COnditional COordinate GAN (COCO-GAN)
Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose \underline{CO}nditional \underline{CO}ordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never generated during training, we show that COCO-GAN can produce \textbf{state-of-the-art-quality} full images during inference. We further demonstrate a variety of novel applications enabled by teaching the network to be aware of coordinates. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called ‘beyond-boundary generation’. We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand. …

Argument unit Recognition and Classification (ARC)
Argument mining is generally performed on the sentence-level — it is assumed that an entire sentence (not parts of it) corresponds to an argument. In this paper, we introduce the new task of Argument unit Recognition and Classification (ARC). In ARC, an argument is generally a part of a sentence — a more realistic assumption since several different arguments can occur in one sentence and longer sentences often contain a mix of argumentative and non-argumentative parts. Recognizing and classifying the spans that correspond to arguments makes ARC harder than previously defined argument mining tasks. We release ARC-8, a new benchmark for evaluating the ARC task. We show that token-level annotations for argument units can be gathered using scalable methods. ARC-8 contains 25\% more arguments than a dataset annotated on the sentence-level would. We cast ARC as a sequence labeling task, develop a number of methods for ARC sequence tagging and establish the state of the art for ARC-8. A focus of our work is robustness: both robustness against errors in sentence identification (which are frequent for noisy text) and robustness against divergence in training and test data. …

SwitchAgg
Many distributed applications adopt a partition/aggregation pattern to achieve high performance and scalability. The aggregation process, which usually takes a large portion of the overall execution time, incurs large amount of network traffic and bottlenecks the system performance. To reduce network traffic,some researches take advantage of network devices to commit innetwork aggregation. However, these approaches use either special topology or middle-boxes, which cannot be easily deployed in current datacenters. The emerging programmable RMT switch brings us new opportunities to implement in-network computation task. However, we argue that the architecture of RMT switch is not suitable for in-network aggregation since it is designed primarily for implementing traditional network functions. In this paper, we first give a detailed analysis of in-network aggregation, and point out the key factor that affects the data reduction ratio. We then propose SwitchAgg, which is an innetwork aggregation system that is compatible with current datacenter infrastructures. We also evaluate the performance improvement we have gained from SwitchAgg. Our results show that, SwitchAgg can process data aggregation tasks at line rate and gives a high data reduction rate, which helps us to cut down network traffic and alleviate pressure on server CPU. In the system performance test, the job-completion-time can be reduced as much as 50% …

NAIL
Interactive Fiction (IF) games are complex textual decision making problems. This paper introduces NAIL, an autonomous agent for general parser-based IF games. NAIL won the 2018 Text Adventure AI Competition, where it was evaluated on twenty unseen games. This paper describes the architecture, development, and insights underpinning NAIL’s performance. …