19 research outputs found
Restricting Double-hatting to Safeguard International Arbitrations
No full textDouble-hatting is when an individual plays the dual role of an arbitrator and a legal counsel—a concept first introduced by Professor P. Sands during an IBA conference in 2009. While it hampers the credibility of the arbitral process, its proponents oppose a complete prohibition reflecting on its benefits. The author hypothesizes that this issue has been inadequately addressed in international commercial arbitrations in juxtaposition to international investment arbitrations. Supporting this, the author introduces the concept, tracing its judicial landscape and scholarly discourse in investment arbitrations highlighting the need to adopt a similar approach in commercial arbitrations. Thereafter, the definition of double-hatting in Article 6 (May 2020) and Article 4 (June 2021) of the draft Code of Conduct for Adjudicators in Investor-State Dispute Settlement is analyzed while concurrently proposing an analogous definition for international commercial arbitrations. Lastly, the author proposes a framework to restrict double-hatting to counteract its negative implications in international commercial arbitration
Suresh Chandra on Historiography of Civilisation: With reference to Dravidian Civilisation
No full textThis paper attempts to give a critical appraisal of Professor Suresh Chandra’s views on Historiography of Civilization with reference to Dravidian Civilization. “Historiography of Indian Civilization: Harappans, Dravidians, Aryans and Gandhi’s freedom struggle” (published in JICPR June 1996) and “Demythologizing History: Dravidians in Relation to Harappans and the Aryans” (presented in the seminar on Dravidian Philosophy organized by Dravidian University, Kuppam) are the two significant works which are devoted to Historiography of civilization by Prof. Suresh Chandra. This paper mainly confines to the first article since the second one, as the author himself stated, is an offshoot of the first
Self-supervised Representation Learning via Image Out-painting for Medical Image Analysis
No full textabstract: In recent years, Convolutional Neural Networks (CNNs) have been widely used in not only the computer vision community but also within the medical imaging community. Specifically, the use of pre-trained CNNs on large-scale datasets (e.g., ImageNet) via transfer learning for a variety of medical imaging applications, has become the de facto standard within both communities.
However, to fit the current paradigm, 3D imaging tasks have to be reformulated and solved in 2D, losing rich 3D contextual information. Moreover, pre-trained models on natural images never see any biomedical images and do not have knowledge about anatomical structures present in medical images. To overcome the above limitations, this thesis proposes an image out-painting self-supervised proxy task to develop pre-trained models directly from medical images without utilizing systematic annotations. The idea is to randomly mask an image and train the model to predict the missing region. It is demonstrated that by predicting missing anatomical structures when seeing only parts of the image, the model will learn generic representation yielding better performance on various medical imaging applications via transfer learning.
The extensive experiments demonstrate that the proposed proxy task outperforms training from scratch in six out of seven medical imaging applications covering 2D and 3D classification and segmentation. Moreover, image out-painting proxy task offers competitive performance to state-of-the-art models pre-trained on ImageNet and other self-supervised baselines such as in-painting. Owing to its outstanding performance, out-painting is utilized as one of the self-supervised proxy tasks to provide generic 3D pre-trained models for medical image analysis.Dissertation/ThesisMasters Thesis Computer Science 202
Analysis of Tweets for Social Media Health Applications
No full textabstract: Social networking sites like Twitter have provided people a platform to connect
with each other, to discuss and share information and news or to entertain themselves. As the number of users continues to grow there has been explosive growth in the data generated by these users. Such a vast data source has provided researchers a way to study and monitor public health.
Accurately analyzing tweets is a difficult task mainly because of their short length, the inventive spellings and creative language expressions. Instead of focusing at the topic level, identifying tweets that have personal health experience mentions would be more helpful to researchers, governments and other organizations. Another important limitation in the current systems for social media health applications is the use of a disease-specific model and dataset to study a particular disease. Identifying adverse drug reactions is an important part of the drug development process. Detecting and extracting adverse drug mentions in tweets can supplement the list of adverse drug reactions that result from the drug trials and can help in the improvement of the drugs.
This thesis aims to address these two challenges and proposes three systems. A generalizable system to identify personal health experience mentions across different disease domains, a system for automatic classifications of adverse effects mentions in tweets and a system to extract adverse drug mentions from tweets. The proposed systems use the transfer learning from language models to achieve notable scores on Social Media Mining for Health Applications(SMM4H) 2019 (Weissenbacher et al. 2019) shared tasks.Dissertation/ThesisMasters Thesis Computer Science 201
File Usage Analysis and Resource Usage Prediction: A Measurement-Based Study
No full textThis thesis demonstrates a practical methodology for file usage analysis and resource usage prediction using trace-data from a production system. A VAX 11/780 system running Berkeley UNIX was instrumented to gather file usage data, in the form of file-related system calls, and resource usage data for each process.First, a user-oriented analysis was done using the file usage data collected from the first measurement. The key aspect of this analysis is a characterization of users and files. Two characterization measures are employed: accesses-per-byte and file size. This new approach is shown to distinguish differences in files as well as in users, which can be used in efficient file system design, and in creating realistic test workloads for simulations. A multi-stage gamma distribution is shown to closely model the file usage measures. Even though overall file sharing is small, some files belonging to a bulletin board system are accessed by many users, simultaneously and otherwise.Next, the file usage data from the second measurement is analyzed using a few simple measures based on the notion of a file reference. The measures used are: fraction referenced, file size, reference-time, number of references, and inter-reference time. Neither the users nor the files were characterized in this analysis. It was shown that in most references, files were accessed completely, substantiating the argument for using access-per-byte measure in user-oriented analysis. It was also shown that most file references lasted for a short time, and that inter-reference time was 2 to 3 orders of magnitude larger than reference time.Finally, a probabilistic resource usage prediction scheme was developed, using the process resource usage data. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of a program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. (Abstract shortened with permission of author.)Made available in DSpace on 2014-12-15T19:26:02Z (GMT). No. of bitstreams: 1
8815335.pdf: 3539000 bytes, checksum: 14a76eb82a61f69463faf9998a462b62 (MD5)
Previous issue date: 1988Embargo set by: Seth Robbins for item 69754
Lift date: Forever
Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988
Can Knowledge Rich Sentences Help Language Models To Solve Common Sense Reasoning Problems?
No full textabstract: Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.Dissertation/ThesisMasters Thesis Computer Science 201
