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4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

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BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
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CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
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Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
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Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
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Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
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RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

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Scholarly articles are articles written by experts and researchers in a field of study to educate or share new discoveries and research. You can use scholarly articles to find out about new innovations, research methodologies, and to dive more deeply into understanding the themes and subtopics of your field of research.

Other names: Scholarly articles are sometimes also called peer-reviewed articles or academic articles. However, not all scholarly articles are peer reviewed.

How to identify a scholarly article:

• Investigate the publisher. Most scholarly articles are published in peer-reviewed journals, e.g.  Strategic Management Journal  or  Journal of Labor Economics
• Browse the structure of the article. Scholarly articles often contain multiples sections and have headers such as introduction, literature review, methodology, discussion, conclusion, etc.
• Check for in-text citations and a large bibliography at the end of the article
• Take note of the language an author uses to write their article. Scholarly articles often use jargon - or specific terms that are used and understood by other professionals in the field. This can make them tricky to understand by others without deeper expertise of the topic.

If you are using a Libraries' database, use a filter to narrow your search to just scholarly articles. This is helpful if you do not want news or magazine articles to show up in your search results.

What is Peer Review?

Some journals require articles to go through a peer review process before accepting it for publication. This means an article is reviewed by other experts in the field to check for accuracy and relevancy before a journal will publish it. 

Why Does This Matter?

Many researchers consider peer review articles as a gold standard in research as they have already been evaluated by a panel of experts. Not all scholarly articles are peer reviewed and while both can be helpful to use in your research, you may need to evaluate scholarly articles for quality just like you would a news article before using it in your research project.

How Can I Tell a Scholarly Article from a Peer Reviewed Journal?

Try googling a journal's name. Many times the journal will mention if they require peer review in their about us, but you can also look at their submission criteria or article requirements to find out what process they use.

For example, this is on the about page for the Journal of Business :

"The  Journal of Business   (JoB)  is a peer-reviewed journal with the focus on research articles and case studies in all academic fields of business discipline. The scope of the journal covers the broad range of areas related to business studies including interdisciplinary topics and newly developing areas of business. Submissions comprise research articles – both theoretical and empirical, case studies and reviews of the literature."

When you are searching in a database there may be other articles that you may come across that are not scholarly, but that can be helpful to you in other ways.

Grey Literature

As the name implies, these articles fall into a sort of grey area, they aren't quite scholarly, but they also don't fit anywhere else. The types of articles you might find that fit into this category are:

• Government documents
• Reports, policies, and white papers either produced by an organization, company, or government agency
• Thesis and dissertations, that graduate students produce as part of their degree requirements
• Conference proceedings that researchers produce to share their research with others in more informal ways than through publication

Trade Journals and Magazines

Trade journals are also not the same as a scholarly journal, though they might be easy to confuse. Trade journals are written for professionals in a trade or industry and cover practical topics that impact their career. These articles are written more like news or magazine articles and are meant for working professionals to learn more about innovative technology, relevant news, and current events that impact their industry.

For example, say you decide to pursue a career in managing a fitness center or gym. A relevant trade journal for you would be National Fitness.

How to Get the Most Out of a Scholarly Article

Because of the technical content and level of prior knowledge the author(s) expect their readers to have; being able to get the most out of a scholarly article is a skill that takes time and practice to get good at.

When you are looking for scholarly articles that fulfill your research needs it can be time consuming to read each one top to bottom to determine whether or not it is relevant to your project. Instead try reading the article out of order to determine whether or not a scholarly article is relevant to your project.

• First, read the abstract - if RELEVANT then read the... 
• Discussion and/or Conclusion - if still RELEVANT read the...
• Methodology and/or Literature Review - if SOUND, examine the...
• Argument - if BIAS is limited, check the...
• References or bibliography - for other sources you can use in your research

Start by reading a scholarly article in this order. If what you read sounds relevant, then move on to reading the next section. At any time if they article no longer meets your needs, stop reading and move on. Once you complete reading an article out of order, and you determine that it is relevant to your project, it can be helpful to read the article again from top to bottom and annotate your thoughts as you go.

How to Evaluate Scholarly Articles for Quality

Since many scholarly articles go through a peer review process, it can be quicker and easier to evaluate. However, there are still a few things to investigate before using a scholarly article in your research.

