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To support this, the CLSP is running an Applicant Support Program that aims to assist underrepresented students as they apply to our PhD program. 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<strong>Additional resources for Fall 2024 applicants</strong></p> <cite><br />Within the Center for Language and Speech Processing, we believe that our research mission depends on a diverse and inclusive community. To support this, the CLSP is running an Applicant Support Program that aims to assist underrepresented students as they apply to our PhD program.<br /><br />Applicants will receive a one-time review of their application materials (statements and CV) from a student volunteer. Participation in this program is separate from admissions and will not affect admissions. <br /><br />Applications are reviewed on a rolling basis until full. Please sign up <a href="https://forms.gle/wBCF52UqpCoAM7hL8" target="_blank" rel="noreferrer noopener">here</a> by <strong>November 17, 2024</strong> to participate in this program. <br /><br /></cite></blockquote> <blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow"> <p></p> </blockquote> <h2 class="wp-block-heading" id="FAQ">PhD Admissions: FAQ</h2> <p id="-FAQ">This guide is written for applicants interested in the PhD program in <em>Computer Science</em> or <em>Electrical and</em> <em>Computer Engineering</em> at Johns Hopkins with an interest in the <em>Center</em> <em>for Language and Speech</em> <em>Processing </em>(CLSP).</p> <div class="entry-content"> <p>CLSP accepts a number of new Ph.D. students each year and offers them full funding. Previous academic or industry experience in speech or language processing is a plus. However, we welcome all talented applicants, including those with skills in related areas such as machine learning, linguistics, mathematics, cognitive science, physics, or neuroscience. We also accept master’s students who are interested in the field.</p> <p>Watch Prof Mark Dredze’s PhD Admissions AMA with an explanation of our admissions process and answers to common questions.</p> <p><div class="media video"><div class="responsive-media" style="padding-bottom:75%"><iframe title="PhD Admissions AMA with Prof Mark Dredze" width="464" height="348" src="https://www.youtube.com/embed/YlwpIPFNXjo?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></div></div></p> <blockquote> <p>Make sure to check out our <a href="#FAQ">FAQ for Ph.D. admissions</a>. It contains great general advice for applying to JHU and elsewhere.</p> </blockquote> </div> <div class="entry-content"> <h2> </h2> <h2>How to Apply</h2> <p>CLSP is a multi-disciplinary interest group of faculty and students who work together across department lines. Students ordinarily join by applying to one of the following departments:</p> <ul> <li class="first-child"><a href="https://www.cs.jhu.edu/academic-programs/graduate-studies/" target="_blank" rel="noopener noreferrer">Computer Science</a></li> <li class="last-child"><a href="https://engineering.jhu.edu/ece/academics/" target="_blank" rel="noopener noreferrer">Electrical and Computer Engineering</a></li> </ul> <p>The departments of <a href="https://cogsci.jhu.edu/graduate/phd-program/" target="_blank" rel="noopener noreferrer">Cognitive Science</a> and <a href="https://pbs.jhu.edu/" target="_blank" rel="noopener noreferrer">Psychological and Brain Sciences</a> are also an important part of the language research community here, and you may consider applying there as well if you are primarily interested in linguistics or human language processing. If you are interested in machine learning, see <a href="http://ml.jhu.edu">this page</a> for additional options.</p> <p>Johns Hopkins has an interdisciplinary culture where it is common to have coursework and advisors in multiple departments. <em>Students must, however, must meet the requirements for admission and degree completion in their home department.</em></p> <p>You may contact <a href="https://www.clsp.jhu.edu/about/contact/">the center</a> or individual faculty with questions. We will also talk to you at length if we are considering admitting you — either bringing you to campus for a 2-day visit or interviewing you by Skype if you are not in the U.S.</p> <h3> </h3> <h2>Why Apply</h2> <p>CLSP is a nexus for research in the field, and offers an unusual array of opportunities to graduate students:</p> <ul> <li class="first-child">Weekly <a href="https://www.clsp.jhu.edu/seminars/">visits</a> from leaders across the field of speech and language</li> <li>A 2-week <a href="https://www.clsp.jhu.edu/workshops/12-workshop/jhu-summer-school-on-human-language-technology/">summer school</a></li> <li>Annual 6-week summer <a href="https://www.clsp.jhu.edu/workshops/">workshops</a>, many of which have produced field-changing research</li> <li>The <a href="http://hltcoe.jhu.edu/">Center of Excellence in Human Language Technology</a>, a federally funded center for research in speech and language processing</li> <li>A