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Learning task-specific similarity

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Algorithms for Similarity Search and Pseudorandomness

  • Computer Science

Research output : Book / Anthology / Report / Ph.D. thesis › Ph.D. thesis

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  • PhD Thesis Final Version Tobias Lybecker Christiani Final published version, 1.38 MB

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  • Similarity Search Keyphrases 100%
  • Pseudorandomness Keyphrases 100%
  • Approximate Nearest Neighbor Search Keyphrases 100%
  • Hash Function Computer Science 100%
  • Hashing Computer Science 100%
  • Efficient Algorithm Computer Science 50%
  • Join Algorithm Computer Science 50%
  • Cosine Similarity Computer Science 50%

T1 - Algorithms for Similarity Search and Pseudorandomness

AU - Christiani, Tobias Lybecker

N2 - We study the problem of approximate near neighbor (ANN) searchand show the following results:• An improved framework for solving the ANN problem usinglocality-sensitive hashing, reducing the number of evaluationsof locality-sensitive hash functions and the word-RAM complexity compared to the standard framework.• A framework for solving the ANN problem with space-timetradeoffs as well as tight upper and lower bounds for the spacetime tradeoff of framework solutions to the ANN problemunder cosine similarity.• A novel approach to solving the ANN problem on sets alongwith a matching lower bound, improving the state of theart. A self-tuning version of the algorithm is shown throughexperiments to outperform existing similarity join algorithms.• Tight lower bounds for asymmetric locality-sensitive hashingwhich has applications to the approximate furthest neighborproblem, orthogonal vector search, and annulus queries.• A proof of the optimality of a well-known Boolean localitysensitive hashing scheme.We study the problem of efficient algorithms for producing highquality pseudorandom numbers and obtain the following results:• A deterministic algorithm for generating pseudorandom numbers of arbitrarily high quality in constant time using nearoptimal space.• A randomized construction of a family of hash functions thatoutputs pseudorandom numbers of arbitrarily high qualitywith space usage and running time nearly matching knowncell-probe lower bounds.

AB - We study the problem of approximate near neighbor (ANN) searchand show the following results:• An improved framework for solving the ANN problem usinglocality-sensitive hashing, reducing the number of evaluationsof locality-sensitive hash functions and the word-RAM complexity compared to the standard framework.• A framework for solving the ANN problem with space-timetradeoffs as well as tight upper and lower bounds for the spacetime tradeoff of framework solutions to the ANN problemunder cosine similarity.• A novel approach to solving the ANN problem on sets alongwith a matching lower bound, improving the state of theart. A self-tuning version of the algorithm is shown throughexperiments to outperform existing similarity join algorithms.• Tight lower bounds for asymmetric locality-sensitive hashingwhich has applications to the approximate furthest neighborproblem, orthogonal vector search, and annulus queries.• A proof of the optimality of a well-known Boolean localitysensitive hashing scheme.We study the problem of efficient algorithms for producing highquality pseudorandom numbers and obtain the following results:• A deterministic algorithm for generating pseudorandom numbers of arbitrarily high quality in constant time using nearoptimal space.• A randomized construction of a family of hash functions thatoutputs pseudorandom numbers of arbitrarily high qualitywith space usage and running time nearly matching knowncell-probe lower bounds.

M3 - Ph.D. thesis

SN - 978-87-7949012-3

T3 - ITU-DS

BT - Algorithms for Similarity Search and Pseudorandomness

PB - IT-Universitetet i København

Learning Task-Specific Similarity

The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example- based learning methods. Similarity is commonly defined in terms of a conventional distance function, but such a definition does not necessarily capture the inherent meaning of similarity, which tends to depend on the underlying task. We develop an algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples. Our learning algorithm constructs, in a greedy fashion, an encoding of the data. This encoding can be seen as an embedding into a space, where a weighted Hamming distance is correlated with the unknown similarity. This allows us to predict when two previously unseen examples are similar and, importantly, to efficiently search a very large database for examples similar to a query.

This approach is tested on a set of standard machine learning benchmark problems. The model of similarity learned with our algorithm provides and improvement over standard example-based classification and regression. We also apply this framework to problems in computer vision: articulated pose estimation of humans from single images, articulated tracking in video, and matching image regions subject to generic visual similarity.

