Evolution from 8QAM live traffic to PCS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

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We report on the evolution of the longest segment of FASTER cable at 11,017 km, with 8QAM transponders at 4b/s/Hz spectral efficiency (SE) in service. With offline testing, 6 b/s/Hz is further demonstrated using probabilistically shaped 64QAM, and a novel, low complexity nonlinearity compensation technique based on generating a black-box model of the transmission by training an artificial neural network, resulting in the largest SE-distance product 66,102 b/s/Hz-km over live-traffic carrying cable.

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ANN-Based Transfer Learning for QoT Prediction in Real-Time Mixed Line-Rate Systems

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Quality of transmission prediction for real-time mixed line-rate systems is realized using artificial neural network based transfer learning with SDN orchestrating. 0.42 dB accuracy is achieved with a 1000 to 20 reduction in training samples.

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41.5 Tb/s Data Transport over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM to Support Metro Network Capacity Demands

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41.5-Tb/s over 549 km of deployed SSMF in Verizon’s network is achieved using probabilistic-shaped 144QAM to optimize throughput at ultra-fine granularity. This is the highest C-band only capacity and spectral efficiency in metro field environment.

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SVBRDF-Invariant Shape and Reflectance Estimation from a Light-Field Camera

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Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals or plastics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup. We show that, although direct shape recovery is not possible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.

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Towards a Timely Causality Analysis for Enterprise Security

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The increasingly sophisticated Advanced Persistent Threat (APT) attacks have become a serious challenge for enterprise IT security. Attack causality analysis, which tracks multi-hop causal relationships between files and processes to diagnose attack provenances and consequences, is the first step towards understanding APT attacks and taking appropriate responses. Since attack causality analysis is a time-critical mission, it is essential to design causality tracking systems that extract useful attack information in a timely manner. However, prior work is limited in serving this need. Existing approaches have largely focused on pruning causal dependencies totally irrelevant to the attack, but fail to differentiate and prioritize abnormal events from numerous relevant, yet benign and complicated system operations, resulting in long investigation time and slow responses.

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Constellation Design with Geometric and Probabilistic Shaping

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A systematic study, including theory, simulation and experiments, is carried out to review the generalized pairwise optimization algorithm for designing optimized constellation. In order to verify its effectiveness, the algorithm is applied in three testing cases: 2-dimensional 8 quadrature amplitude modulation (QAM), 4-dimensional set-partitioning QAM, and probabilistic-shaped (PS) 32QAM. The results suggest that geometric shaping can work together with PS to further bridge the gap toward the Shannon limit.

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Joint Pixel and Feature-level Domain Adaptation in the Wild

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Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature level, inspired by insights from semi-supervised learning in a domain adversarial neural network, we propose a novel regularization in the form of domain adversarial entropy minimization. Next, we posit that insights from computer vision are more amenable to injection at the pixel level and specifically address the key challenge of adaptation across different semantic levels. In particular, we use 3D geometry and image synthetization based on a generalized appearance flow to preserve identity across higher-level pose transformations, while using an attribute-conditioned CycleGAN to translate a single source into multiple target images that differ in lower-level properties such as lighting. We validate on a novel problem of car recognition in unlabeled surveillance images using labeled images from the web, handling explicitly specified, nameable factors of variation through pixel-level and implicit, unspecified factors through feature-level adaptation. Extensive experiments achieve state-of-the-art results, demonstrating the effectiveness of complementing feature and pixel-level information via our proposed domain adaptation method.

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Video Generation From Text

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Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called “gist,” are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs.

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Adaptive Feature Abstraction for Translating Video to Text

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Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent semantics in the video may make it more appropriate to adaptively select features from the multiple CNN layers. We propose a new approach to generating adaptive spatiotemporal representations of videos for the captioning task. A novel attention mechanism is developed, which adaptively and sequentially focuses on different layers of CNN features (levels of feature “abstraction”), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.

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Learning random-walk label propagation for weakly-supervised semantic segmentation

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Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks. We propose a novel training approach to address this difficulty. Given cheaply-obtained sparse image labelings, we propagate the sparse labels to produce guessed dense labelings. A standard CNN-based segmentation network is trained to mimic these labelings. The label-propagation process is defined via random-walk hitting probabilities, which leads to a differentiable parameterization with uncertainty estimates that are incorporated into our loss. We show that by learning the label-propagator jointly with the segmentation predictor, we are able to effectively learn semantic edges given no direct edge supervision. Experiments also show that training a segmentation network in this way outperforms the naive approach.

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