SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension

We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70% on the development set and about 60% on the test set.Comment: SemEval 201

Learning to Compose Task-Specific Tree Structures

For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures only from plain text data efficiently. Our model uses Straight-Through Gumbel-Softmax estimator to decide the parent node among candidates dynamically and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language inference and sentiment analysis, and show that our model outperforms or is at least comparable to previous models. We also find that our model converges significantly faster than other models.Comment: AAAI 201

Weighted inhomogeneous regularization for inverse problems with indirect and incomplete measurement data

Regularization promotes well-posedness in solving an inverse problem with incomplete measurement data. The regularization term is typically designed based on a priori characterization of the unknown signal, such as sparsity or smoothness. The standard inhomogeneous regularization incorporates a spatially changing exponent $p$ of the standard $\ell_p$ norm-based regularization to recover a signal whose characteristic varies spatially. This study proposes a weighted inhomogeneous regularization that extends the standard inhomogeneous regularization through new exponent design and weighting using spatially varying weights. The new exponent design avoids misclassification when different characteristics stay close to each other. The weights handle another issue when the region of one characteristic is too small to be recovered effectively by the $\ell_p$ norm-based regularization even after identified correctly. A suite of numerical tests shows the efficacy of the proposed weighted inhomogeneous regularization, including synthetic image experiments and real sea ice recovery from its incomplete wave measurements

Real-Time Digital Video Streaming at Low-VHF for Compact Autonomous Agents in Complex Scenes

This paper presents an experimental investigation of real-time digital video streaming in physically complex Non-Line-Of-Sight (NLoS) channels using a low-power, low-VHF system integrated on a compact robotic platform. Reliable video streaming in NLoS channels over infrastructure-poor ad-hoc radio networks is challenging due to multipath and shadow fading. In this effort, we focus on exploiting the near-ground low-VHF channel which has been shown to have improved penetration, reduced fading, and lower power requirements (which is critical for autonomous agents with limited power) compared to higher frequencies. Specifically, we develop a compact, low-power, low-VHF radio test-bed enabled by recent advances in efficient miniature antennas and off-the-shelf software-defined radios. Our main goal is to carry out an empirical study in realistic environments of how the improved propagation conditions at low-VHF affect the reliability of video-streaming with constraints stemming from the limited available bandwidth with electrically small low-VHF antennas. We show quantitative performance analysis of video streaming from a robotic platform navigating inside a large occupied building received by a node located outdoors: bit error rate (BER) and channel-induced Peak Signal-to-Noise Ratio (PSNR) degradation. The results show channel-effect-free-like video streaming with the low-VHF system in complex NLoS channels.Comment: Accepted for publication in 2019 IEEE 89th Vehicular Technology Conferenc

Miniaturized Antenna and Wave Propagation Studies Enabling Compact Low-Power Mobile Radio Networks at Low VHF

Reliable tactical mobile networking in cluttered infrastructure-poor environments at conventional microwave frequencies is a very challenging task, which requires innovative and unconventional networking capabilities, due to very high signal attenuation and small-scale fading. At lower frequencies, such effects are significantly reduced, which makes these frequencies more appropriate for robust moderate-rate communication over longer ranges with low transmit power. However, the prohibitively large size of conventional antennas and lack of efficient small antennas have been a major bottleneck in realizing compact systems for applications such as autonomous networking among small robotic platforms. To enable compact, low-power, low frequency wireless mobile systems, empirical studies are first conducted to investigate the propagation characteristics of the low frequency channel among near-ground nodes. From rigorous studies via physics-based simulation and extensive measurements in complex environments such as non-line-of-sight (NLOS) indoor and outdoor settings, the lower-VHF band (30 MHz – 60 MHz) is chosen due to its favorable propagation properties (high signal penetration through multiple layers of walls and very low signal and phase distortion and delay spread) compared to higher frequency bands (e.g., upper VHF and UHF bands). The second key aspect of this thesis is the design of miniaturized antennas that enable the realization of compact low-VHF communication systems for mobile networking applications. Also, methods for its bandwidth enhancement and performance characterization are examined. A highly miniaturized (0.013λ in lateral dimension and 0.02λ in height at 40 MHz) and lightweight (98 grams) antenna is designed. The antenna provides an impedance bandwidth of 0.35 % and a vertically polarized omnidirectional pattern with the maximum gain of -13 dBi, which is more than 10 dB higher than state-of-the-art antennas with comparable size. In order to further enhance its bandwidth, a new design approach for a non-Foster matching technique utilizing a negative impedance converter is presented. This approach enhances 3 dB power bandwidth with a power efficiency advantage more than twofold compared to that of the passive one. Furthermore, a very effective characterization method for low frequency antennas is developed. This method comprises two procedures: 1) non-intrusive very-near-field measurements using an electro-optical system dispensing with costly large anechoic chambers, and 2) near-field to far-field transformation to compute a far-field radiation based on the reciprocity theorem and full-wave numerical simulations. In the third part of this thesis, a compact, low-power, low-VHF radio employing off-the-shelf ZigBee technology and an optimally designed bi-directional frequency converter (UHF ↔ low VHF) is introduced, in conjunction with the antenna described above, to investigate performance of such systems. The experimental studies show a highly reliable mobile ad-hoc network with a radio coverage of more than 280 m at low power (< 10 mW) in complex propagation scenarios. This work also facilitates multi-node mobile networking at low VHF applied to networking of autonomous vehicles carrying out collaborative tasks such as autonomous exploration and mapping.PHDElectrical & Computer Eng PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137070/1/jihchoi_1.pd