Thesis chapters

  • Front matter (of little scientific interest)
  • Chapter 1: Introduction

This chapter defines some technical concepts, most importantly the notion of similarity we want to model, and provides a brief overview of the contributions of the thesis.

  • Chapter 2: Background

Among the topics covered in this chapter: example-based classification and regression, previous work on learning distances and (dis)similarities (such as MDS), and algorithms for fast search and retrieval, with emphasis on locality sensitive hashing (LSH).

  • Chapter 3: Learning embeddings that reflect similarity
  • Similarity sensitive coding (SSC). This algorithm discretizes each dimension of the data into zero or more bits. Each dimension is considered independently of the rest.
  • Boosted SSC. A modification of SSC in which the code is constructed by greedily collecting discretization bits, thus removing the independence assumption.

The underlying idea of all three algorithms is the same: build an embedding that, based on training examples of similar pairs, maps two similar objects close to each other (with high probability). At the same time, there is an objective to control for "spread": the probability of arbitrary two objects (in particular of dissimilar pairs of objects, if examples of such pairs are available) to be close in the embedding space should be low.

This chapter also describes results of an evaluation of the proposed algorithms on seven benchmarks data sets from UCI and Delve repositories.

  • Chapter 4: Articulated pose estimation

An application of the ideas developed in previous chapters to the problem of pose estimation: inferring the articulated body pose (e.g. the 3D positions of key joints, or values of joint angles) from a single, monocular image containing a person.

  • Chapter 5: Articulated tracking

In a tracking scenario, a sequence of views, rather than a single view of a person, is available. The motion provides additional cues, which are typically used in a probabilistic framework. In this chapter we show how similarity-based algorithms have been used to improve accuracy and speed of two articulated tracking systems: a general motion tracker and a motion-driven animation system focusing on swing dancing.

  • Chapter 6: Learning image patch similarity

An important notion of similarity that is naturally conveyed by examples is the visual similarity of image regions. In this chapter we focus on a particular definition of such similarity, namely invariance under rotation and slight shift. We show how the machinery developed in Chapter 3 allows us to improve matching performance for two popular representations of image patches.

  • Chapter 7: Conclusions
  • Bibliography

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similarity search phd thesis

Plagiarism and what are acceptable similarity scores?

Dec 1, 2020 • knowledge article, information.

The Similarity Report is a flexible document that provides a summary of matching or similar text in submitted work compared against a huge database of Internet sources, journals and previously submitted work, allowing students and instructors to review matches between a submitted work and the database scanned by Turnitin. Therefore, the Turnitin Similarity Report does not define whether or not a student's work is plagiarized. The instructor responsible for the course - as a subject matter expert - has a duty to exercise academic judgement on the work that is submitted to Turnitin for their classes. The percentage that is returned on a student's submission (called similarity index or similarity score) defines how much of that material matches other material in the database, it is not a marker as to whether a student has or has not plagiarized. Matches will be displayed to material that has been correctly cited and used, which is where the instructor's academic judgement must come into play. Please find our guide links below on how to interpret the Similarity Report and its similarity score: If you are a student, click here . If you are an instructor, click here . 

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Why and what?

Maastricht University endorses the principles of scientific integrity and therefore provides services to check for the similarity between documents. Separate services are provided for research and educational purposes.

Every UM-affiliated researcher can use this service. Ithenticate – provided by TurnItIn – compares your submitted work to millions of articles and other published works and billions of webpages.

Check a manuscript / PhD thesis

This tool is for research purposes only!

Not for educational purposes

The Similarity Check Service is not intended for educational purposes (e.g., checking master’s theses for plagiarism). Please use Turnitin Originality instead (available through the digital learning environment Canvas). Turnitin Originality is tailored to the specific requirements for educational purposes.

The maximum number of submissions for these services is adapted to their respective purposes.

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In case you are in doubt about which similarity check service to use for a particular purpose, please contact us so we can find a suitable solution for you while guaranteeing the sustainable availability of the services for all UM scholars.