full curriculum of <a href="https://www.clsp.jhu.edu/about/courses/">coursework</a> in the language and speech areas</li> <li class="last-child">Weekly reading groups in natural language processing, machine learning, computational approaches to meaning and translation, and speech processing and recognition</li> </ul> <p>CLSP has one of the world’s largest collections of speech and language <a href="https://www.clsp.jhu.edu/faculty/">faculty</a> while enjoying a close-knit collaborative environment with a low student-to-faculty ratio. CLSP research areas include:</p> <ul> <li class="first-child"><strong>Speech processing:</strong> recognition, language modeling, biologically inspired speech perception, robust recognition</li> <li><strong>Natural language processing:</strong> parsing, semantics, information extraction, morphology, phonology, sentiment</li> <li><strong>Machine translation:</strong> syntax-based and phrase-based translation, lexicon induction</li> <li><strong>Machine learning:</strong> semi-supervised learning, graphical models, Bayesian methods, large-scale learning, transfer learning</li> <li><strong>Information retrieval:</strong> large-scale retrieval, cross-language retrieval</li> <li class="last-child"><strong>Statistics:</strong> computational statistics, statistical pattern recognition, statistical inference for high-dimensional and graph data, information theory</li> </ul> </div> <h4> </h4> <h2>Statement on Diversity</h2> <p>We are committed to a diverse community within the center, as diversity is a key element of the educational experience of our students. Diversity presents itself in many different forms such as socioeconomic status, race or ethnicity, religion, gender, sexual orientation, nationality or place of origin, disability, unique work or life experience, etc. Our goal through the admissions process is to cultivate an environment that values diverse backgrounds, approaches, and perspectives.</p> <p>Johns Hopkins University does not discriminate on the basis of gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, veteran status or other legally protected characteristic in any student program, activity administered by the university, admission, or employment.</p> <p>CLSP also includes students from PhD programs in Cognitive Science, Applied Math and Statistics, and others. Some of this guide may apply to other PhD programs, and non-CLSP students. You can view statistics about the students in each department’s program here: <a href="https://oir.jhu.edu/phd-statistics/">https://oir.jhu.edu/phd-statistics/</a></p> <hr class="wp-block-separator has-alpha-channel-opacity is-style-wide"/> <ul class="wp-block-list"> <li><a href="#toc-1"><strong>What goes into my application material?</strong></a> <ul class="wp-block-list"> <li><a href="#toc-1.1">Essay</a></li> <li><a href="#toc-1.2">Letters</a> <ul class="wp-block-list"> <li><a href="#toc-1.2.1">How many letters of recommendation should I get?</a></li> </ul> </li> </ul> <ul class="wp-block-list"> <li><a href="#toc-1.3">Grades</a></li> <li><a href="#toc-1.4">Test Scores</a> <ul class="wp-block-list"> <li><a href="#toc-1.4.1">GRE</a></li> <li><a href="#toc-1.4.2">TOEFL or IELTS</a></li> </ul> </li> </ul> </li> <li><a href="#toc-2"><strong>Application Links</strong></a> <ul class="wp-block-list"> <li><a href="#toc-2.1">What is the application fee? Is there fee waiver?</a></li> </ul> </li> <li><a href="#toc-3"><strong>Applications Tips</strong></a></li> <li><a href="#toc-4"><strong>Application Deadlines</strong></a></li> <li><a href="#toc-5"><strong>More FAQs</strong></a> <ul class="wp-block-list"> <li><a href="#toc-5.1">Which department to apply – CS or ECE?</a></li> <li><a href="#toc-5.2">Should I email professors before I apply?</a></li> <li><a href="#toc-5.3">Does CLSP value a diverse community?</a></li> <li><a href="#toc-5.4">Do I need to have a CS degree?</a></li> <li><a href="#toc-5.5">Do I need to have published papers?</a></li> <li><a href="#toc-5.6">How will my advisor be assigned?</a></li> <li><a href="#toc-5.7">How will I receive funding?</a></li> <li><a href="#toc-5.8">What is included in the student healthcare policy?</a></li> <li><a href="#toc-5.9">How many applicants do you plan to admit?</a></li> <li><a href="#toc-5.10">What is JHU’s policy on parental leave?</a></li> </ul> </li> <li><strong><a href="#toc-6">Lingering questions</a></strong></li> </ul> <hr class="wp-block-separator has-alpha-channel-opacity is-style-wide"/> <h2 class="wp-block-heading" id="toc-1">What goes into my application material?</h2> <p>The 4 major components of an application are essay, letters, grades and test scores.</p> <blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow"> <p>The most important question we seek to answer when reviewing applications: will this individual excel at research. Every part of your application is helpful only insofar as it answers that question.</p> </blockquote> <p></p> <h3 class="wp-block-heading" id="toc-1.1"><strong>Essay</strong></h3> <ul class="wp-block-list"> <li>This is one of the most important parts of your application.