used different measurements of colliding nuclei to replace those assumptions with data. “In this work, we asked two questions,” said Lynch. “For a given measurement, what density does that measurement probe? After that, we asked what that measurement tells us about the equation of state at that density.”   

In its recent paper, the team combined its own experiments from accelerator facilities in the United States and Japan. It pulled together data from 12 different experimental constraints and three neutron-star observations. The researchers focused on determining the EOS for nuclear matter ranging from half to three times a nuclei’s saturation density—the density found at the core of all stable nuclei. By producing this comprehensive EOS, the team provided new benchmarks for the larger nuclear physics and astrophysics communities to more accurately model interactions of nuclear matter.

The team improved its measurements at intermediate densities that neutron star observations do not provide through experiments at the GSI Helmholtz Centre for Heavy Ion Research in Germany, the RIKEN Nishina Center for Accelerator-Based Science in Japan, and the National Superconducting Cyclotron Laboratory (FRIB’s predecessor). To enable key measurements discussed in this article, their experiments helped fund technical advances in data acquisition for active targets and time projection chambers that are being employed in many other experiments world-wide.   

In running these experiments at FRIB, Tsang and Lynch can continue to interact with MSU students who help advance the research with their own input and innovation. MSU operates FRIB as a scientific 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. FRIB is the only accelerator-based user facility on a university campus as one of 28 DOE-SC user facilities .  Chun Yen Tsang, the first author on the Nature Astronomy  paper, was a graduate student under Betty Tsang during this research and is now a researcher working jointly at Brookhaven National Laboratory and Kent State University. 

“Projects like this one are essential for attracting the brightest students, which ultimately makes these discoveries possible, and provides a steady pipeline to the U.S. workforce in nuclear science,” Tsang said.

The proposed FRIB energy upgrade ( FRIB400 ), supported by the scientific user community in the 2023 Nuclear Science Advisory Committee Long Range Plan , will allow the team to probe at even higher densities in the years to come. FRIB400 will double the reach of FRIB along the neutron dripline into a region relevant for neutron-star crusts and to allow study of extreme, neutron-rich nuclei such as calcium-68. 

Eric Gedenk is a freelance science writer.

Michigan State University 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. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

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.