Plagiarism and how to prevent it

Plagiarism is using someone else’s work or findings without stating the source and thereby implying that the work is your own. When using previously established ideas that add pertinent information in a research paper, every researcher should be cautious not to fall into the trap of sloppy referencing or even plagiarism.

Plagiarism is not just confined to papers, articles or books, it can take many forms (for more information, see this infographic by iThenticate ).

The Similarity Check Service can help you to prevent only one type of plagiarism: verbatim plagiarism, and only if the source is part of the corpus.

The software does not automatically detect plagiarism; it provides insight into the amount of similarity in the text between the uploaded document and other sources in the corpus of the software. This does not mean this part of the text is viewed as plagiarism in your specific field. For instance, the methods section in some subfields follows very common wording, which could lead to a match. If there are instances where the submission’s content is similar to the content in the database, it will be flagged for review and should be evaluated by you.

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Getting started.

Go to iThenticate and enter your UM username and password in the appropriate fields. Select ‘login’.

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As a first-time user, you will then have to check your personal information and declare that you agree to the Terms and conditions.

similarity search phd thesis

3. My Folders and My Documents iThenticate

iThenticate will provide you with a folder group My Folders and a folder within that group titled My Documents.

similarity search phd thesis

From the My Documents folder, you will be able to submit a document by selecting the Submit a document link.

similarity search phd thesis

4. Upload a file

On the Upload a file page, enter the authorship details and the document title. Select Choose File and locate the file on your device.

similarity search phd thesis

Select the Add another file link to add another file. You can add up to ten files before submitting. Select Upload to upload the document(s).

5. Similarity Report

To view the Similarity Report for the paper, select the similarity score in the Report column. It usually takes a couple of minutes for a report to generate.

similarity search phd thesis

Finding your way around

The main navigation bar at the top of the screen has three tabs. Upon logging in, you will automatically land on the folders page.

similarity search phd thesis

This is the main area of iThenticate. From the folders page, you will be able to upload, manage and view documents.

The settings page contains configuration options for the iThenticate interface.

Account Info

The account information page contains the user profile and account usage.

Options for exclusion

There can be various reasons why you may want to exclude certain sources that your document is compared to or certain parts of your document in the similarity check. You can specify options for exclusion in the Folder settings.

similarity search phd thesis

If you choose to exclude ‘small matches’, you will be asked to specify the minimum number of words that you want to be shown as a match.

If you choose to exclude ‘small sources’, you will be asked to specify a minimum number of words or a minimum match percentage.

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How to read the similarity report

The similarity report provides the percentage of similarity between the submitted document and content in the iThenticate database. This is the type of report that you will use most often for a similarity check.

It is perfectly natural for a submitted document to match against sources in the database, for example if you have used quotes. 

The similarity score simply makes you aware of potential problem areas in the submitted document. These areas should then be reviewed to make sure there is no sloppy referencing or plagiarism.

iThenticate should be used as part of a larger process, in order to determine if a match between your submitted document and content in the database is or is not acceptable.

This video shows how to read the various reports

This video shows how the Document viewer works  

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Everyone involved in teaching and research at Maastricht University shares in the responsibility for maintaining academic integrity (see Scientific Integrity ). All academic staff at UM are expected to adhere to the general principles of professional academic practice at all times.

Adhering to those principles also includes preventing sloppy referencing or plagiarism in your publications.

Additional information on how to avoid plagiarism can be also be found in the Copyright portal of the library.

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iThenticate compares the submitted work to 60 million scholarly articles, books, and conferences proceedings from 115,000 scientific, technical, and medical journals, 114 million Published works from journals, periodicals, magazines, encyclopedias, and abstracts, 68 billion current and archived web pages.

Checking PhD theses

The similarity check service (iThenticate) can be used by doctoral candidates or their supervisors to assess the work. Find out about the level of similarity with other publications and incorrect referencing before you send (parts of) the thesis to the Assessment Committee, a publisher or send in the thesis for deposit in the UM repository.

We kindly request you submit the whole thesis as one document (i.e. not per chapter) and only once to prevent unnecessary draws on the maximum number of submissions, as our contract provides a limited number of checks.