</li> <li>It lets us get to know you, and allows to create a narrative of your academic career and future plans.</li> <li>Before you write the essay, start by thinking about you want us to learn about you.</li> <li>Make a list of important achievements, perspectives and goals. Build the essay around this list. We are looking for students who have made the most of the opportunities they have had, and who are smart, creative and motivated.</li> <li>Keep in mind that we’ll also have your CV and letters, so we don’t necessarily need a list of accomplishments. However, the essay can fill in the narrative around what you did, specifically, why you did it. What motivates you? What are your research interests and why? These details aren’t found elsewhere in your application, so focus on them in the essay.</li> <li>There are a few things we suggest not including in the essay. It’s tempting to give a rationale for why you are applying to our program. But if it’s uninformed, don’t bother. Consider: “I want to apply to CLSP because it’s one of the premier academic groups in NLP.” We know that already</li> <li>If you do have specific reasons to be interested in our program (e.g. location, specific project, faculty, etc.) be sure to mention them.</li> <li>In terms of motivation, be specific! Don’t write: “I’ve wanted to do a PhD in CS since I was 6 years old.” We don’t trust that 6 year olds make good decisions. If you write “I have always found language fascinating”, why?</li> <li>In terms of mentioning faculty to work with: it’s a good idea if you know, but skip it if you don’t. If you explain your research interests in detail, we’ll be able to do a better job matching you with an adviser.</li> <li>We suggest this post from our colleague Nathan Schneider on <a href="https://medium.com/@nschneid/inside-ph-d-admissions-what-readers-look-for-in-a-statement-of-purpose-3db4e6081f80">what readers look for in a statement of purpose</a></li> </ul> <p></p> <h3 class="wp-block-heading" id="toc-1.2"><strong>Letters</strong></h3> <h4 class="wp-block-heading" id="toc-1.2.1"><strong>How many letters of recommendation should I get?</strong></h4> <p>PhD applicants need <strong>three</strong> letters. We require that your recommenders submit their recommendations online.</p> <p>The two most important aspects of a letter.</p> <ol class="wp-block-list"> <li>Select someone who knows you well, and</li> <li>Select someone who knows how to write a letter.</li> </ol> <ul class="wp-block-list"> <li>First, it’s tempting to ask Prof X. to write a letter for you because she is a well known person in the field. It’s true that we trust letters more from people we know, but it’s only helpful if the letter contains meaningful information. If Prof X writes, “I’ve met the applicant a few times and they seem sharp” that’s not useful information. It’s more important to select someone who knows you well, and can discuss your achievements in detail.</li> <li>Second, it’s important that the letter writer knows how to write a letter. Academic research programs look for different things than a company. We often read letters from work supervisors that say nice things, but don’t speak to the qualities we find most important in a letter. Of course, it’s a balance. You want someone who knows you well, but they still need to know how to write letters.</li> <li>We understand that three letters are a lot, especially for an undergrad applying directly to a PhD program. We don’t expect each candidate to have three amazing letters. Your choice is about balance. You want people who know you well, can write good academic letters, and know our field. Use the choice of three people to create this balance.</li> </ul> <p></p> <h3 class="wp-block-heading" id="toc-1.3"><strong>Grades</strong></h3> <p>There isn’t much you can do about this; you have the grades you have. It’s important to understand, we don’t use any grade cutoffs or thresholds in admissions. We want to see that you did well, and excelled in whatever program you were in. We look at the classes you took. Did you push yourself to take upper level classes? Did you do well in classes most directly related to our research area? If you have special circumstances that explain some of your grades, be sure to include a description in the letter.</p> <p></p> <h3 class="wp-block-heading" id="toc-1.4"><strong>Test Scores</strong></h3> <h4 class="wp-block-heading" id="toc-1.4.1"><strong>1. GRE</strong></h4> <p>The Whiting School of Engineering does <strong>NOT</strong> require GRE General Test scores for applications to our PhD programs.</p> <h4 class="wp-block-heading" id="toc-1.4.2">2. <strong>TOEFL or IELTS</strong></h4> <p>Non-native English speakers must take the TOEFL exam or IELTS. Details on accepted exams, scores, and exceptions to this requirement can be found <a href="https://engineering.jhu.edu/admissions/graduate-admissions/full-time-programs/how-to-apply/international-students/">here</a>.