Deep Metric Learning via Lifted Structured Feature Embedding

Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart. In this paper, we describe an algorithm for taking full advantage of the training batches in the neural network training by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. This step enables the algorithm to learn the state of the art feature embedding by optimizing a novel structured prediction objective on the lifted problem. Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning. Our experiments on the CUB-200-2011, CARS196, and Online Products datasets demonstrate significant improvement over existing deep feature embedding methods on all experimented embedding sizes with the GoogLeNet network.Comment: 11 page

Smart navigation system for electric vehicles charging

In the present time, there is still a lack of popularity in the use of electric vehicles, because of the actual disadvantages that they have. For this work presents the process of research and development of a web based application with the main purpose of helping Electric Vehicle owners decide the Charging Station that, by selecting it to go and charge their vehicles, represents the lowest cost in time or money (depending on their priorities) when they need to go to charge their electric vehicles and to give them less time or energy consuming route to follow in order to arrive to the charging station selected. This, to reduce the concern of the users about if the can or not arrive to a charging station. To do this, the application has been developed with several features to help the users. First, the application has the feature of being accessed from multiple type of devices. Second, the application has the feature of detecting the users locations using Global Positioning System. Third, the application has the ability to find the charging stations and their coordinates that are near to the users. Fourth, the application has the ability to formulate the route with the lowest time or energy cost between the users locations and the charging stations. Fifth, after creating all the routes, the application shows the users the parameters of every route and charging station. Sixth, the application has the ability to let the users decide the priority to select the charging station. Seventh, the application let the users decide the battery percentage that they want their vehicles to have after charging them. This application was created using mostly Javascript language, Expressjs as the framework and for the user interface jQuery. Moreover, MongoDB and PosgreSQL were used as databases. Furthermore, some web services like Amazon Web Services were used for server hosting, OpenStreetMap for obtaining GeoSpatial data, Open Charging Map for obtaining charging stations coordinates and data and Fuel Economy for obtaining vehicles data were used to complement the application. For the route formulation, Dijkstra's algorithm and pgRouting was used. Results indicated that the application can successfully recommend routes and charging stations to the users with a reduction of 90\% of time needed against the less time consuming cheapest option when time is the priority and a reduction of 27 times the money needed for the fastest option when price is the priority. Meaning that the navigation system can successfully reduce the time or costs to adjust to the users necessities

TMB-Hunt: a web server to screen sequence sets for transmembrane β-barrel proteins

TMB-Hunt is a program that uses a modified k-nearest neighbour (k-NN) algorithm to classify protein sequences as transmembrane β-barrel (TMB) or non-TMB on the basis of whole sequence amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, a discrimination accuracy of 92.5% was achieved, as tested using a rigorous cross-validation procedure. The TMB-Hunt web server, available at , allows screening of up to 10 000 sequences in a single query and provides results and key statistics in a simple colour coded format

Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling

Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI). These models generally perform cross-attention over input pairs, leading to prohibitive computational costs. Recent studies propose dual-encoder and late interaction architectures for faster computation. However, the balance between the expressive of cross-attention and computation speedup still needs better coordinated. To this end, this paper introduces a novel paradigm MixEncoder for efficient sentence pair modeling. MixEncoder involves a light-weight cross-attention mechanism. It conducts query encoding only once while modeling the query-candidate interaction in parallel. Extensive experiments conducted on four tasks demonstrate that our MixEncoder can speed up sentence pairing by over 113x while achieving comparable performance as the more expensive cross-attention models.Comment: Accepted to EMNLP 202