ARXIV 1 Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction Roberto Souza, PhD, Mariana Bento, PhD, Nikita Nogovitsyn, MSc, MD, Kevin J. Chung, BSc, R. Marc Lebel, PhD, and Richard Frayne, PhD Abstract --The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and d) W-net KI were evaluated. Selected promising four element networks (WW-nets) were also examined. Two configurations of each network were compared: 1) Each coil channel processed independently, and 2) all channels processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal to noise ratio, visual information fidelity and visual inspection were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain are better suited when processing individual channels of multi-channel data independently. Dual domain methods are more advantageous when simultaneously reconstructing all channels of multi-channel data. Also, the appropriate cascade of U-nets compared favorably ( p 0 . Index T erms --Magnetic resonance imaging, compressed sensing, multi-channel (coil), image reconstruction, inverse problems, brain, machine learning M AGNETIC RESONANCE (MR) imaging is a sensitive diagnostic modality that allows specific, high-quality investigation of structure and function of the brain and body. One major drawback is the overall acquisition time to complete an MR imaging protocol, which can easily exceed 30 minutes per patient [1]. Lengthy MR examination times are costly ( 300 USD or more per examination); increase susceptibility to patient motion artifacts, which negatively impact image quality; further reduce patient throughput and contribute to repeated studies.

This work introduces two strategies for training network classifiers with heterogeneous agents. One strategy promotes global smoothing over the graph and a second strategy promotes local smoothing over neighbourhoods. It is assumed that the feature sizes can vary from one agent to another, with some agents observing insufficient attributes to be able to make reliable decisions on their own. As a result, cooperation with neighbours is necessary. However, due to the fact that the feature dimensions are different across the agents, their classifier dimensions will also be different. This means that cooperation cannot rely on combining the classifier parameters. We instead propose smoothing the outputs of the classifiers, which are the predicted labels. By doing so, the dynamics that describes the evolution of the network classifier becomes more challenging than usual because the classifier parameters end up appearing as part of the regularization term as well. We illustrate performance by means of computer simulations.

RS Energy Group, Inc. (RSEG) is an advanced analytics and technology firm that delivers industry-leading, comprehensive insights to those operating, investing in or servicing the energy space. RSEG's work environment is positive, supportive, innovative, and dynamic, with interdisciplinary teams focused on leveraging the latest in technology, machine learning, data science and AI. Headquartered in Calgary, RSEG also has offices in Houston, New York and Conshohocken.

Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itself. To go a step further in this journey of removing human-computer language barrier, this paper proposes machine learning approach using Recurrent Neural Network(RNN) and Long-Short Term Memory(LSTM) to convert human language into programming language code. The programmer will write expressions for codes in layman's language, and the machine learning model will translate it to the targeted programming language. The proposed approach yields result with 74.40% accuracy. This can be further improved by incorporating additional techniques, which are also discussed in this paper. Keywords: Text to code, machine learning, machine translation, NLP, RNN, LSTM 1 Introduction Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, arXiv:1910.11471v1

ABSTRACT Google's multilingual speech recognition system combines low-level acoustic signals with language-specific recognizer signals to better predict the language of an utterance. This paper presents our experience with different signal combination methods to improve overall language identification accuracy. We compare the performance of a lattice-based ensemble model and a deep neural network model to combine signals from recognizers with that of a baseline that only uses low-level acoustic signals. Experimental results show that the deep neural network model outperforms the lattice-based ensemble model, and it reduced the error rate from 5 .5% in the baseline to 4 .3%, Index T erms-- Signal combination, language identification, lattice regression, deep neural network 1. INTRODUCTION Multilingual speech recognition is an important feature for modern speech recognition systems allowing users to speak in more than a single, preset language.

A graph-based sampling and consensus (GraphSAC) approach is introduced to effectively detect anomalous nodes in large-scale graphs. Existing approaches rely on connectivity and attributes of all nodes to assign an anomaly score per node. However, nodal attributes and network links might be compromised by adversaries, rendering these holistic approaches vulnerable. Alleviating this limitation, GraphSAC randomly draws subsets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node. These learned nominal distributions are minimally affected by the anomalous nodes, and hence can be directly adopted for anomaly detection. Rigorous analysis provides performance guarantees for GraphSAC, by bounding the required number of draws. The per-draw complexity grows linearly with the number of edges, which implies efficient SSL, while draws can be run in parallel, thereby ensuring scalability to large graphs. GraphSAC is tested under different anomaly generation models based on random walks, clustered anomalies, as well as contemporary adversarial attacks for graph data. Experiments with real-world graphs showcase the advantage of GraphSAC relative to state-of-the-art alternatives.

Calgary-based Chata Technologies, which has developed a cloud-based conversational application allowing users to access, search, and analyze their business data through natural language, has raised a $4.5 million CAD seed round. "It's been very exciting to see the vast potential and profound implications of Conversational AI-based data interactions." The funding was raised from undisclosed local investors, with the round officially closing in August. The new capital will be put towards the research, development, and implementation of Chata Technologies' new product, Data Messenger, currently under the name Chata.io. Chata Technologies claims that the product is the first conversational interface designed specifically for data query and analysis.

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Automatic text recognition from ancient handwritten record images is an important problem in the genealogy domain. However, critical challenges such as varying noise conditions, vanishing texts, and variations in handwriting make the recognition task difficult. We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form. The proposed model consists of three-components including a generator, and word-level and character-level discriminators. The model incorporates Sliced Wasserstein distance (SWD) and U-Net architectures in HW2MP-GAN for better quality image-to-image transformation. Our experiments reveal that HW2MP-GAN outperforms state-of-the-art baseline cGAN models by almost 30 in Frechet Handwritten Distance (FHD), 0.6 on average Levenshtein distance and 39% in word accuracy for image-to-image translation on IAM database. Further, HW2MP-GAN improves handwritten recognition word accuracy by 1.3% compared to baseline handwritten recognition models on the IAM database.

In the first decades of the 20th century, people around the world began succumbing to an entirely new cause of mortality. These new deaths, due to the dangers of the automobile, soon became accepted as a lamentable but normal part of modern life. A hundred years later, with 1.25 million people worldwide (about 30,000 in the U.S.) being killed every year in road crashes, there's now an effort to reject the perception that these deaths are normal or acceptable. As reported in the New York Times, a growing number of safety advocates, government officials, and journalists are moving away from the phrase "car accident" on the grounds that it presumes that the drivers involved are blameless--a presumption that is correct only 6 percent of the time, according to a report by the U.S. Department of Transportation. The vast majority of such incidents are caused by drivers who make mistakes, take risks, or drive while distracted or impaired.