</p> <p></p> <h4 class="wp-block-heading"><strong>When are applicants waived from having to submit a TOEFL/IELTS score?</strong></h4> <p>When an applicant’s native language is English or an applicant meets the University’s TOEFL/IELTS requirement <a href="https://livejohnshopkins-my.sharepoint.com/:b:/g/personal/seisenb9_jh_edu/EU2EQwwcVmtFm9fQDsOwOQcBvCF4dCHMiKa2QrAHRlQDnA?e=W8rdkJ">waiver policy</a>.</p> <h4 class="wp-block-heading">If I am eligible for a TOEFL/IELTS score waiver, how will the waiver be noted on my application?</h4> <p>The Office of Graduate Admissions will note the waiver. If you do not see the waiver within 14 days of submitting your application:</p> <p>Contact <a href="/cdn-cgi/l/email-protection#b0e7e3f5f7c2d1d49df1d4ddd9c3c3d9dfdec3f0dad8c59ed5d4c5">the graduate admissions office</a> directly. Do not contact the department, as they can only refer you to graduate admissions.</p> <p>[Source:<a href="https://engineering.jhu.edu/ece/ece-admissions/graduate-admission-info/"> ECE Graduate Admissions page</a>]</p> <p></p> <p></p> <h2 class="wp-block-heading" id="toc-2">Application Links</h2> <p>You’ve decided to apply! Fantastic. You’ll find the application here: <a href="https://engineering.jhu.edu/admissions/graduate-admissions/">Graduate Admissions</a> , <a href="https://engineering.jhu.edu/admissions/graduate-admissions/full-time-programs/how-to-apply/general-application-requirements/">How to apply and general graduate requirements</a></p> <p>General CS information can be found here: <a href="https://www.cs.jhu.edu/academic-programs/graduate-studies/">CS Graduate Studies</a></p> <p>General ECE Information can be found here: <a href="https://engineering.jhu.edu/ece/academics/phd-program/">ECE Doctoral Studies</a></p> <h4 class="wp-block-heading" id="toc-2.1"><strong>What is the application fee? Is there fee waiver?</strong></h4> <p>(might be outdated – check websites above for updated information)</p> <ul class="wp-block-list"> <li><strong>Computer Science</strong> <ul class="wp-block-list"> <li>A $75 fee is typically required alongside your application. However, we offer waivers in cases where the fee is a barrier to your application. If you are a first-generation college student, have participated in a program such as NNE or SACNAS (<a href="https://grad.jhu.edu/apply/application-process/#1464027869971-8cbbde29-b882">among many others</a>), or are unable to apply due to a financial burden, please contact us at <a href="/cdn-cgi/l/email-protection#abcacfc6c2d8d8c2c4c5d8ebc8d885c1c3de85cecfde"><span class="__cf_email__" data-cfemail="d8b9bcb5b1ababb1b7b6ab98bbabf6b2b0adf6bdbcad">[email protected]</span></a>.</li> <li>[Source: <a href="https://www.cs.jhu.edu/academic-programs/graduate-studies/phd-program/phd-admissions-faqs/">PhD Admissions FAQs – Department of Computer Science</a>]</li> </ul> </li> <li><strong>Electrical & Computer Engineering</strong> <ul class="wp-block-list"> <li>$25 Application Fee</li> <li>[Source: <a href="https://engineering.jhu.edu/ece/ece-admissions/graduate-admission-info/">Graduate Admissions – Department of ECE</a>]</li> </ul> </li> </ul> <p></p> <p></p> <h2 class="wp-block-heading" id="toc-3">Application Tips</h2> <p>There are many helpful guides for PhD applications. A few we recommend:</p> <ul class="wp-block-list"> <li><a href="https://www.cs.jhu.edu/~mdredze/publications/HowtoBeaSuccessfulPhDStudent.1_1.pdf">How to be a Successful PhD Student</a> (co-authored by our own <a href="https://www.cs.jhu.edu/~mdredze/">Mark Dredze</a>)</li> <li><a href="https://medium.com/@nschneid/inside-ph-d-admissions-what-readers-look-for-in-a-statement-of-purpose-3db4e6081f80">What Readers Look for in a Statement of Purpose (by Prof. Nathan Schneider)</a></li> <li><a href="https://blog.nelsonliu.me/2019/10/24/student-perspectives-on-applying-to-nlp-phd-programs/">Student Perspectives on Applying to NLP PhD Programs</a></li> <li><a href="http://karpathy.github.io/2016/09/07/phd/">A Survival Guide to a PhD</a></li> </ul> <p></p> <p></p> <h2 class="wp-block-heading" id="toc-4">Application Deadlines</h2> <p>We accept applications beginning in August for Fall. No recruiting for Spring admissions.</p> <p>(might be outdated – check websites above for updated information)</p> <p><strong>Computer Science (CS):</strong></p> <p>The deadline for Fall is December 15</p> <p><strong>Electrical and Computer Engineering (ECE):</strong></p> <p>The application submission deadline is December 22 at 11:59 p.m. EDT (UTC-5).</p> <p></p> <p></p> <h2 class="wp-block-heading" id="toc-5">More FAQs</h2> <h4 class="wp-block-heading" id="toc-5.1"><strong>Which department to apply – CS or ECE?</strong></h4> <p>CLSP is an interdisciplinary center that includes students and faculty from the computer science (CS), electrical and computer engineering (ECE), cognitive science and other departments. CLSP is an organizing group around research, but your PhD program is run by a department.