41.5-Tb/s Transmission Over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM

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We demonstrate high spectral efficiency transmission over 549 km of field-deployed single-mode fiber using probabilistic-shaped 144QAM. We achieved 41.5 Tb/s over the C-band at a spectral efficiency of 9.02 b/s/Hz using 32-Gbaud channels at a channel spacing of 33.33 GHz, and 38.1 Tb/s at a spectral efficiency of 8.28 b/s/Hz using 48-Gbaud channels at a channel spacing of 50 GHz. To the best of our knowledge, these are the highest total capacities and spectral efficiencies reported in a metro field environment using C-band only. In high spectral efficiency transmission, it is necessary to optimize back-to-back performance in order to maximize the link loss margin. Our results are enabled by the joint optimization of constellation shaping and coding overhead to minimize the gap to Shannon’s capacity, transmitter- and receiver-side digital backpropagation, signal clipping optimization, and I/Q imbalance compensation.

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Visual Entailment Task for Visually-Grounded Language Learning

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We introduce a new inference task – Visual Entailment (VE) – which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a natural language sentence as in TE tasks. A novel dataset SNLI-VE is proposed for VE tasks based on the Stanford Natural Language Inference corpus and Flickr30K. We introduce a differentiable architecture called the Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and several other state-of-the-art visual question answering (VQA) based models are evaluated on the SNLI-VE dataset, facilitating grounded language understanding and providing insights on how modern VQA based models perform.

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SkyRAN: A Self-Organizing LTE RAN in the Sky

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We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV’s operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV’s position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 – 0.95X of optimal, which is about 1.5 – 2X over other popular baseline schemes.

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Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding

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Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of image data is much smaller than its pixel space dimension and the vulnerability of neural networks grows with the input dimension, we propose to embed high-dimensional input images into a low-dimensional space to perform classification. However, arbitrarily projecting the input images to a low-dimensional space without regularization will not improve the robustness of deep neural networks. Leveraging optimal transport theory, we propose a new framework, Optimal Transport Classifier (OT-Classifier), and derive an objective that minimizes the discrepancy between the distribution of the true label and the distribution of the OT-Classifier output. Experimental results on several benchmark datasets show that, our proposed framework achieves state-of-the-art performance against strong adversarial attack methods.

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Unseen Object Segmentation in Videos via Transferable Representations

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In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: (1) solving a submodular function for selecting object-like segments, and (2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms.

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Scalable Deep k-Subspace Clustering

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Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.

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Leveraging Knowledge Bases for Future Prediction with Memory Comparison Networks

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Making predictions about what might happen in the future is important for reacting adequately in many situations. For example, observing that “Man kidnaps girl” may have the consequence that “Man kills girl”. While this is part of common sense reasoning for humans, it is not obvious how machines can acquire and generalize over such knowledge. In this article, we propose a new type of memory network that can predict the next future event also for observations that are not in the knowledge base. We evaluate our proposed method on two knowledge bases: Reuters KB (events from news articles) and Regneri KB (events from scripts). For both knowledge bases, our proposed method shows similar or better prediction accuracy on unseen events (or scripts) than recently proposed deep neural networks and rankSVM. We also demonstrate that the attention mechanism of our proposed method can be helpful for error analysis and manual expansion of the knowledge base.

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Teaching Syntax by Adversarial Distraction

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Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly.

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Team Papelo: Transformer Networks at FEVER

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We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify a broad set of potential evidence. The precision of the entailment classifier allows us to enhance recall by considering every statement from several articles to decide upon each claim. We include not only the articles best matching the claim text by TFIDF score, but read additional articles whose titles match named entities and capitalized expressions occurring in the claim text. The entailment module evaluates potential evidence one statement at a time, together with the title of the page the evidence came from (providing a hint about possible pronoun antecedents). In preliminary evaluation, the system achieves .5736 FEVER score, .6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.

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Learning Context-Sensitive Convolutional Filters for Text Processing

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Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-sensitive filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.

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