Present but not The type of sound described is present, but the audio clip also predominant (PNP) contains other salient types of sound and/or strong background noise. Not Present (NP) The type of sound described is not present in the audio clip. Unsure (U) I am not sure whether the type of sound described is present or not. Table 2: Categories composing FSDKaggle2018, along with the number of samples and time (in minutes, rounded) in the train set. Percategory AP@3 achieved by the baseline system is reported using all the test files for every category (i.e., not following the public/private splits of the Kaggle leaderboard).

The conflict between increasing demand for computing and sluggish grow of hardware capability triggers the heated development of approximate computing, which has achieved massive success in both industry and research community. Many applications that do not require utterly accurate computation can achieve tremendous acceleration and drastic reduction of the energy consumption by leveraging approximate computing, especially in domains that call for real-time calculation, fast response and low power consumption such as learning [27], image processing [19] and scientific computation [24]. Approximation computing can be conduct in different hierarchies, such as hardware [6], [18], system and software levels. Various approximate computing architectures [17], [19], [27] are advocated. Neural network (NN) based approximate computing focus on the acceleration in software-level and has many advantages when compared to previous methods. First, neural networks are proved to be able to fit any continuous function [12], and thus this method can universally be adopted by different tasks. Second, enormous parallelism in the neural networks is exploited by the rapid advancement of various neural network accelerators.

In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.

We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities. By comparing, combining and extending different methodologies for link prediction on graph-based data coming from different domains, we formalize a unified methodology for the quality evaluation benchmark of neural embeddings for knowledge graphs. This benchmark is then used to empirically investigate the potential of training neural embeddings globally for the entire graph, as opposed to the usual way of training embeddings locally for a specific relation. This new way of testing the quality of the embeddings evaluates the performance of binary classifiers for scalable link prediction with limited data. Our evaluation pipeline is made open source, and with this we aim to draw more attention of the community towards an important issue of transparency and reproducibility of the neural embeddings evaluations.

Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization. The key step in SNNs is to properly scale the exponential linear unit (referred to as SELU) to inherently incorporate normalization based on central limit theory. SELU is a monotonically increasing function, where it has an approximately constant negative output for large negative input. In this work, we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property. Differently from SELU, the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function, which is referred to as a scaled exponentially-regularized linear unit (SERLU). The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically. To effectively combat over-fitting, we develop a so-called shift-dropout for SERLU, which includes standard dropout as a special case. Experimental results on MNIST, CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU, SELU, Swish, Leakly ReLU and ReLU.

In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.

The University of Hull's Mixed Reality accelerator was recently launched with the remit of promoting collaboration between industry and academia to develop commercial applications for Microsoft HoloLens. It is led by VISR a company founded in 2015 by veteran Xbox games developer Louis Deane and his business partner Lindsay West. They were one of the earliest Microsoft Mixed Reality partners in Europe. John Hemingway, Director of ICT at the University of Hull explains that hosting the Mixed Reality Accelerator was a natural progression for the University, as it taps into the institution's history of computer games development, virtual reality and 3D visualization developed over the past 30 years. As a University, it's important for us to not only lead from the front when it comes to cutting-edge technologies, but also to look at how those technologies allow us to create ever more skilled and work-ready graduates.

One day in March 2010, Isak McCune started clearing his throat with a forceful, violent sound. The New Hampshire toddler was 3, with a Beatles mop of blonde hair and a cuddly, loving personality. His parents had no idea where the guttural tic came from. They figured it was springtime allergies. Soon after, Isak began to scream as if in pain and grunt at his parents and peers. When he wasn't throwing hours-long tantrums, he stared vacantly into space. By the time he was 5, he was plagued by insistent, terrifying thoughts of death. "He would smash his head into windows and glass whenever the word'dead' came into his head. He was trying to drown out the thoughts," says his mother, Robin McCune, a baker in Goffstown, a small town outside Manchester, New Hampshire's largest city.

HOUSTON – High-tech devices in agriculture such as unmanned aerial vehicles and sensors are leading to immense growth in data collection and deployment, and a Houston conference Aug. 20-21 will feature scholars and industry experts discussing future applications in all aspects of production agriculture. The invitation-only conference, Identifying Obstacles to Applying Big Data in Agriculture, will be held at the Houston Airport Marriott at George Bush Intercontinental Airport. It is sponsored by Texas A&M AgriLife Research and the U.S. Department of Agriculture- National Institute of Food and Agriculture. "We have had advanced technologies like GPS in agriculture for over 20 years, but only a small handful of these technologies have made a significant impact," said Dr. Alex Thomasson, conference coordinator and Texas A&M AgriLife Research engineer in College Station. "Thus we want to cast a vision for the practical use of big data in production agriculture so we can take advantage of the current wave of attendant technologies like the so-called Internet of Things, artificial intelligence, wireless communications, the cloud, etc. "This conference will feature discussion with key business leaders and academics involved in a broad range of disciplines within big data and precision agriculture.

Do you want to find out which are the best frameworks or libraries for natural language processing (NLP) in Python? Do you want to mine the social web and summarise blog posts? There are a lot of NLP libraries on the internet, but finding the right fit for your project is difficult. Natural Language Toolkit is one of the most popular platforms for building Python programs. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenisation, stemming, tagging, parsing, and semantic reasoning.