Abstract

In this paper, we present a solution to generate semantically richer descriptions and instructions for driver assistance and safety. Our solution builds upon a set of computer vision and machine learning modules. We start with low-level image processing and finally generate high-level descriptions. We do this by combining the results of the image pattern recognition module with the prior knowledge on traffic rules and larger context present in the video sequence. For recognition of road markings, we use a SVM based classifier and HOG based classifier. We test our method on real data captured in urban settings, and report impressive performance. Qualitative and quantitative performance of various modules are presented.

Abstract

Proteins display characteristic dynamical signatures that appear to be universal across all proteins regardless of topology and size. Here, we systematically characterize the universal features of fast side chain motions in proteins by examining the conformational energy surfaces of individual residues obtained using enhanced sampling molecular dynamics simulation (618 free energy surfaces obtained from 0.94 μs MD simulation). The side chain conformational free energy surfaces obtained using the adaptive biasing force (ABF) method for a set of eight proteins with different molecular weights and secondary structures are used to determine the methyl axial NMR order parameters (Oaxis 2), populations of side chain rotamer states (ρ), conformational entropies (Sconf), probability fluxes, and activation energies for side chain interrotameric transitions. The free energy barriers separating side chain rotamer states range from 0.3 to 12 kcal/mol in all proteins and follow a trimodal distribution with an intense peak at∼ 5 kcal/mol and two shoulders at∼ 3 and∼ 7.5 kcal/mol, indicating that some barriers are more favored than others by proteins to maintain a balance between their conformational stability and flexibility. The origin and the influences of the trimodal barrier distribution on the distribution of Oaxis 2 and the side chain conformational entropy are discussed. A hierarchical grading of rotamer states based on the conformational free energy barriers, entropy, and probability flux reveals three distinct classes of side chains in proteins. A unique nonlinear correlation is established between Oaxis 2 and the side chain rotamer populations (ρ). The …

Abstract

Entity Disambiguation is the task of associating entity name mentions in text to the correct referent entities in the knowledge base, with the goal of understanding and extracting useful information from the document. Entity disambiguation is a critical component of systems designed to harness information shared by users on microblogging sites like Twitter. However, noise and lack of context in tweets makes disambiguation a difficult task. In this paper, we describe an Entity Disambiguation system, EDIUM, which uses User interest Models to disambiguate the entities in the user’s tweets. Our system jointly models the user’s interest scores and the context disambiguation scores, thus compensating the sparse context in the tweets for a given user. We evaluated the system’s entity linking capabilities on tweets from multiple users and showed that improvement can be achieved by combining the user models and the context based models.

Abstract

The last two decades have witnessed an exponential rise in web content from a plethora of domains, which has necessitated the use of domain-specific search engines. Diversity of crawled content is one of the crucial aspects of a domain-specific search engine. To a large extent, diversity is governed by the initial set of seed URLs. Most of the existing approaches rely on manual effort for seed selection. In this work we automate this process using URLs posted on Twitter. We propose an algorithm to get a set of diverse seed URLs from a Twitter URL graph. We compare the performance of our approach against the baseline zero similarity seed selection method and find that our approach beats the baseline by a significant margin.

Abstract

The velocity, volume and variety with which Twitter generates text is increasing exponentially. It is critical to determine latent sub-topics from such tweet data at any given point of time for providing better topic-wise search results relevant to users’ informational needs. The two main challenges in mining sub-topics from tweets in real-time are (1) understanding the semantic and the conceptual representation of the tweets, and (2) the ability to determine when a new sub-topic (or cluster) appears in the tweet stream. We address these challenges by proposing two unsupervised clustering approaches. In the first approach, we generate a semantic space representation for each tweet by keyword expansion and keyphrase identification. In the second approach, we transform each tweet into a conceptual space that represents the latent concepts of the tweet. We empirically show that the proposed methods outperform the state-of-the-art methods.

Abstract

Entity Disambiguation is the task of associating entity name mentions in text to the correct referent entities in the knowledge base, with the goal of understanding and extracting useful information from the document. Entity disambiguation is a critical component of systems designed to harness information shared by users on microblogging sites like Twitter. However, noise and lack of context in tweets makes disambiguation a difficult task. In this paper, we describe an Entity Disambiguation system, EDIUM, which uses User interest Models to disambiguate the entities in the user’s tweets. Our system jointly models the user’s interest scores and the context disambiguation scores, thus compensating the sparse context in the tweets for a given user. We evaluated the system’s entity linking capabilities on tweets from multiple users and showed that improvement can be achieved by combining the user models and the context based models.

Abstract

To determine the significance of urine albumin/creatinine ratio (UACR) as to predict proteinuria in new-onset hypertension and uric acid in women with preeclampsia and its comparison with healthy normotensive pregnant women. Methods. Healthy normotensive pregnant women (n= 45), women with preeclampsia (n= 36) were included in the study. The preeclamptic group was further divided into two subgroups: mild (n= 25) and severe preeclampsia (n= 11). Results. Higher mean values of urine albumin/creatinine ratio (UACR) and uric acid were found in preeclamptic pregnant women (319.05±247.56 mg/g, 5.98±2.1 mg/dl) as compared to healthy normotensive pregnant women (22.15±8.1 mg/g, 4.07±0.91 mg/dl) in the third trimester with highly significant difference (p< 0.001) statistically. When comparison of the mean values of urine albumin/creatinine ratio and uric acid was done between mild (199.9±46.78 mg/g, 5.24±1.89 mg/dl) and severe preeclamptic pregnant women (589.67±305.38 mg/g, 7.64±1.4 mg/dl), the values were found to be elevated in the latter group with highly significant difference (p< 0.001) statistically. The positive and highly significant correlation was found in UACR/blood pressure, uric acid/blood pressure and UACR/uric acid. Conclusion. It was concluded that UACR and serum uric acid levels increased as disease progressed from mild to severe conditions.

Abstract

Bilingual dictionaries are the key component of the cross-lingual similarity estimation methods. Usually such dictionary generation is accomplished by manual or automatic means. Automatic generation approaches include to exploit parallel or comparable data to derive dictionary entries. Such approaches require large amount of bilingual data in order to produce good quality dictionary. Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora. In this work we propose a method which exploits continuous quasi-comparable corpora to derive term level associations for enrichment of such limited dictionary. Though we propose our experiments for English and Hindi, our approach can be easily extendable to other languages. We evaluated dictionary by manually computing the precision. In experiments we show our approach is able to derive interesting term level associations across languages.

Abstract

For privacy reasons, personally identifiable information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the fields of advertising, forensics and business intelligence. Existing methods for this problem have focused on classifier learning using content based features like word n-grams and style based features like Part of Speech (POS) n-grams. Two major drawbacks of previous approaches are:(1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. We propose a novel method to address these drawbacks by representing the document using Wikipedia concepts and category information. Experimental results show that classifiers learned using such features along with previously used features help us achieve significantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features.

Abstract

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weatherbased decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology0 domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.