Examine the Article

• How current is the article? Don't just look at when the article was published, but also scan the references to make sure the author(s) used current sources.
• Is the methodology used sound? This may require deeper expertise of the methods used in your field to evaluate. Talk with your faculty mentor about the articles you found to help determine what is appropriate for your field.
• Is this relevant to my research topic? Consider if and how the article you found is relevant to your research topic. Does it provide background on your topic or relate to your research in other ways? Even if an article is well cited, it won't be helpful to you if it doesn't connect to your topic.

Investigate Beyond the Article

• Does the journal require peer review?
• Is this journal well regarded by other experts?
• What expertise do the authors have on this topic?
• What other works have these authors published?
• What is it -  https://www.nature.com/articles/d41586-023-03974-8
• Tools like Retraction Watch can be helpful

It can be helpful to google the author and publisher to see what you can find out about them.

Evaluating a source is to explore the source. You do not need to answer all the questions above each time you evaluate a source. Over time you will become familiar with well regarded journals and authors in your field. All research skills take practice, the more you use this skill the faster and better you will become at it.

• Retraction Watch A database that tracks retractions of scholarly articles
• Retraction - Example This article was retracted from the journal Environmental Science and Pollution research due to a number of concerns including including but not limited to a compromised peer review process, inappropriate or irrelevant references and citation behavior.
• << Previous: Find Scholarly Articles
• Next: Organizing Your Research >>
• Last Updated: Sep 27, 2024 11:54 AM
• URL: https://guides.lib.jmu.edu/reu

Evaluating Policy Counterfactuals: A VAR-Plus Approach

In a rich family of linearized structural macroeconomic models, the counterfactual evolution of the macro-economy under alternative policy rules is pinned down by just two objects: first, reduced-form projections with respect to a large information set; and second, the dynamic causal effects of policy shocks. In particular, no assumptions about the structural shocks affecting the economy are needed. We propose to recover these two sufficient statistics using a ``VAR-Plus'' approach, and apply it to evaluate several monetary policy counterfactuals.

We received helpful comments from Marios Angeletos, Régis Barnichon, Marco Bassetto, Anmol Bhandari, Francesco Bianchi, Ricardo Caballero, Gabriel Chodorow-Reich, Martin Eichenbaum, Simon Gilchrist, Cosmin Ilut, Giuseppe Moscarini, Mikkel Plagborg-Møller, Giorgio Primiceri, Valerie Ramey, Matt Rognlie, Ben Schumann, Ludwig Straub, Iván Werning, and seminar participants at various venues. We also thank Seungki Hong, Klodiana Istrefi, and Diego Känzig for valuable discussions, and Valeria Morales Vasquez for superb research assistance. Wolf acknowledges that this material is based upon work supported by the NSF under Grant #2314736. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, the Federal Reserve Bank of Minneapolis, or the Federal Reserve System. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Overall risk was calculated as the number of patients with outcomes during the follow-up time window divided by the number of patients in the cohort at the beginning of the time window. Other glucagon-like peptide 1 receptor agonists (GLP-1RAs) include albiglutide, dulaglutide, exenatide, liraglutide, and lixisenatide. The mean (SD) follow-up times for semaglutide vs each comparison group are as follows: insulin: 342.0 (21.0) days vs 329.0 (32.7) days, metformin: 342.5 (20.6) days vs 337.6 (25.5) days, dipeptidyl-peptidase-4 inhibitors (DPP-4is): 342.1 (20.9) days vs 329.4 (32.1) days, sodium-glucose cotransporter-2 inhibitors (SGLT2is): 341.8 (21.1) days vs 332.1 (29.8) days, sulfonylureas: 341.7 (21.3) days vs 335.6 (27.0) days, thiazolidinediones: 353.5 (10.5) days vs 330.6 (31.3) days, any other GLP-1RA, 342.7 (20.4) days vs 340.6 (22.6) days, liraglutide: 341.6 (21.4) days vs 339.7 (23.5) days, and dulaglutide: 342.6 (20.5) days vs 340.4 (22.7) days. HR indicates hazard ratio.