</p> <p>If you are interested in working with faculty in CLSP, you first need to decide what department best fits your interests. Once enrolled in that department as a PhD student, you’ll have the option of working with all faculty in that department, including those who also affiliate with CLSP.</p> <p>If you are unsure which department best fits you, consider both the faculty and the graduate program. In terms of faculty, which professors best fit your interests? We have many students interested in faculty broadly in CS, including NLP, Machine learning, and medicine (for example). Second, look at the graduate programs. You’ll be taking classes in your first two years. Which department’s classes are the best fit for you?</p> <p><mark style="background-color:#8ed1fc" class="has-inline-color">💡 You apply directly to the departments, not to CLSP. You can indicate your area of interest on your application as related to natural language processing, so it will come to the attention of the CLSP faculty</mark>.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.2"><strong>Should I email professors before I apply?</strong></h4> <p>CLSP accepts students every year in every area of speech and NLP. If your question is “will you have slots,” there is no need to ask. Apply!</p> <p>If you have specific questions about a faculty member’s research, you are welcome to contact them ahead of time. However, keep in mind that faculty members receive dozens of emails from applicants and often don’t have time to response. This is especially true of long emails that focus on the applicant without specific questions for faculty. If you email faculty, we recommend you keep it short, to the point, and specific to that faculty member’s interests. Don’t be offended if they don’t reply.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.3"><strong>Does CLSP value a diverse community?</strong></h4> <p>We are committed to a diverse community within the center, as diversity is a key element of the educational experience of our students. Diversity presents itself in many different forms such as socioeconomic status, race or ethnicity, religion, gender, sexual orientation, nationality or place of origin, disability, unique work or life experience, etc.  Our goal through the admissions process is to cultivate an environment that values diverse backgrounds, approaches, and perspectives.</p> <p>Johns Hopkins University does not discriminate on the basis of gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, veteran status or other legally protected characteristic in any student program, activity administered by the university, admission, or employment.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.4"><strong>Do I need to have a CS degree?</strong></h4> <p>No! We are an interdisciplinary center. Applicants have degrees in CS, ECE, linguistics, cognitive science, math, and many other areas. While we want to see some experience with CS, we take students from a diverse range of backgrounds.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.5"><strong>Do I need to have published papers?</strong></h4> <p>Certainly not. While many of our applicants have publications demonstrating their research experience, we care more about what you did with the opportunities you were given. Some students come from major US universities with active research programs, in which case students have the opportunity to publish. Other students come from smaller schools without these opportunities. Each year we accept many students without publications, and reject students with multiple publications.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.6"><strong>How will my advisor be assigned?</strong></h4> <p>You will be assigned an initial academic advisor, who may or may not become your eventual thesis advisor. All students are admitted with at least two potential advisors. Additionally, students are required to complete research qualifying projects early in their studies with two different faculty. If you review publications coming from the center, you’ll observe that many have more than one faculty member, and individual students will publish with different faculty. Many of our students are co-advised, and changing advisors or adding a second advisor is relatively easy. If you are a PhD student in Computer Science, you will be able to work with any CS faculty member, subject to mutual agreement.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.7"><strong>How will I receive funding?</strong></h4> <p>(might be outdated – check websites above for updated information)</p> <p>WSE PhD students are fully funded (tuition, health insurance and stipend) for the duration of their PhD program while they are in a full-time, resident status. The stipend <strong>minimum</strong> is equivalent to 12 months at $35,600. Admission offer letters cite specifics for each student and program.</p> <p><a href="https://homewoodgrad.jhu.edu/life-at-hopkins/costsandfunding/">Costs and Funding | Graduate & Postdoctoral Affairs</a></p> <p>In particular, CLSP PhD students are also all fully funded. Funding includes a yearly stipend, tuition grant and health insurance and typically increases each year. Our current CS stipend is $43,000.