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similarity search phd thesis

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Turnitin Access To Plagiarism Check For GMS Students

Turnitin is an online plagiarism checking tool that compares your work with existing online publications.

To gain access:

  • Request  access  to the   Plagiarism-Check Blackboard site
  • Access to this site will be continuous throughout your time in GMS

How it works:

  • Upload your papers to Blackboard Learn to check for similarity index
  • Submit  multiple versions of papers, take home assignments, theses or dissertations and rewrite text as needed

Extended Directions:

  • You will be asked to give your name, email BUID and the submission type (i.e. Dissertation, Thesis or Paper) and your GMS program affiliation.
  • After verifying you are not a robot, you be should sent to a new page and get a notification saying “We have received your request to be added to the plagiarism check Blackboard Learn site.” It will take some time for your request to be approved (at least 10 min, possibly 24 hr). The next time you log in to Blackboard, under ‘My Courses’ in the right-hand column you should see the entry:  “GMS Plagiarism Check”.
  • Then click on “>> View/Complete”  (The first time you use Turnitin it will ask you to agree the user agreement). If you have never submitted a document before there should be a submit button.  If you have submitted before you will need to click the resubmit button to submit your new or revised document.  You may get a warning that resubmitting will replace your earlier submission.  It also reminds you that you can only upload 3 documents in 24 hours.
  • You can browse to find the document on your computer then click ‘upload’
  • You will get a message saying it may take up to 2-min to load
  • Once the document is loaded be sure to click on ‘confirm’
  • You should get a congratulatory message.  (You will also get an email confirming the successful submission.)
  • Click on the link to return to submission list
  • You will likely see that your document ‘similarity’ index is ‘processing.’  The algorithm may take a few hours to run.  You will need check back and see when it has finished.
  • Press “view” on the far right to see the submitted manuscript with Turnitin’s Feedback Studio which has some interesting automation features.
  • Alternatively, you can download the results with the arrow icon on the far right of the display
  • NOTE:  Students cannot submit more than 3 documents in a 24hour period.  Please plan accordingly or use an alternatively resource like Turnitin Draft Coach alternative via Google Doc
  • It is recommended that you remove the bibliography/references prior to submission to Turnitin.
  • Because Turnitin cannot scan Images and Figures these can also be removed prior to the check. You must manually check images and figures for plagiarism and potential copyright violations.
  • Published manuscripts should be removed from your thesis or dissertation prior to submission to Turnitin, assuming that they have already been analyzed by Turnitin. If they have not been, they should be included. Unpublished manuscripts should be included in your submission to Turnitin. • How to interpret a “Turnitin Originality Report”

 How to Interpret Your Score Report

  •  Similarity index, Similarity by Source, Internet Sources, Publications, & Student Papers
  • Generally, the similarity index should be less than 20%.
  • A common definition may be acceptable.
  • Did it identify methods or protocols from your lab? These will need to be rewritten.
  • Did it identify matches to publications or text from the internet—again these sentences, paragraphs,  sections will need to be rewritten
  • If you have any questions interpreting the Turnitin report please reach out to your faculty mentors.
  • Once you have made edits it is important to resubmit the document for a final check.
  • The final Turnitin report should be submitted to your mentor and first reader for approval.
  • Having problems with Turnitin:  Reach out to Dr. Theresa Davies for assistance ( [email protected] )

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A.  THESIS SUBMISSION REQUIREMENTS FOR EXAMINATION (GS-15a/GS-15b)

Abstract – • between 300 – 500 words. • written in both English and Bahasa Melayu. • include Keywords (not more than 5 keywords) and SDGs (not more than 3 SDG).

The margins of pages are as follows: top : 25 mm right : 25 mm bottom : 25 mm left : 40 mm, spacing – • double spacing for text. • single spacing for footnotes, long quotations, references or bibliography, multi-line captions, appendices (such as questionnaires, letter) and headings or subheadings., white simile a4 size (210mm x 297mm) paper (80g) or paper of equivalent quality should be used. students must include an extra blank sheet for the front and back of the thesis. photocopies of the thesis must be on similar quality paper (if a hardcopy of thesis required by the examiner)., the thesis, along with the required documents, should be submitted via email to [email protected] ..

