Abstract

In this paper, a new Adaptive Sliding Mode Control (ASMC) framework is proposed for the tracking control of a class of uncertain nonlinear systems where system states are present explicitly in the upper bound of the overall (or lumped) system uncertainty. Conventional ASMC strategies presume that either the overall (or lumped) uncertainty of the system or its time derivative is norm bounded by a constant. However, such assumption restricts the evaluation of the states a priori for the class of systems that has been considered in this paper. The proposed ASMC law does not presume such constant upper bound on the system uncertainties and, rather, exploits the unique structure of the uncertainty bound to design the control law. Moreover, the adaptive law of the proposed ASMC alleviates the overestimation-underestimation problems of switching gain which is commonly observed in the existing ASMC laws. Simulation results using a two-link manipulator demonstrate improved control performance of the proposed controller in comparison to the conventional ASMC.

Abstract

Preablative stimulated thyroglobulin (ps‑Tg) is an important investigation in the follow‑up of patients with Differentiated thyroid cancer(DTC) after surgery. Levels of ps‑Tg >2–10 ng/ml have been suggested to predict metastasis to cervical and extracervical sites. There is still debate on the need for routine iodine whole‑body scan (131I WBS) in the management of low‑to‑intermediate‑risk DTC patients. Objective: We analyzed our data of patients with DTC who underwent total thyroidectomy to discuss the predictability of ps‑Tg on metastatic disease on the 131I WBS. Materials and Methods: Retrospective analysis of patient records. Results: One hundred and seventeen patients with DTC (95 papillary thyroid cancer [71 had classic histology, 8 had tall cell variant, 16 had follicular variant] and 22 follicular thyroid cancer [18 minimally invasive, 2 hurtle cell, and 2 widely invasive cancers]) had undergone total thyroidectomy. All these patients underwent ps‑Tg assessment and an 131I WBS. About 65% of them went on to have radioiodine ablation along with a posttherapy 131I WBS. We divided the cohort into four groups based on their ps‑Tg levels: Group 1 (ps‑Tg <1), Group 2 (ps‑Tg 1–1.9), Group 3 (ps‑Tg 2–5), and Group 4 (ps‑Tg >5). None of the patients in Group 1, 7% of those combined in Groups 2 and 3 (2 out of 28 patients), and 26% (12 out of 47) of those in Group 4 had either cervical or extracervical metastasis. Those with extracervical metastatic disease to lungs and bones had a mean (standard deviation) ps‑Tg value of 436 (130) and median of 500 ng/ml and those with cervical metastatic disease had a mean Tg value of 31 (64) and median 6.6 ng/ml. Conclusions: A ps‑Tg value in the absence of anti‑Tgantibodies <1 ng/ml reliably excludes metastatic disease in DTC, while a value >5 ng/ml has a 26% risk of having either cervical or extracervical metastasis.

Abstract

Efficient discovery of interesting patterns using parallel algorithms is an actively studied topic in data mining. A key research issue related to this topic is data segmentation, which influences the overall computational requirements of an algorithm. This paper makes an effort to address this issue in correlated pattern mining. Two novel data segmentation techniques, ‘database segmentation’ and ‘transaction segmentation,’ have been introduced to discover the patterns efficiently. The former technique involves segmenting the database into multiple sub-databases such that each sub-database can be mined independently. The latter technique involves segmenting a transaction into multiple sub-transactions such that each sub-transaction can be processed as an individual transaction. The proposed techniques are algorithm independent, and therefore, can be incorporated into any parallel algorithm to find correlated patterns effectively. In this paper, we introduce map-reduce based pattern-growth algorithm by incorporating the above mentioned techniques. Experimental results demonstrate that the proposed algorithm is memory and runtime efficient and highly scalable as well.

Abstract

Weighted Frequent Itemset (WFI) mining is an important model in data mining. The popular adoption and successful industrial application of this model has been hindered by the following two obstacles: (i) finding WFIs is a computationally expensiveness process as these itemsets do not satisfy the downward closure property and (ii) lack of parallel algorithms to find WFIs in very large databases (e.g. astronomical data and twitter data). This paper makes an effort to address these two obstacles. Two pattern-growth algorithms, Sequential Weighted Frequent Pattern-growth and Parallel Weighted Frequent Pattern-growth, have been introduced to discover WFIs efficiently. Both algorithms employ three novel pruning techniques to reduce the computational cost effectively. The first pruning technique prunes some of the uninteresting items by employing a criterion known as cutoff weight. The second pruning technique, called conditional pattern base elimination, eliminates the construction of conditional pattern bases if a suffix item is an uninteresting item. The third pruning technique, called pattern-growth termination, defines a new terminating condition for the pattern-growth technique. Experimental results demonstrate that the proposed algorithms are memory and runtime efficient, and highly scalable as well.

Abstract

A professional player's expertise rests on the ability to predict action by optimally extracting the opponent's postural cues. Eye tracking (head-mounted system) data in a naturalistic singles badminton play was collected from one professional wor0ld-ranked player facing five amateur players (10 serves or 50 trials) and two amateurs playing against four other amateur players each (10 serves or 80 trials). The visual gaze on the opponent body, segregated into 3 areas-of-interest covering the feet, face/torso, and hand/racket of the opponent and the shuttle, was analysed for a) the period just before the serve, b) while receiving the serve and c) the entire rally. The comparative analysis shows the first area-of-interest for professional player as the opponent's feet while executing the serve and the hand/racket when receiving a serve. On the other hand, the amateur players show no particular strategy of fixation location either for the serve task or while facing a serve. The average fixation duration (just before serve) for the professional was 0.96s and for the amateurs it was 1.48s. The findings highlight the differences in the postural cue considered important and the preparatory time in professional and amateur players. We believe, analytical models from dynamic gaze behavior in naturalistic game conditions as applied in this study can be used for enhancing perceptual-cognitive skills during training.

Abstract

Efficacy of games aslearning medium is of interest to researchers and the gaming industry. A critical metric for learning is knowledge retention and very few studies have conducted in-depth comparisons of: a) game versus no-game learning, b) collaborative versus individual learning. Towards this, the study reported in this article will present the findings from an experiment using Asia and world maps, cut into pieces as in a jigsaw puzzle. The participants were primary school children who were randomly assigned to the puzzle and no-puzzle group. To understand the role of collaborative interactions in learning, each group was further divided into two subgroups. Each subgroup either solved the puzzle or studied the full map (no-puzzle) individually or collaboratively. Three post-tests were conducted over a period of 10 days. The mean scores and Mann Whitney test shows: a) In the no-puzzle condition, no difference in the average scores of the individual and collaborative groups for both maps was observed, b) In the puzzle condition, the collaborative group score was slightly more than that of the individual group for Asia map, while the difference was significant for the continent map, and c) Puzzle and no-puzzle individual group scores were comparative for Asia map but the continent map groups showed a major difference. The findings are mixed with collaborative puzzle solving showing higher retention while puzzle solving does not show significant effect on learning and retention.

Abstract

In this paper, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we approach is determining the credibility of the authors based on the predictions coming out to be true. The two specific problems we address are extracting the predictions from the articles and annotating with various prediction attributes. And then we determine the truth of these predictions, using Wikipedia as a credible source to extract relevant facts which can ascertain the validity of the predictions. We proposed and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We considered 28 news articles and extracted 97 predictions from these articles and the range of credibility scores(Fscores) for these articles are (0.57-0.71).

Abstract

We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting futurerelated information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.