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Wang W , Volkow ND , Wang Q, et al. Semaglutide and Opioid Overdose Risk in Patients With Type 2 Diabetes and Opioid Use Disorder. JAMA Netw Open. 2024;7(9):e2435247. doi:10.1001/jamanetworkopen.2024.35247

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Semaglutide and Opioid Overdose Risk in Patients With Type 2 Diabetes and Opioid Use Disorder

• 1 Center for Science, Health, and Society, Case Western Reserve University School of Medicine, Cleveland, Ohio
• 2 National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland
• 3 Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio
• 4 Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, Ohio
• 5 Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio
• 6 Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland

Drug overdose fatalities in the United States remain high, with an estimated 107 543 deaths in 2023, mostly from opioids. 1 Despite the effectiveness of medications for opioid use disorder (OUD) in preventing overdoses, only an estimated 25% of individuals with OUD receive them, 2 and close to 50% discontinue treatment within 6 months. There is an urgency for alternative treatments for OUD. Glucagon-like peptide-1 receptor agonists (GLP1-RAs), used for type 2 diabetes (T2D) and obesity, modulated dopamine reward signaling and decreased drug rewards, including heroin in rodents. 3 Anecdotal reports of reduced drug craving in individuals using semaglutide, a new generation GLP-1RA, along with empirical studies showed its therapeutic benefits in alcohol and nicotine use disorders. 4 , 5 This led us to investigate whether semaglutide could protect against overdoses in patients with OUD.

In this cohort study, we conducted an emulation target trial to compare the association of semaglutide vs other antidiabetic medications, ie, insulin, metformin, dipeptidyl-peptidase-4 inhibitors (DPP-4is), sodium-glucose cotransporter-2 inhibitors (SGLT2is), sulfonylureas, thiazolidinediones, and other GLP-1RAs, including liraglutide and dulaglutide, with opioid overdose risk in patients with comorbid T2D and OUD. Each component of the target trial was emulated using electronic health records (EHRs) from the TriNetX Analytics Platform, a federated health research network providing access to deidentified EHRs of 116.6 million patients in the US. 6 Previously, we used TriNetX to study semaglutide’s association with outcomes for alcohol and nicotine use disorders. 4 , 5

Eligibility criteria included patients diagnosed with both T2D and OUD; prescribed semaglutide or other antidiabetic medications between December 2017 and June 2023; and with a history of obesity, hypertension, hypercholesterolemia, hyperlipidemia, heart diseases, or stroke. Exclusion criteria were bariatric surgery, pancreatitis, type 1 diabetes, thyroid cancer, or gastroparesis. Patients were classified into semaglutide and other antidiabetes medication groups based on the first prescription during the study period, which was the baseline or index event (eAppendix in Supplement 1 ). The semaglutide group and each comparison group were separately propensity-score matched for covariates at the baseline to emulate randomization. The main outcome (opioid overdose) and a negative control outcome (medical encounters for congenital malformations, deformations, and chromosomal abnormalities) were examined. Follow-up was from the index event until the outcome, death, loss to follow-up, or 12 months, whichever occurred first. Hazard ratios (HRs) and cumulative incidences were estimated using Cox proportional hazard and Kaplan-Meier survival analyses, with censoring applied. Further details appear in the eAppendix in Supplement 1 . We used built-in functions that are implemented on the TriNetX analytics platform using libraries and utilities from R version 4.0.2 (R Project for Statistical Computing), Python version 3.7 (Python Software Foundation), and Java version 11.0.16 (Oracle).

The MetroHealth System, Cleveland, Ohio, institutional review board determined research using TriNetX, in the way described here, is not human subject research, and therefore institutional review board approval was not required and the requirement for informed consent was waived. This study follows STROBE reporting guidelines for cohort studies.

The study included 33 006 eligible patients: 3034 were prescribed semaglutide (mean [SD] age, 57.4 [11.0] years; 1714 [56.5%] female) and 29 972 were prescribed other antidiabetic medications. Semaglutide was compared with each antidiabetic medication class in patients with comorbid T2D and OUD. Before propensity-score matching, the semaglutide and comparison groups differed by age, sex, ethnicity, and comorbidity conditions, but characteristics were balanced after matching ( Table ). Semaglutide was associated with a significantly lower risk of opioid overdose during a 1-year follow-up compared with other antidiabetic medications, including other GLP-1RAs, with HRs ranging from 0.32 (95% CI, 0.12-0.89) to 0.58 (95% CI, 0.38-0.87) ( Figure ). The negative control outcome showed no difference between groups.