</p> <p>Our students are primarily funded on research assistantships, with some amount of time on teaching assistantships (teaching is a requirement of the CS PhD program.)</p> <p>Additionally, we have many students with external fellowships. To the best of our knowledge, no CLSP student has ever left the program due to a lack of funding. Additionally, Baltimore offers a lower cost of living than many other major US cities.</p> <p>Many CLSP students participate in summer internship programs within industry, which pay considerably higher salaries.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.8"><strong>What is included in the student healthcare policy?</strong></h4> <p>The student healthcare package contains medical care via Cigna, vision care via EyeMed, and dental via Delta Dental. The full policy details are available here:</p> <p><a href="https://benefits.jhu.edu/health-and-life/student_health/overview.cfm">Student Health Benefits – JHU Human Resources</a></p> <p></p> <h4 class="wp-block-heading" id="toc-5.9"><strong>How many applicants do you plan to admit?</strong></h4> <p>There is no predetermined number, as it will vary from year-to-year depending on positions available under faculties. Please indicate the faculty you would like to work with in your application.</p> <p></p> <h4 class="wp-block-heading" id="toc-5.10"><strong>What is JHU’s policy on parental leave?</strong></h4> <p>PhD students at Johns Hopkins are fully covered for <a href="https://benefits.jhu.edu/health-and-life/student_health/overview.cfm">medical (Cigna), dental (Delta Dental), and vision (EyeMed)</a>. Plans are available for masters students as well.</p> <p>“All eligible full-time graduate students and postdoctoral trainees shall receive no less than 8 weeks of fully-paid new child accommodations”. For more information, see:</p> <p><a href="https://homewoodgrad.jhu.edu/student-services/family-resources-for-students-and-postdoctoral-fellows/">Family Resources for Students and Postdoctoral Fellows | Graduate & Postdoctoral Affairs</a><a href="https://www.jhu.edu/assets/uploads/2017/06/newchildaccommgradandpostdoc.pdf"></a></p> <p>Additionally, JHU offers parents of young children <a href="https://hr.jhu.edu/benefits-worklife/family-programs/child-care/jhu-child-care-vouchers/">childcare vouchers</a>.</p> <h2 class="wp-block-heading" id="toc-6">Lingering Questions</h2> <p>Feel free to contact us with additional questions not answered here.</p> <p>You can reach out to a number of current graduate students who will be able and willing to answer your questions or by sending an email to <a href="https://www.notion.so/JHU-CLSP-Admissions-a8e2dfd3634346398539cfb3a0aac336"><span class="__cf_email__" data-cfemail="97f6f3fafee4e4fef8f9e4d7f4e4b9fdffe2b9f2f3e2">[email protected]</span></a> or 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class="ai1ec-weekday">Fri</div> </a> <div class="ai1ec-date-events"> <div class="ai1ec-event ai1ec-event-id-28122 ai1ec-event-instance-id-3477 "> <a href="https://www.clsp.jhu.edu/events/chenhao-tan-university-of-chicago-beyond-human-preferences-use-human-goals-to-guide-ai-towards-complementary-ai-2/?instance_id=3477" class="ai1ec-popup-trigger ai1ec-load-event"> <span class="ai1ec-event-time"> 12:00 pm </span> <span class="ai1ec-event-title"> Chenhao Tan (University of Chica... <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> </a> <div class="ai1ec-popover ai1ec-popup ai1ec-event-instance-id-3477"> <span class="ai1ec-popup-title"> <a href="https://www.clsp.jhu.edu/events/chenhao-tan-university-of-chicago-beyond-human-preferences-use-human-goals-to-guide-ai-towards-complementary-ai-2/?instance_id=3477" class="ai1ec-load-event" >Chenhao Tan (University of Chica...</a> <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> <div class="ai1ec-event-time"> Nov 22 @ 12:00 pm – 1:15 pm </div> <div class="ai1ec-popup-excerpt">Abstract Human preferences have been the main component in guiding pretrained AI. In this talk, I argue that human preferences are often insufficient for complementing human intelligence and demonstrate the key role of human goals[...]</div> </div> </div> </div> </div> </div> <div class="ai1ec-subscribe-buttons-widget"> <a class="ai1ec-btn ai1ec-btn-default ai1ec-btn-xs ai1ec-pull-right ai1ec-calendar-link" href="https://www.clsp.jhu.edu/seminars/"> View Calendar <i class="ai1ec-fa ai1ec-fa-arrow-right"></i> </a> <div class="ai1ec-subscribe-dropdown ai1ec-dropdown ai1ec-btn ai1ec-btn-default ai1ec-btn-xs"> <span role="button" class="ai1ec-dropdown-toggle ai1ec-subscribe" data-toggle="ai1ec-dropdown"> <i class="ai1ec-fa ai1ec-icon-rss ai1ec-fa-lg ai1ec-fa-fw"></i> <span class="ai1ec-hidden-xs"> Add <span class="ai1ec-caret"></span> </span> </span> <ul class="ai1ec-dropdown-menu ai1ec-pull-left" role="menu"> <li> <a class="ai1ec-tooltip-trigger ai1ec-tooltip-auto" target="_blank" data-placement="right" title="Copy this URL for your own Timely calendar or click to add 