Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations

We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like $\textit{tf-idf}$, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen datasets.Comment: 5 pages, 3 figures. Rep4NLP workshop at ACL 201

Zero-shot Neural Transfer for Cross-lingual Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-based entity linking, which leverages information from a high-resource "pivot" language to train character-level neural entity linking models that are transferred to the source low-resource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of 54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario. Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.Comment: To appear in AAAI 201

OCR Post Correction for Endangered Language Texts

There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.Comment: Accepted to EMNLP 202

A biologically inspired optical flow system for motion detection and object identification

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on April 7, 2008)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Electrical and computer engineering.Optical flow is possibly the best known method for motion segmentation. However its application is restricted to offline processing as it requires extensive computational resources and time. This thesis explores an optical flow method derived from observation on vision system of diptereous insect. The proposed method , Biological Optical flow (BioOF) was implemented using series of first order filters, and, therefore is much faster than any existing machine coded optical flow algorithm beside being hardware implement able. Like other optical flow methods, the output of proposed BioOF has two components: horizontal optical flow and vertical optical flow; both of them can be combined in order to get a better final result in terms of motion segmentation. Unfortunately, this combined output of the BioOF can be heavily coupled with noise. So, in order to remove the noise, intensive image processing had to be performed. The result was an algorithm that can provide a good contour of the segmented object in an image. Finally the object contour is converted to a Fourier feature space leading to a representation that is rotational and translational invariant. Over this feature space various classification algorithms including SVM, feature subset forward selection, Scatter matrix, and a simple linear classifier using principal component analysis and Mahanabolis distance were investigated

Role of acoustic radiation force impulse elastography, aminotransferase to platelet ratio index and fibrotest for the assessment of significant fibrosis and cirrhosis in chronic liver disease

Background: Accurate grading of hepatic fibrosis is important for the application of appropriate intervening strategy. Liver biopsy is the golden standard of fibrotic grading, however wide clinical application is hindered by its inherent drawbacks. Biomechanical-based ultrasonic elastography has received mass attention. However, several clinical studies found that the sole application of ultrasonic elastography may bring evident errors in diagnosing hepatic fibrosis. It is suggested that a combination of ultrasonic elastography and serum liver functions tests holds the potential to overcome those disadvantages. Aims and objectives was to study the diagnostic accuracy of ultrasonography elastography, APRI, fibrotest for significant fibrosis and cirrhosis in patients with chronic liver disease and established the correlation between ARFI elastography, APRI, Fibrotest in grading of liver fibrosisMethods: Sixty three patients with chronic liver disease were studied.  Liver stiffness was evaluated with ARFI elastography. Histologic staging of liver fibrosis served as the reference standard except a very few cirrhotic patients who were graded as cirrhotic on the basis of clinical examination. The required APRI, Fibrotest parameters and relevant clinical history was recorded.  Fibrosis stage was assessed according to the METAVIR classification.Results: ARFI, APRI, and Fibrotest demonstrated a significant correlation with the histological stage. According to ARFI and APRI for evaluating fibrotic stages more than F2, ARFI showed an enhanced diagnostic accuracy than APRI. The combined measurement of ARFI and APRI exhibited better accuracy than ARFI alone when evaluating ≥ F2 fibrotic stage that showed  significant concordance  i.e. 79.3% cases,  out of which 69.8% of total cases were correctly diagnosed on comparison with the gold standard. Fibrotest and ARFI elastography show significant concordance in grading of fibrosis i.e. 82.5%. Cases out of which 68.3% of total cases were correctly diagnosed on comparison with the gold standard.Conclusions: APRI, ARFI, and fibrotest are novel tools among non-invasive modalities to rule out significant fibrosis and cirrhosis in patients with chronic liver disease. ARFI with APRI and ARFI with fibrotest showed enhanced diagnostic accuracy than ARFI or APRI or fibrotest alone for significant liver fibrosis

Human-Robot Teaming Configurations: A Study of Interpersonal Communication Perceptions and Affective Learning in Higher Education

Technology encourages collaboration in creative ways in the classroom. Specifically, social robots may offer new opportunities for greater innovation in teaching. In this study, we combined the established literature on co-teaching teams with the developing field of machine actors used in education to investigate the impressions students had of different team configurations that included both a human and a robot. Participants saw one of three teams composed of a human and a social robot with different responsibilities present a short, prerecorded lecture (i.e., human as lead teacher-robot as teaching assistant, robot as lead teacher-human as teaching assistant, human and robot as co-teachers). Overall, students rated the human-led team as more appealing and having more credibility than the robot-led team. The data suggest that participants would be more likely to take a course led by a human instructor than a social robot. Previous studies have investigated machine actors in the classroom, but the current findings are unique in that they compare the individual roles and power structures of human-robot teams leading a course

Association of non-alcoholic fatty liver disease with chronic kidney disease in type 2 diabetes mellitus

Background: Non-alcoholic fatty liver disease (NAFLD) is closely associated with metabolic syndrome. NAFLD is considered a disease of no consequence. Data on the effect of NAFLD on renal dysfunction in T2DM is sparse. Author aimed to study the association of NAFLD with CKD in Indian T2DM subjects.Methods: In an observational cross-sectional study at Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India from February 2017 to March 2018. 197 out of 268 randomly selected type 2 diabetes mellitus (T2DM) subjects were selected for the study after considering the inclusion and exclusion criteria. CKD was defined as estimated GFR <60 ml/min per 1.73 m2 and/or albumin to creatinine ratio ≥30 mg/g. NAFLD was diagnosed using ultrasonography. The association between NAFLD and CKD was analyzed using SPSS (version 24.0).Results: On ultrasonography 133 (67.5%) T2DM subjects had NAFLD. Diabetic with NAFLD (133, 67.51%) had significantly more history of hypertension (p 0.006), higher systolic (p 0.03) and diastolic BP (p 0.009), higher BMI (p <0.001), waist circumference (p <0.001), fasting glucose (p 0.03), triglyceride (p<0.001) and higher urinary albumin-to-creatinine ratio (p <0.001). Diabetics with CKD (61, 30.96%), were older (p 0.03), hypertensive (p <0.001) and had higher fasting glucose (p 0.003). Subjects with CKD had a higher prevalence of underlying NAFLD (78.69% vs 62.5%, p 0.03) as compared with diabetics with no CKD. T2DM subjects with NAFLD had more than two times (OR 2.88 (1.1-6.78), p 0.03) the risk of developing CKD after multivariate analysis as compared to subjects without NAFLD.Conclusions: NAFLD is a risk factor for development of CKD in patients of type 2 diabetes mellitus. Screening and early preventive measures may go long way in reducing morbidity