  • Thesis A4 Format (Template)
  • Please refer to the Guide to Thesis Preparation for other details format of the thesis. 
  • You are advised not to use software for graphics and diagrams which may complicate conversion and documents transmission.

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COMMENTS

  1. What is the acceptable similarity in a mathematics PhD dissertation

    A similarity index between 20-40 percent generally means there is a problem unless a large portion of text that should have been skipped was not (e.g., block quotes, reference lists, or appendices of common tables). A similarity index in excess of 40 percent is almost always problematic. You really should not depend on the overall similarity index.

  2. PDF High-Dimensional Similarity Search for Large Datasets

    to conduct content-based similarity search, e.g., to search for images with content similar to a query image. How to build data structures to support efficient similarity search on a large scale is an issue of increasing importance. The challenge is that feature-rich data are usually represented as high-dimensional feature vectors, and the

  3. High-dimensional similarity search and sketching : algorithms and hardness

    The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 241-255). by Ilya Razenshteyn. ... a new C++ library for high-dimensional similarity search. ' An efficient algorithm for the ANN problem over any distance that ...

  4. PDF Representation Learning for Efficient and Effective Similarity Search

    Similarity Search and Recommendation Advisors: Stephen Alstrup, Christina Lioma, Jakob Grue Simonsen Handed in: April 28, 2021 This thesis has been submitted to the PhD School of The Faculty of Science, University of Copenhagen. Abstract How data is represented and operationalized is critical for building computational

  5. Learning task-specific similarity

    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006. ... The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example-based learning methods. ... importantly, to efficiently search a ...

  6. PDF Efficient and Effective Similarity Search in Image Databases

    1.3 Thesis Outline The work of the present thesis is organized in six Chapters and two Appendices as follows: In Chapter 2, we explore the state-of-the-art on similarity search, and interactive simi-larity search in image DBs, pointing out major limits suffered by current image retrieval

  7. PDF UTS Similarity Search

    timestamp is unavailable or unknown. Traditional similarity search techniques used for standard time series are not always effective for uncertain time series data analysis. This motivates our work in this dissertation. We investigate new, efficient solution techniques for similarity search

  8. [PDF] Learning task-specific similarity

    An algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples, which allows to predict when two previously unseen examples are similar and to efficiently search a very large database for examples similar to a query. The right measure of similarity between ...

  9. Algorithms for Similarity Search and Pseudorandomness

    TY - BOOK. T1 - Algorithms for Similarity Search and Pseudorandomness. AU - Christiani, Tobias Lybecker. PY - 2018. Y1 - 2018. N2 - We study the problem of approximate near neighbor (ANN) searchand show the following results:• An improved framework for solving the ANN problem usinglocality-sensitive hashing, reducing the number of evaluationsof locality-sensitive hash functions and the word ...

  10. Scalable Similarity Search

    The Scalable Similarity Search project, led by professor Rasmus Pagh and hosted at the IT University of Copenhagen, is funded by the European Research Council (ERC).The project runs from May 2014 to April 2019. The team includes 3 PhD students and 3 post-docs. The aim of the project is to improve theory and practice of algorithms for high-dimensional similarity search on big data, and to ...

  11. Learning Task-Specific Similarity

    Chapter 3: Learning embeddings that reflect similarity; This is the main algorithmic "meat" of the thesis: this chapter describes three algorithms for learning an embedding of the data into a weighted Hamming space, with the objective for L1 distance there to reflect the underlying similarity. The algorithms are: Similarity sensitive coding (SSC).

  12. OATD

    You may also want to consult these sites to search for other theses: Google Scholar; NDLTD, the Networked Digital Library of Theses and Dissertations.NDLTD provides information and a search engine for electronic theses and dissertations (ETDs), whether they are open access or not. Proquest Theses and Dissertations (PQDT), a database of dissertations and theses, whether they were published ...

  13. Plagiarism and what are acceptable similarity scores?

    Plagiarism and what are acceptable similarity scores? The Similarity Report is a flexible document that provides a summary of matching or similar text in submitted work compared against a huge database of Internet sources, journals and previously submitted work, allowing students and instructors to review matches between a submitted work and ...