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data-nummobile="5" data-channel-subscribers="121 subscribers" data-subscribe-btn="1" data-subscribe-btn-text="Subscribe" data_channel_header_colors ="{"channelName":"","subscribeCount":"","buttonBackground":"","buttonText":""}" data-sby-flags="resizeDisable" data-postid="4142" data-sby-supports-lightbox="1" data-videocardlayout="vertical" > <div class="sby_items_wrap" style="padding: 5px;"> <div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_cf4apIcnPtU" data-date="1732073606" data-video-id="cf4apIcnPtU"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=cf4apIcnPtU" target="_blank" rel="noopener" data-full-res="https://i4.ytimg.com/vi/cf4apIcnPtU/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/default.jpg","320":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/mqdefault.jpg","480":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/hqdefault.jpg","640":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/sddefault.jpg"}" data-video-id="cf4apIcnPtU" data-video-title="Generative World Explorer"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Project page: https://generative-world-explorer.github.io/ Paper: https://arxiv.org/abs/2411.11844 Abstract: Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can imagine unseen parts of the world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions, without necessitating the physical exploration of the world at all times. To achieve this human-like ability, we introduce the Generative World Explorer (Genex), an egocentric world exploration framework that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and acquire imagined observations to update its belief. This updated belief will then help the agent to make a more informed decision at the current step. To train Genex, we create a synthetic urban scene dataset, Genex-DB. Our experimental results demonstrate that (1) Genex can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e.g., an LLM agent) to make better plans."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Generative World Explorer</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_d5p6IhagfXM" data-date="1731992425" data-video-id="d5p6IhagfXM"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=d5p6IhagfXM" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/d5p6IhagfXM/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/sddefault.jpg"}" data-video-id="d5p6IhagfXM" data-video-title="Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="December 6, 2013 Abstract In this talk, I will discuss how to automatically derive the orderings and meanings of gradable adjectives. To determine whether the intended answer is “yes” or “no” in a dialogue such as “Was the movie wonderful? It was worth seeing”, we need to evaluate how “worth seeing” relates to “wonderful”. Can we automatically learn from real texts the scalar orderings people assign to these modifiers? I will show how we can exploit the availability of large amounts of text on the web (such as online reviews ratings) to approximate these orderings. Then I will turn to neural network language models. I will show that continuous space word representations extracted from such models can be used to derive adjectival scales of high quality, emphasizing that neural network language models do capture semantic regularities. I evaluate the quality of the adjectival scales on several datasets. Next, I will briefly turn to biomedical data: what does it mean to show “severe symptoms of cardiac disease” or “mild pulmonary symptoms”? I will outline work in progress targeting the meaning of gradable adjectives in that domain. Not only do we want to get an ordering between such adjectives, but we also want to learn what counts as “severe” or “mild” symptoms of a disease. Biography Marie-Catherine de Marneffe is an assistant professor in Linguistics at The Ohio State University. She received her PhD from Stanford University in December 2012 under the supervision of Christopher D. Manning. She is developing computational linguistic methods that capture what is conveyed by speakers beyond the literal meaning of the words they say. Primarily she wants to ground meanings in corpus data, and show how such meanings can drive pragmatic inference. She has also worked on Recognizing Textual Entailment and contributed to defining the Stanford Dependencies representation, which is designed to be a practical representation of grammatical relations and predicate argument structure"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_uf6PSqy3WPc" data-date="1731645479" data-video-id="uf6PSqy3WPc"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=uf6PSqy3WPc" target="_blank" rel="noopener" data-full-res="https://i2.ytimg.com/vi/uf6PSqy3WPc/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/default.jpg","320":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/mqdefault.jpg","480":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/hqdefault.jpg","640":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/sddefault.jpg"}" data-video-id="uf6PSqy3WPc" data-video-title="Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Zhili