Semaglutide was associated with reduced opioid overdose risk in patients with comorbid T2D and OUD, suggesting its potential therapeutic value for preventing overdoses. Study limitations include potential unmeasured or uncontrolled confounders, biases, and others inherent in EHR-based observational studies. Results need validation from other data resources and study populations. Further research is warranted to investigate the underlying mechanisms and randomized clinical trials are necessary to corroborate the clinical effects on OUD.

Accepted for Publication: July 28, 2024.

Published: September 25, 2024. doi:10.1001/jamanetworkopen.2024.35247

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Wang W et al. JAMA Network Open .

Corresponding Authors: Rong Xu, PhD, Center for Artificial Intelligence in Drug Discovery, 2103 Cornell Rd, Cleveland, OH 44106 ( [email protected] ); Nora D. Volkow, MD, National Institute on Drug Abuse, National Institutes of Health, 6001 Executive Blvd, Room 5274 MSC 9581, Bethesda, MD 20892 ( [email protected] ).

Author Contributions: Dr Xu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Davis, Kaelber, Xu.

Acquisition, analysis, or interpretation of data: W. Wang, Volkow, Q. Wang, Berger, Xu.

Drafting of the manuscript: Volkow, Xu.

Critical review of the manuscript for important intellectual content: W. Wang, Volkow, Q. Wang, Berger, Davis, Kaelber, Xu.

Statistical analysis: W. Wang, Q. Wang.

Obtained funding: Volkow, Davis, Xu.

Administrative, technical, or material support: Volkow, Davis, Kaelber, Xu.

Supervision: Volkow, Berger, Davis, Kaelber, Xu.

Conflict of Interest Disclosures: None reported.

Funding/Support: We acknowledge support from National Institute on Alcohol Abuse and Alcoholism (grant AA029831), National Institute on Aging (grants AG057557, AG061388, AG062272, and AG07664), and the National Center for Advancing Translational Sciences (grant TR004528). This research was supported in part by the Intramural Research Program of the National Institutes of Health.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to acknowledge the strong support and significant contribution of Nathan A. Berger, PhD (1940-2024), to this study, who also served as the primary research mentor of Mr W. Wang (first author of this study).

Additional Information: Dr Berger is deceased.

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Knowledge grows step-by-step despite the exponential growth of papers, finds study

Scientific knowledge is growing at a linear rate despite an exponential increase in publications. That’s according to a study by physicists in China and the US, who say their finding points to a decline in overall scientific productivity. The study therefore contradicts the notion that productivity and knowledge grow hand in hand – but adds weight to the view that the rate of scientific discovery may be slowing or that “information fatigue” and the vast number of papers can drown out new discoveries .

Defining knowledge is complex, but it can be thought of as a network of interconnected beliefs and information. To measure it, the authors previously created a knowledge quantification index (KQI). This tool uses various scientific impact metrics to examine the network structures created by publications and their citations and quantifies how well publications reduce the uncertainty of the network, and thus knowledge.

The researchers claim the tool’s effectiveness has been validated through multiple approaches, including analysing the impact of work by Nobel laureates.

In the latest study, published on arXiv , the team analysed 213 million scientific papers, published between 1800 and 2020, as well as 7.6 million patents filed between 1976 and 2020. Using the data, they built annual snapshots of citation networks, which they then scrutinised with the KQI to observe changes in knowledge over time.

The researchers – based at Shanghai Jiao Tong University in Shanghai, the University of Minnesota in the US and the Institute of Geographic Sciences and Natural Resources Research in Beijing –found that while the number of publications has been increasing exponentially, knowledge has not.

Instead, their KQI suggests that knowledge has been growing in a linear fashion. Different scientific disciplines do display varying rates of knowledge growth, but they all have the same linear growth pattern. Patent growth was found to be much slower than publication growth but also shows the linear growth in the KQI.

‘Hidden’ citations conceal the true impact of scientific research

According to the authors, the analysis indicates “no significant change in the rate of human knowledge acquisition”, suggesting that our understanding of the world has been progressing at a steady pace.

