A discriminative method for global query expansion and term reweighting using co-occurrence graphs

This article presents a new query expansion (QE) method aiming to tackle term mismatch in information retrieval (IR). Previous research showed that selecting good expansion terms which do not hurt retrieval effectiveness remains an open and challenging research question. Our method investigates how global statistics of term co-occurrence can be used effectively to enhance expansion term selection and reweighting. Indeed, we build a co-occurrence graph using a context window approach over the entire collection, thus adopting a global QE approach. Then, we employ a semantic similarity measure inspired by the Okapi BM25 model, which allows to evaluate the discriminative power of words and to select relevant expansion terms based on their similarity to the query as a whole. The proposed method includes a reweighting step where selected terms are assigned weights according to their relevance to the query. What’s more, our method does not require matrix factorisation or complex text mining processes. It only requires simple co-occurrence statistics about terms, which reduces complexity and insures scalability. Finally, it has two free parameters that may be tuned to adapt the model to the context of a given collection and control co-occurrence normalisation. Extensive experiments on four standard datasets of English (TREC Robust04 and Washington Post) and French (CLEF2000 and CLEF2003) show that our method improves both retrieval effectiveness and robustness in terms of various evaluation metrics and outperforms competitive state-of-the-art baselines with significantly better results. We also investigate the impact of varying the number of expansion terms on retrieval results.

Novel Discriminative Method for Illegal Parking and Abandoned Objects

Computer vision techniques have been widely applied in Intelligent Transportation Systems (ITSs) to automatically detect abnormal events and trigger alarms. In the last few years, many abnormal traffic events, such as illegal parking, abandoned objects, speeding, and overloading, have occurred on the highway, threatening traffic safety. In order to distinguish illegal parking and abandoned object events, we propose an effective method to classify these types of abnormal objects. First, abnormal areas are detected by feature point extraction and matching. The transformation relation, between the world and image coordinate systems, is then established by camera calibration. Next, different-height inverse projection planes (IPPs) are built to obtain the inverse projection maps (IPMs). Finally, the 3D information describing the abnormal objects is estimated and used to distinguish illegally parked vehicles and abandoned objects. Experimental results from traffic image sequences show that this method is effective in distinguishing illegal parking and abandoned objects, while its low computational cost satisfies the real-time requirements; furthermore, it can be used in vehicle classification.

Festive Culture of Kryashens

<p>Traditional festive ritual culture occupies an important position in the life of the Kryashens. The article is based on our own field research conducted in 2014. The purpose of the article is to study traditional holidays and their significance for the Kryashens. The article showed popular traditional Kryashen holidays, their innovations and origins, which go deep into history and are closely intertwined with the culture of neighboring peoples. The methodological base of the study assumes the consideration and the analysis of the traditional festive culture of the Kryashens. The work uses general historical methods: historical-comparative, cultural-anthropological, the method of complex analysis and the discriminative method. The work is also based on the combination of quantitative and qualitative methods: discourse - mass survey through questionnaires, in-depth interviews, focus groups, included monitoring. The article gives a detailed description of each holiday. Kryashen people keep the ancient traditions of their ancestors, combining their Turkic roots and Orthodox culture. During the long parallel development of national holidays, customs and religions, Christianity has become an integral part of the Kryashen spiritual life - this confirms the special significance of Orthodox religious holidays. Also, ethnic-cultural characteristics and the celebration of traditional holidays are of great importance for Kryashens. Particularly honored calendar holidays for the Kryashens are the following ones: Easter, Christmas, Epiphany, Petrov Day (Pitrau), Trinity, Nardugan, Semik, Pokrov. These festive traditions are marked by a certain important value and stability in the cultural environment of the Tatarstan Kryashens. The materials of the article can be useful for ethnologists, social and cultural anthropologists, and everyone interested in this topic.</p>

A Comparison of Molecular Typing Methods Applied to Enterobacter cloacae complex: hsp60 Sequencing, Rep-PCR, and MLST

Molecular typing using repetitive sequenced-based PCR (rep-PCR) and hsp60 sequencing were applied to a collection of diverse Enterobacter cloacae complex isolates. To determine the most practical method for reference laboratories, we analyzed 71 E. cloacae complex isolates from sporadic and outbreak occurrences originating from 4 geographic areas. While rep-PCR was more discriminating, hsp60 sequencing provided a broader and a more objective geographical tracking method similar to multilocus sequence typing (MLST). In addition, we suggest that MLST may have higher discriminative power compared to hsp60 sequencing, although rep-PCR remains the most discriminative method for local outbreak investigations. In addition, rep-PCR can be an effective and inexpensive method for local outbreak investigation.

Brain Tumor Progression Modeling - A Data Driven Approach

Malignant gliomas are highly heterogeneous brain tumors with complex an- isotropic growth patterns and occult invasion. Computational modeling of cell migration and proliferation has been subject of intensive research aiming at a deeper understanding of the tumor biology and the ability to predict growth and thus improve therapy. However, current modeling techniques follow a generative approach and make strong assumptions about underlying mechanisms. The tumor is so far treated as homogeneous entity with behavioral parameters extrapolated from previous longitudinal image information. We present a novel way of approaching this problem by employing data driven, discrim- inative modeling techniques that learn relevant features from observed growth patterns and are able to make meaningful predictions solely on basis of local and regional tissue characteristics at one given point in time. We demonstrate superior performance of the proposed discriminative method (DICE score 83) compared to the state of the art generative approach (DICE score 72) on six patients and a total of nine different time intervals. Our approach can help estimating occult invasion as well as it can advance our understanding of the tumor biology and lead to valuable predictions of tumor growth patterns that could guide and improve radio therapy.