Convolutional 2D Knowledge Graph Embeddings

Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models -- which potentially limits performance. In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree -- which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set -- however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets -- deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets.Comment: Extended AAAI2018 pape

High Performance Computing Algorithms for Accelerating Peptide Identification from Mass-Spectrometry Data Using Heterogeneous Supercomputers

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances due to their inefficient parallelization models, low resource utilization and high overhead costs

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover

Objects that Sound

In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that can localize the object that sounds in an image, given the audio signal. We achieve both these objectives by training from unlabelled video using only audio-visual correspondence (AVC) as the objective function. This is a form of cross-modal self-supervision from video. To this end, we design new network architectures that can be trained for cross-modal retrieval and localizing the sound source in an image, by using the AVC task. We make the following contributions: (i) show that audio and visual embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and between-mode retrieval; (ii) explore various architectures for the AVC task, including those for the visual stream that ingest a single image, or multiple images, or a single image and multi-frame optical flow; (iii) show that the semantic object that sounds within an image can be localized (using only the sound, no motion or flow information); and (iv) give a cautionary tale on how to avoid undesirable shortcuts in the data preparation.Comment: Appears in: European Conference on Computer Vision (ECCV) 201

Real-Time Riverine Particle Image Velocimetry

A modular particle image velocimetry program was developed and optimized to read and process video of river surface flows from different sensor types. The program was designed for long-term deployment with the ability to sample data continuously in realtime and save the results in a compact format. The time needed to compute a velocity measurement from video input was reduced by using concurrent processing techniques, multi-threading, and a graphics hardware-based correlation algorithm. When used to process field data on a low power Intel Atom based computer the PIV system was capable of computing up to 64 velocity measurements at a rate of 7.5 frames per second. A more powerful computer equipped with a discrete GPU was capable of computing 4800 velocity measurements at a rate of 7.5 frames per second when using the same PIV data and settings. Processing speed of the GPU correlation module was analyzed using a number of different benchmarks. Results show that the GPU-based correlation algorithm has the potential to improve the PIV processing speed of high-end workstations by as much as 2x and low-end portable computers by 10-20x. Methods were also introduced to improve the quality of PIV measurements on river currents. Processing video of river currents with the standard particle image velocimetry technique produced a large number of inaccurate vectors. Most of these inaccurate vectors were correctly identified and removed by using different confidence scoring and filtering techniques. Results from three different experiments were compared to the velocity measurements of other devices to verify the accuracy of the program. These measurements agree to within 16% difference. These results show that accurate PIV measurements of river surface velocity may be computed in real time even on low end and portable computer hardware

Accelerating the pace of protein functional annotation with intel xeon phi coprocessors

© 2002-2011 IEEE. Intel Xeon Phi is a new addition to the family of powerful parallel accelerators. The range of its potential applications in computationally driven research is broad; however, at present, the repository of scientific codes is still relatively limited. In this study, we describe the development and benchmarking of a parallel version of {\mmb e}FindSite, a structural bioinformatics algorithm for the prediction of ligand-binding sites in proteins. Implemented for the Intel Xeon Phi platform, the parallelization of the structure alignment portion of {\mmb e}FindSite using pragma-based OpenMP brings about the desired performance improvements, which scale well with the number of computing cores. Compared to a serial version, the parallel code runs 11.8 and 10.1 times faster on the CPU and the coprocessor, respectively; when both resources are utilized simultaneously, the speedup is 17.6. For example, ligand-binding predictions for 501 benchmarking proteins are completed in 2.1 hours on a single Stampede node equipped with the Intel Xeon Phi card compared to 3.1 hours without the accelerator and 36.8 hours required by a serial version. In addition to the satisfactory parallel performance, porting existing scientific codes to the Intel Xeon Phi architecture is relatively straightforward with a short development time due to the support of common parallel programming models by the coprocessor. The parallel version of {\mmb e}FindSite is freely available to the academic community at www.brylinski.org/efindsite