If scientific productivity is defined as the number of papers required to grow knowledge, this signals a significant decline in productivity, the authors claim.

The analysis also revealed inflection points associated with new discoveries, major breakthroughs and other important developments, with knowledge growing at different linear rates before and after.

Such inflection points create the illusion of exponential knowledge growth due to the sudden alteration in growth rates, which may, according to the study authors, have led previous studies to conclude that knowledge is growing exponentially.

Research focus

“Research has shown that the disruptiveness of individual publications – a rough indicator of knowledge growth – has been declining over recent decades,” says Xiangyi Meng , a physicist at Northwestern University in the US, who works in network science but was not involved in the research. “This suggests that the rate of knowledge growth must be slower than the exponential rise in the number of publications.”

Meng adds, however, that the linear growth finding is “surprising” and “somewhat pessimistic” – and that further analysis is needed to confirm if knowledge growth is indeed linear or whether it “more likely, follows a near-linear polynomial pattern, considering that human civilization is accelerating on a much larger scale”.

Due to the significant variation in the quality of scientific publications, Meng says that article growth may “not be a reliable denominator for measuring scientific efficiency”. Instead, he suggests that analysing research funding and how it is allocated and evolves over time might be a better focus.

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Microsoft Research Lab – Asia

Nnscaler: exploring a new paradigm for parallel execution in deep learning, share this page.

Author: Youshan Miao

Today, deep learning has permeated our daily lives. As the size of models continues to grow, training these models on massive GPU accelerators has become increasingly time-consuming and costly. To effectively harness the power of massive GPUs and enhance efficiency, researchers have been developing various parallel strategies to improve performance across multiple devices. However, many potentially efficient parallel strategies remain unexplored. Fully exploring these possibilities to further unlock performance remains a major challenge in both research and application.

Recently, researchers from Microsoft Research Asia proposed nnScaler, a framework that supports freely expressing parallel strategies through a set of parallel primitives while minimizing search costs by utilizing a strategy-constrained search space.

Parallel primitives: A unified framework to describe parallel strategies

Mainstream training systems, such as Megatron-LM, DeepSpeed, and Alpa, typically incorporate built-in parallel strategies like data-parallelism, tensor-parallelism, and pipeline-parallelism, which can be combined through configuration. While this approach is efficient for most cases and convenient for users, it may overlook more efficient strategies. Including new efficient parallel strategies often require substantial modifications to the underlying system code, which is impractical.

Therefore, researchers revisited the fundamental components of parallel strategies. The execution of deep learning models can usually be represented as a data-flow graph, where tensors (representing data) are the edges, and tensor operators are the vertices. The process of parallelization involves transforming the computation data-flow graph, originally designed for single-device execution, into a distributed data-flow graph for multi-device execution. Thus, researchers propose a set of basic operations as parallel primitives, including:

• op-trans : Describes how operators and tensors are partitioned.
• op-assign : Assigns the partitioned operators to specific devices.
• op-order : Specifies the execution order of operators on the same device.

By utilizing these primitives, we can precisely describe how each operator and tensor in the data-flow graph is partitioned and scheduled across devices (spatially) and over time (temporal order on the same device).

These parallel primitives can naturally express various widely used parallel strategies. For instance, data-parallelism can be expressed as partitioning all forward pass and backward pass operators along the data sample dimension and distributing them evenly across all devices, while copying the optimizer operators to each device. All operators on each device maintain the same execution order as in the original graph. The introduction of parallel primitives allows for the flexible description and integration of various parallel strategies within a unified framework, significantly expanding the representational scope of parallel strategy spaces.

This universality not only systematically expresses existing parallel strategies but also provides the potential to explore new ones. For example, for operators that produce intermediate results too large for a single device, the original strategy might be tensor-parallelism with communication coordination. However, using parallel primitives, we can partition the tensor for that specific operator and schedule all partitions on the same device for sequential execution, successfully executing the operation while avoiding communication overhead, as shown in the figure below.

Enhancing efficiency through expert-guided strategy search

While parallel primitives expand the strategy space, this expansion also introduces a new challenge—the vast strategy space makes it difficult to complete searches within a limited time. With a multitude of possible combinations, efficiently finding the optimal strategy becomes a pressing issue.

nnScalerleverages domain expert wisdom to guide effective strategy searches. This guidance can be described through parallel primitives, seamlessly integrating into the system. For example, as shown in the figure below, researchers can constrain the operator’s partitioning scheme to the options specified in the algo set and limit the partitioning to the number of pieces specified by num.