Feng: https://zhilif.github.io/"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_X_U_jdXXfv0" data-date="1731004488" data-video-id="X_U_jdXXfv0"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=X_U_jdXXfv0" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/X_U_jdXXfv0/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/sddefault.jpg"}" data-video-id="X_U_jdXXfv0" data-video-title="AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Paper: https://arxiv.org/abs/2402.12370 Abstract: Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We test a broad collection of proprietary models (e.g., GPT family, Claude V2) and open source models such as LLaMA2. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_JVhXsqlzsWI" data-date="1730935150" data-video-id="JVhXsqlzsWI"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=JVhXsqlzsWI" target="_blank" rel="noopener" data-full-res="https://i3.ytimg.com/vi/JVhXsqlzsWI/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i3.ytimg.com\/vi\/JVhXsqlzsWI\/default.jpg","320":"https:\/\/i3.ytimg.com\/vi\/JVhXsqlzsWI\/mqdefault.jpg","480":"https:\/\/i3.ytimg.com\/vi\/JVhXsqlzsWI\/hqdefault.jpg","640":"https:\/\/i3.ytimg.com\/vi\/JVhXsqlzsWI\/sddefault.jpg"}" data-video-id="JVhXsqlzsWI" data-video-title="Parisa Rashidi: AI and Pervasive Sensing: The Silent Guardians of Tomorrow's ICU"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Abstract In the high-stakes world of critical care, every second counts. Traditional manual patient monitoring, while effective, strains our healthcare system’s resources. Nurses, already stretched thin, repetitively assess acute care indices like physical function. Meanwhile, crucial patient data— cognitive function, sleep quality, and environmental factors—often slip through the cracks or lack granular detail. Artificial Intelligence (AI) and pervasive sensing promise to revolutionize patient care in the Intensive Care Unit (ICU). By seamlessly integrating with continuous physiologic measurements and clinical data, they offer a real-time, interpretable, and precise assessment of patient condition. We have developed AI system that augment traditional clinical data with pervasive sensing, capturing previously unmeasured or manually recorded acute care indices. This breakthrough forms the cornerstone of our vision: to sense, quantify, and communicate a patient’s condition with unprecedented accuracy and clarity. This presentation will unveil the potential of pervasive sensing and AI in acute care settings. We’ll explore how these technologies are set to reshape current medical practices and spark a dialogue about the integration of intelligent, pervasive health technology in hospitals. Biography Dr. Parisa Rashidi is the founding co-director of the Intelligent Clinical Care Center (IC3) at the University of Florida (UF) and a professor at the J. Crayton Pruitt Family Department of Biomedical Engineering (BME). She is also affiliated with the Electrical & Computer Engineering (ECE) and Computer and Information Science and Engineering (CISE) departments. Her research aims to bridge the gap between machine learning and patient care. Dr. Rashidi is a fellow of the American Institute for Medical and Biological Engineering (AIMBE), the National Science Foundation (NSF) CAREER awardee, the National Institute of Health (NIH) Trailblazer Awardee, Herbert Wertheim College of Engineering Leadership Excellence Awardee, Herbert Wertheim College of Engineering Assistant Professor Excellence Awardee, and a recipient of the UF term professorship. She also received UF’s Provost Excellence Award for assistant professors; with more than 500 tenure-track assistant professors at UF, Dr. Rashidi is one of only 10 to receive this award. She was invited by the National Academy of Engineering (NAE) as one of only 38 outstanding US engineers under 45 to participate in the EU-US Frontiers of Engineering (FOE) Meeting. To date, she has authored 170+ peer-reviewed publications. She has chaired several workshops and symposiums on intelligent health systems and has served on the program committee of 20+ conferences. Dr. Rashidi’s research has been supported by local, state, and federal grants, including awards from the National Institutes of Health (NIBIB, NCI, and NIGMS) and the National Science Foundation (NSF)."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Parisa Rashidi: AI and Pervasive Sensing: The Silent Guardians of Tomorrow's ICU</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div> </div> <div class="sby_footer"> <a class="sby_load_btn" href="javascript:void(0);" > <span class="sby_btn_text" >Load More...</span> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> <span class="sby_follow_btn" > <a href="https://www.youtube.com/channel/UCWWiAEF7PMk-OK0-rwGITIg/" target="_blank" rel="noopener" > <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 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