The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain

Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model named MeSH-gram which relies on a straighforward approach that extends the skip-gram neural network model by considering MeSH (Medical Subject Headings) descriptors instead words. Trained on publicly available corpus PubMed MEDLINE, MeSH-gram is evaluated on reference standards manually annotated for semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. A deeper comparison is performed with tewenty existing models. All the obtained results of Spearman's rank correlations between human scores and computed similarities show that MeSH-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.Comment: 6 pages, 2 table

Study of flow through rockfill in channel = Etude des écoulements dans une mèche en canal

We present the results of an experimental, theoretical, and also numerical study of free surface buried flows that we have undertaken in the laboratory LSTE of INAT. This work was conducted as part of collaboration with the engineering international office MECATER which included the construction of a new channel with large section in INAT, this channel was designed to study the losses in porous media with high porosity for which Darcy's law is no longer applicable. The experiments consisted in determining the evolution of the free surface for different geometrical characteristics of riprap and different flow rates. These tests were used to verify different laws of head loss proposed in the literature, by performing numerical simulations of the evolution of the water surface for each experiment. The analysis of our results shows that the Stephenson’s formula is relevant to calculate the non-linear head loss for flows with high Reynolds number of pore. Furthermore, the simulations have highlighted the important role of porosity. These results should be confirmed by further tests to assess in particular the influence of the slope and to analyze the behavior of the flow at the inlet and outlet of the riprap

Evolution of flow velocities in a rectangular channel with homogeneous bed roughness.

The flow velocity above large scale roughness is investigated for low and steep slope between 0 and 4 %. The experiments were conducted in the laboratory of Fluid Mechanics Institute of Toulouse -IMFT. For the velocity measurement, the channel is equipped with a fast camera and a lightening system. The originality of the study lies in the application of a particle tracking technique (Particle Tracking Velocimetry). It is a non-intrusive measurement technique to measure instantaneous velocity in two-dimensional fields in stationary and unsteady flows. Analysis of the results is performed by processing images taken by the camera using a developed detection algorithm. A logarithmic distribution has been found for The velocity profiles which are influenced by roughness and slope. The obtained results show a depression of the maximum speed below the free surface. This behavior indicates a delay of the flow in the vicinity of the free surface and this is a direct consequence of the presence of secondary flows in these areas

Link Prediction via Community Detection in Bipartite Multi-Layer Graphs

International audienceThe growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bipartite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art

Study of free surface flows in rectangular channel over rough beds.

This paper presents the results of an experimental and umerical study of fully developed flow in a straight rectangular open channel over rough beds. Conical ribs were placed on the flume bottom to simulate different bed roughness conditions. Acoustic Doppler Velocimetry (ADV) measurements were made to obtain the velocity components profiles as well as the Reynolds stress profiles, at various locations. The experimental results are validated by simulations using an algebraic stress model. These investigations could be useful for researches in the field of sediment transport, bank protection, etc

Analysis of Saint Venant Model Closure Laws from 3D Model Simulations

In this work, in a first step, the effects of secondary motions on the transverse distribution of the depth average velocity in free surface flows above non uniform bottom roughness is illustrated by simulations based on an anisotropic algebraic Reynolds stress model (3D). These 3D-simulations were applied to determine the wall friction and the dispersion terms present in the depth average momentum equation. In a second step, closure laws of these terms were tested to define a 2D-Saint Venant model which is solved to calculate the transverse profile of the depth averaged velocity. This process could allow analyze of scale change problems

Mean Velocity Predictions in Vegetated Flows

Vegetation plays an important role in influencing the hydrodynamic behavior, ecological equilibrium and environmental characteristics of water bodies. Several previous models have been developed, to predict hydraulic conditions in vegetated rivers, but only few are actually used in practice. In This paper six analytic model derived for submerged vegetation are compared and evaluate: Klopstra et al. (1997); Stone and Shen (2002); Van velzen (2003); Baptist et al. (2007); Huthoff et al. (2007) and Yang and Choi (2010). The evaluation of the flow formulas is based on the comparison with experimental data from literature using the criteria of deviation. Most descriptors show a good performance for predicting the mean velocity for rigid vegetation. However, the flow formulas proposed by Klopstra et al. (1997) and Huthoff et al. (2007) show the best fit to experimental data. Only for experiments with law density, these models indicate an underestimation. Velocity predicted for flexible vegetation by the six models is less accurate than the prediction in the case of rigid vegetation