By setting specific constraints, experts can significantly narrow the search space, making the search process more efficient and targeted. Experiments have shown  up to 10x increase in search efficiency without compromising the effectiveness of the strategies.

This approach can uncover high-performance strategies that existing methods overlook and complete searches in a shorter time, thereby improving the efficiency of deep learning training. nnScaler has been validated in the training of multiple deep learning models, demonstrating significant performance improvements.

For more details, please refer to the nnScaler paper: https://www.usenix.org/conference/osdi24/presentation/lin-zhiqi (opens in new tab)

By introducing parallel primitives and expert-guided strategy searches, nnScaler has effectively addressed many issues in the design of parallel strategies for deep learning. This method not only significantly expands the space of parallel strategies, but also provides new directions and tools for future research in parallel strategies. Researchers are eager to see this method demonstrate its potential in broader applications, bringing more possibilities to the development of deep learning.

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Speaker 1: Hello and welcome to a new video on publishing your paper. If you have watched my previous video on how peer review process work, you might know about all the stages your paper goes through before it is finally published. If your paper has passed from editor to reviewer for being reviewed, it means it has the potential to get published. The reviewers are the experts in that area and send their suggestions or queries regarding your paper. Based on the extent of the concern or improvement, they send it back to the corresponding author through the editor for minor or major revisions. And to get the manuscript accepted, authors must consider the comments carefully. In today's video, you will learn how to respond appropriately to the comments received from reviewers. First of all, if your paper has come for revision receiving several comments, it does not mean the paper is not worthy. Stay optimistic. Take the reviewer's comment positively rather than being criticizing about it. Take it as an opportunity to improve your paper further. Reviewers take a lot of time from their busy schedule for reviewing your manuscript. Thank them for their efforts to review the paper and for suggesting the shortcomings. Authors are given few days to few weeks time depending upon the type of revision and corrections. So rather than quickly reacting to the comments, read each and every comment critically and find the suitable answer or solution for each of them and respond scientifically. While responding, do not beat around the bush writing unnecessary content. Write what is asked and what is relevant to the question. Else, this may increase the chances of rejection. Do not start writing the response sheet right away. Read each and every comment before making corrections and responding. This way you get to know whether the suggestions given are doable or not. Also, if there are common or similar comments, you may prepare the corrections and answers accordingly. Authors have to respond to each and every comment separately. Authors may choose to respond in tabular format or point-wise mentioning their query number, what was the query, followed by the response. If there are more than one reviewer, then answer reviewer-wise. Unless mentioned, do all the corrections in track changes mode and also provide a clean revised version of the manuscript. This way the reviewers will know what changes were made. In the response sheet, include what text was replaced with what or what new was inserted along with its page number and line number. This eases the reviewer's work to search the whole document. If last text or paragraph is modified, you may just mention the page number and the line numbers. Answering to the reviewer's comment is an art. Response to the comment should be such that the comments are properly and politely addressed. If you think the raised question is genuine, then acknowledge and make the required changes. If you think the raised question is not appropriate, you still need to answer tactfully, disagreeing giving appropriate justification. If some additional experiment or work is asked and you can do it, then just do it. But if performing experiments is not possible, justify it with a reason. If two reviewers are making contrasting comments, choose to adhere to one, responding the other with a reason. If any changes were made in the figures or the tables, the same needs to be uploaded and mentioned in the response sheet. Along with the response sheet, attach a new cover letter thanking the editor and reviewer for considering your manuscript. Mention that all the comments have been addressed and manuscript is being revised. Before submitting the revised manuscript and response sheet, get them checked by other authors. Check if every comment is properly answered and desired changes are made in the manuscript. Take care to provide right page number and line number in the response. I am sure this video will increase the possibility of your paper to get accepted. So that's all for the today's video. If you like it, do share with your colleagues. If I missed something important, do mention in the comments. Subscribe and hit the bell icon to get notified about my latest uploads. Check my playlist about the research-related topics and publishing. And see you in my next video.