  14. PDF GMS Master's and PhD Candidate Guidelines for using Turnitin

    All capstone, thesis and dissertation documents must be scanned using Turnitin plagiarism detection software. A final Turnitin report must be approved by your mentor, advisor, first reader (BU faculty) or external committee member(s) depending on the GMS program. A final Similarity Report must be submitted to your program director.

  15. Contextual Document Similarity for Content-based Literature Recommender

    context to the similarity. The contextual document similarity is defined as a triple of two documents and the context that specifies to what the similarity of the two documents relates. On a technical level, we will incorporate semantic features from text and links in. hybrid manner to represent documents.

  16. Similarity Index of Doctoral Theses Submitted to Universities in Kerala

    Faculty of Arts (26.6%) has a high degree of similarity followed by Humanities (26%) and lowest in Science (16%).Among the Universities University of Calicut occupied the top position with. 21 % similarity index in its PhD theses followed by M.G University having 20.4% and the University of Kerala has least with 17.9% of similarity index.

  17. A feature-based method for tire pattern similarity detection

    The tire pattern photos are then realized, measured, and detected for similarity following the rules of similarity theory. Experiments were designed to fit the framework of the given detection method. The test results prove that the proposed method can detect tire pattern similarity quickly and conveniently.

  18. iThenticate

    The similarity check service (iThenticate) can be used by doctoral candidates or their supervisors to assess the work. Find out about the level of similarity with other publications and incorrect referencing before you send (parts of) the thesis to the Assessment Committee, a publisher or send in the thesis for deposit in the UM repository.

  19. UK Doctoral Thesis Metadata from EThOS // British Library

    UK Doctoral Thesis Metadata from EThOS. The datasets in this collection comprise snapshots in time of metadata descriptions of hundreds of thousands of PhD theses awarded by UK Higher Education institutions aggregated by the British Library's EThOS service. The data is estimated to cover around 98% of all PhDs ever awarded by UK Higher ...

  20. Turnitin Access To Plagiarism Check For GMS Students

    Students should search their Blackboard site for "plagiarism" and see if they have access. If not, then follow this link to request access: Plagiarism-Check Blackboard site. You will be asked to give your name, email BUID and the submission type (i.e. Dissertation, Thesis or Paper) and your GMS program affiliation.

  21. How do I exclude irrelevant similarities from my similarity score?

    You can exclude these irrelevant similarities from your total similarity score. Exclude similarities: Step 1: Open your Plagiarism Check results. Step 2: Click on the highlighted similarity that you would like to exclude. Step 3: Click on the "Exclude" button on the right. The similarity is now excluded from your total similarity score.

  22. Thesis Submission Requirements for Examination

    Text Similarity Search Report (Turnitin) - ≤25% (≤20% only for students under the School of Business and Economics). ... Thesis: Minimum: Maiximum: PhD: 20,000 (~70 pages) 100,000 (~330 pages) ... Students must include an extra blank sheet for the front and back of the thesis. Photocopies of the thesis must be on similar quality paper (if a ...

  23. Finding theses

    We hold theses written by the University's Higher Degree by Research (PhD or Masters by Research) students in our collections. You can find a University of Sydney thesis by searching the Library catalogue. Select the "Advanced search" and then select "USYD Theses" from the "Material type" dropdown menu. You can also find digital ...

  24. PhD Dissertation Defense: Kasra Naftchi-Ardebili

    PhD Defense Dissertation phd defense. Title: Transcranial Ultrasound Stimulation: Optimizing Simulation Paradigms and Modulation of the Neurons Abstract: Transcranial focused ultrasound stimulation (TUS) is a promising, non-invasive method for neural modulation in treating neurological disorders. Traditional TUS faces challenges in targeting ...

  25. PhD Defense

    Abstract A study by FEMA suggests that 20-40% modern code-conforming buildings would be unfit for re-occupancy following a major earthquake (taking months or years to repair) and 15-20% would be rendered irreparable. The increasing human and economic exposure in seismically active regions emphasizes the urgent need to bridge the gap between national seismic design provisions (which do not ...