Bayesian reordering model with feature selection

In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language. We explore generative learning approach to phrase reordering in Arabic to English. Formulating the reordering problem as a classification problem and using naive Bayes with feature selection, we achieve an improvement in the BLEU score over a lexicalized reordering model. The proposed model is compact, fast and scalable to a large corpus

Learning Classical Planning Strategies with Policy Gradient

A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner's state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner's performance relative to the baselines of plain best-first search and a uniform policy.Comment: Accepted for ICAPS 201

Towards learning domain-independent planning heuristics

Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key factors restricting the applicability of planning is its computational complexity resulting from exponentially large search spaces. Heuristic approaches are necessary to solve all but the simplest problems. In this work, we explore the possibility of obtaining domain-independent heuristic functions using machine learning. This is a part of a wider research program whose objective is to improve practical applicability of planning in systems for which the planning domains evolve at run time. The challenge is therefore the learning of (corrections of) domain-independent heuristics that can be reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality and Autonom

M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks

In this paper, we propose a new routing protocol for heterogeneous Wireless Body Area Sensor Networks (WBASNs); Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficientMulti-hop ProTocol (M-ATTEMPT). A prototype is defined for employing heterogeneous sensors on human body. Direct communication is used for real-time traffic (critical data) or on-demand data while Multi-hop communication is used for normal data delivery. One of the prime challenges in WBASNs is sensing of the heat generated by the implanted sensor nodes. The proposed routing algorithm is thermal-aware which senses the link Hot-spot and routes the data away from these links. Continuous mobility of human body causes disconnection between previous established links. So, mobility support and energy-management is introduced to overcome the problem. Linear Programming (LP) model for maximum information extraction and minimum energy consumption is presented in this study. MATLAB simulations of proposed routing algorithm are performed for lifetime and successful packet delivery in comparison with Multi-hop communication. The results show that the proposed routing algorithm has less energy consumption and more reliable as compared to Multi-hop communication.Comment: arXiv admin note: substantial text overlap with arXiv:1208.609

Frequency of Genetic Polymorphisms of CYP2C19 in Native Hawaiian, and Asian and Pacific Islander Subgroups: Implications for Personalized Medicine

Pharmacogenetic testing, where prescriptions are tailored to the individual patient based on his/her genetic makeup, increases the ability to predict individual drug response. However, little is known about the prevalence of clinically actionable pharmacogenes in diverse populations. This study seeks to assess the prevalence of select drug-gene alleles that are implicated in the metabolism of commonly prescribed drugs, so-called Very Important Pharmacogenes (VIPs). The results of this study will fill in the gaps of knowledge of VIPs in underrepresented populations and characterize their potential risk for drug adverse events or due to their underlying genetic polymorphisms, especially in patients of Asian, Hawaiian or Marshallese, or Samoan descent. The Ensemble genome browser was used to compare the frequencies of three major single nucleotide polymorphisms (SNPs) in the cytochrome P450 subfamily 2 class 19 (CYP2C19) in European (EUR) with our studied populations. Specially, SNPs of interest included rs4244285 G\u3eA, rs4986893 G\u3eA, and rs12248560 C\u3eT, for CYP2C19*2, *3, and *17, respectively. In this cross-sectional study, chi-square or Fisher’s exact test was used, when appropriate, with P \u3c 0.05 for significance. Biobank DNA samples of 1064 participants were used to calculate genotype and allele frequencies for our population groups. The sample was distributed across six self-reported ethnicities; Filipino (21.61%), Japanese (19.73%), Korean (9.77%), Hawaiian (14.84%), Marshallese (15.13%), and Samoan (18.89%). In each ethnicity from our population, the distributions of allele and genotype frequencies of the CYP2C19 *2 (rs4244285 G\u3eA), *3 (rs4986893 G\u3eA), and *17 (rs12248560 C\u3eT) variants were significantly different from EUR. The overall loss-of-function allele (A) frequencies of *2 (rs4244285 G\u3eA) and *3 (rs4986893 G\u3eA) were significantly higher in our population groups (25%-36% and 2.5%-10%, respectively) than EUR (15%, and 0%, respectively). In contrast, the overall increased function allele (T) frequencies of *17 (rs12248560 C\u3eT) were significantly higher in EUR (22.5%) than in our population (1%-6%). In conclusion, our results are consistent with published reports of Asian populations are enriched with the reduced or loss of function alleles of CYP2C19 compared with EUR.https://scholarscompass.vcu.edu/gradposters/1136/thumbnail.jp

STUDENT ENGAGEMENT AND MATH TEACHERS SUPPORT

This study aimed to investigate the factors that influence student engagement in mathematics classes. It explored the relationship among emotional, organizational, and instructional support and the impacts of characteristics of teacher, such as years of experience, and sexual orientation, on student engagement. Data were taken from the Consortium for Political and Social Research. The study was involved mathematics teachers and encompassed three years of data collection and observation. Data were collected first hand through classroom observations and student–teacher surveys. In this study, ANOVA, t-test, and partial correlation were employed to evaluate the relationships among the study variables based on participants’ responses. The relationship between student engagement and instructional support weakened after controlling for emotional and organizational support. However, instructional support continued to significantly influence student engagement. In addition, results showed a significant difference in student engagement attributed to the teacher’s gender. Results revealed the interaction between gender and years of experience significantly influenced student engagement, which was in favor of female teachers

Pulmonary rehabilitation and cardiovascular risk in COPD: a systematic review

Introduction: Pulmonary Rehabilitation (PR) is an effective intervention in COPD however the value of PR in reducing cardiovascular risk in COPD (measured by aortic pulse wave velocity, aPWV) is unclear and there is no existing systematic review. Objectives: To conduct a systematic review examining whether PR results in alteration of CV risk in COPD (as measured by aPWV). Methods: An electronic systematic search concordant with PRISMA guidelines was conducted. The search was complete to the 27th of May 2017. Six databases were examined: Embase, Medline, AMED, Web of Science, Cochrane clinical trials, and CINAHL. Results: This study generated 767 initial matches, which were filtered using inclusion/exclusion criteria. Three studies (201 COPD participants) were included. Our analysis does not confirm that PR affects aPWV but studies were heterogeneous. Conclusion: There is currently insufficient information on the effect of PR on reducing CV risk in COPD. Therefore controversy remains, with the possibility that there might be some subjects who benefit and others who might experience an increase in CV risk in response to PR. These results will be of value to those interested in gaining a better understanding of the benefits of PR on CV risk in COPD

An energy scaled and expanded vector-based forwarding scheme for industrial underwater acoustic sensor networks with sink mobility

Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay