Graphical Models and Symmetries : Loopy Belief Propagation Approaches

Whenever a person or an automated system has to reason in uncertain domains, probability theory is necessary. Probabilistic graphical models allow us to build statistical models that capture complex dependencies between random variables. Inference in these models, however, can easily become intractable. Typical ways to address this scaling issue are inference by approximate message-passing, stochastic gradients, and MapReduce, among others. Exploiting the symmetries of graphical models, however, has not yet been considered for scaling statistical machine learning applications. One instance of graphical models that are inherently symmetric are statistical relational models. These have recently gained attraction within the machine learning and AI communities and combine probability theory with first-order logic, thereby allowing for an efficient representation of structured relational domains. The provided formalisms to compactly represent complex real-world domains enable us to effectively describe large problem instances. Inference within and training of graphical models, however, have not been able to keep pace with the increased representational power. This thesis tackles two major aspects of graphical models and shows that both inference and training can indeed benefit from exploiting symmetries. It first deals with efficient inference exploiting symmetries in graphical models for various query types. We introduce lifted loopy belief propagation (lifted LBP), the first lifted parallel inference approach for relational as well as propositional graphical models. Lifted LBP can effectively speed up marginal inference, but cannot straightforwardly be applied to other types of queries. Thus we also demonstrate efficient lifted algorithms for MAP inference and higher order marginals, as well as the efficient handling of multiple inference tasks. Then we turn to the training of graphical models and introduce the first lifted online training for relational models. Our training procedure and the MapReduce lifting for loopy belief propagation combine lifting with the traditional statistical approaches to scaling, thereby bridging the gap between statistical relational learning and traditional statistical machine learning

A mini-review on the most important effective medicinal plants to treat hypertension in ethnobotanical evidence of Iran.

Nowadays, cardiovascular diseases are highly prevalent in human communities. Hypertension is a multifactorial disease which causes a mortality twice higher than general population. Given the fact that medicinal plants have long been used to treat hypertension and are currently being administered for this disease, we sought to report the mostly effective and important medicinal plants on hypertension therapy in ethno-botanical evidence of Iran. In this study, hypertension, Iran, ethno-botany, medicinal plants, and traditional medicine were used as key words to search in Web of Science, PubMed, Scopus, EBSCO and EMBASE to select relevant articles. The findings of this study indicated that in Iran 40 plants in various provinces are used to treat hypertension. Because medicinal plants in this study contain effective compounds and have long been used to treat and reduce hypertension, they could provide suitable research arrangements for controlling hypertension, while effective natural drugs could be developed to control hypertension if their properties are confirmed in pharmacological studies

Water cut/salt content forecasting in oil wells using a novel data-driven approach

International audienceWater cut is an important parameter in reservoir management and surveillance. Unlike traditional approaches, including numerical simulation and analytical techniques, which were developed for predicting water production in oil wells based on some assumptions and limitations, a new data-driven approach is proposed for forecasting water cut in two different types of oil wells in this article. First, a classification approach is presented for water cut prediction in sweet oil wells with discontinuous salt production patterns. Different classification algorithms including Support Vector Machine (SVM), Classification Tree (CT), Random Forest (RF), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA) and Naïve Bayes (NB) are investigated in this regard. According to the results of a case study on a real Iranian sweet oil well, RF, CT, MLP and SVM can provide the best performance measures, respectively. Next, a Vector Autoregressive (VAR) model is proposed for forecasting water cut in salty oil wells with continuous water production during the life of the well. The proposed VAR model is verified using data of two real salty oil wells. The results confirm that the well-tuned proposed VAR model could provide reliable and acceptable results with very good accuracy in forecasting water production for the near future days

Limited packings: related vertex partitions and duality issues

A $k$-limited packing partition ($k$LP partition) of a graph $G$ is a partition of $V(G)$ into $k$-limited packing sets. We consider the $k$LP partitions with minimum cardinality (with emphasis on $k=2$). The minimum cardinality is called $k$LP partition number of $G$ and denoted by $\chi_{\times k}(G)$. This problem is the dual problem of $k$-tuple domatic partitioning as well as a generalization of the well-studied $2$-distance coloring problem in graphs. We give the exact value of $\chi_{\times2}$ for trees and bound it for general graphs. A section of this paper is devoted to the dual of this problem, where we give a solution to an open problem posed in $1998$. We also revisit the total limited packing number in this paper and prove that the problem of computing this parameter is NP-hard even for some special families of graphs. We give some inequalities concerning this parameter and discuss the difference between $2$TLP number and $2$LP number with emphasis on trees

GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science

Many collective human activities have been shown to exhibit universal patterns. However, the possibility of universal patterns across timing events of researcher migration has barely been explored at global scale. Here, we show that timing events of migration within different countries exhibit remarkable similarities. Specifically, we look at the distribution governing the data of researcher migration inferred from the web. Compiling the data in itself represents a significant advance in the field of quantitative analysis of migration patterns. Official and commercial records are often access restricted, incompatible between countries, and especially not registered across researchers. Instead, we introduce GeoDBLP where we propagate geographical seed locations retrieved from the web across the DBLP database of 1,080,958 authors and 1,894,758 papers. But perhaps more important is that we are able to find statistical patterns and create models that explain the migration of researchers. For instance, we show that the science job market can be treated as a Poisson process with individual propensities to migrate following a log-normal distribution over the researcher's career stage. That is, although jobs enter the market constantly, researchers are generally not "memoryless" but have to care greatly about their next move. The propensity to make k>1 migrations, however, follows a gamma distribution suggesting that migration at later career stages is "memoryless". This aligns well but actually goes beyond scientometric models typically postulated based on small case studies. On a very large, transnational scale, we establish the first general regularities that should have major implications on strategies for education and research worldwide

Contrasting actions of various antioxidants on hyperlipidemia: A review and new concepts

Hyperlipidemia can lead to disorders that result in the onset of various diseases, including cardiovascular diseases which are the leading cause of death in many industrialized countries. Antioxidants are recommended in the treatment of various diseases, particularly atherosclerosis. However, the results of the studies are inconclusive and do not provide strong evidence that antioxidants have a substantial effect on disease. From the results of the studies presented in this paper it might be concluded that although antioxidants might be beneficial in patients with atherosclerosis or other cardiovascular diseases, however, single or even combination of a few antioxidants are not reliable agents for this purpose. This might be due to the complexity of free radicals which are produced and work as a continuous chain. It is known that after scavenging electron, if an antioxidant is not restored by the following antioxidant in the chain, it usually changes to a pro-oxidant. In this situation, the final effect of such supplementation would be no or a damaging effect. In this review study, other than presenting and discussing the studied antioxidants on hyperlipidemia and cardiovascular diseases, the possible reasons for the opposing actions of different antioxidants are discussed in detail

Clinical, Histologic and Histomorphometric Evaluation of Bone Strip Allograft with Resorbable Membrane in Horizontal Alveolar Ridge Augmentation: A Preliminary Study

Objective: Alveolar ridge preservation in patients with inadequate bone volume is one treatment option for successful implant placement and can be done by using bone graft materials. On the other hand, Ceno Bone has been recently produced by Hamanand Saz Baft Kish Co. as a bone bioimplant of allograft origin. This study aimed to assess the clinical, histologic and histomorphometric results of Bone Strip Allograft (CenoBone) for horizontal alveolar ridge augmentation.Methods: In this semi-experimental clinical trial, 7 areas requiring horizontal ridge augmentation  and subsequent implant placement in the maxilla were selected using non-randomized consecutive sampling. Surgeries were mostly performed via the buccal cortical plate of the edentulous ridge. The buccal bone was decorticated, Ceno Bone was fixed by titanium screws, covered with Ceno Membrane (resorbable) and sutured. Buccolingual width of the ridge was measured in stage-one surgery and six months later in stage-two surgery for implant placement. A core biopsy was also taken to assess the trabecular thickness, percentage of new bone formation, percentage of remnant particles, degree of inflammation, foreign body reaction, vitality, bone-biomaterial contact and number of blood vessels by microscopic, histologic and histomorphometric analyses of the slides. The clinical ridge width values in the first- and second-stage surgeries were analyzed using  Wilcoxon Signed Rank test.Results: The mean clinical ridge width at 2mm distance from the ridge crest was 2.49 (0.72) mm in the first-stage and 4.79 (0.75) mm in the second-stage surgery. The mean clinical ridge width at  5mm distance from the ridge crest was 3.6 (0.57) mm in the first-stage and 6.3 (1.13) mm in the second-stage surgery. At both sites, application of Ceno Bone significantly increased the clinical ridge width in the second-stage surgery (both ps<0.05). Also, inflammation in most specimens (85.7%) was grade I and no case of foreign body reaction was seen. Bone was vital in all patients. The  mean  trabecular  thickness was  87.96  (38.74)μ.  The percentage  of new  bone  formation was58.43 (26.42%) and the percentage of remnant particles was 4.07% (2.44%).Conclusion: The results of this preliminary study revealed that application of CenoBone stimulates osteogenesis and significantly increases the clinical ridge width at 2 and 5mm distances from the ridge crest for implant placement

Overview of medicinal plants used for cardiovascularsystem disorders and diseases in ethnobotany of differentareas in Iran

Background and Aims: Today, cardiovascular diseases are the prominent cause of death in industrialized countries which include a variety of diseases such as hypertension, hyperlipidemia, thromboembolism, coronary heart disease, heart failure, etc. Recent research findings haveshown that not only the extent of cultivation and production of medicinal plants have not beenreduced, but also day-to-day production and consumption have increased. In traditional botanicalknowledge, herbal medicines are used for the treatment of cardiovascular disorders. In this study,we sought to gather and report medicinal plants used to treat these diseases in different regionsof Iran.Methods: The articles published about ethnobotanical study of cardiovascular diseases in variousregions of Iran, such as Arasbaran, Sistan, Kashan, Kerman, Isfahan Mobarakeh, Lorestan andIlam were prepared and summarized.Results: The results of ethnobotanical studies of various regions of Iran, such as Arasbaran, Sistan,Kashan, Kerman, Isfahan Mobarakeh, Lorestan and Ilam were gathered. The results showed thatsumac plants, barberry, yarrow, wild cucumber, horsetail, Eastern grape, hawthorn, wild rose,spinach, jujube, buckwheat, chamomile, chicory, thistle, Mary peas, nightshade, verbena, sorrel ,cherry, citrullus colocynthis, Peganum harmala, sesame and so many other plants are used for thetreatment of cardiovascular diseases and disorders.Conclusion: Herbal medicines are used effectively for some cardiovascular diseases. Rigoroustraining of patients to take precautions and drug interactions into account and to avoid thearbitrary use of medicinal plants is very important