arXiv subjects

Hao Zhou

Publications and source records attributed to Hao Zhou.

At least 469 records · Page 26Linked to original sources

arXiv2019

COOK: Chirp-OOK Communication with Self-reliant Bitrate Adaptation in Backscatter Networks

For large-scale Internet of Things (IoT), backscatter communication is a promising technology to reduce power consumption and simplify deployment. However, backscatter communication lacks stability, along with limited communication range within a few meters. Due to the limited computation ability of backscatter tags, it is burdensome to effectively adapt the bitrate for the time-varying channel. Thus, backscatter tags are failed to fully utilize the optimal transmission rate. In this paper, we design a system named COOK with self-reliant bitrate adaptation in backscatter communication. Channel symmetry allows backscatter tags to adjust bitrate depending on the received signal strength of the excitation source (ES) without feedback. In addition, the chirp spreading signal is exploited as the ES signal to enable backscatter tags to work under noise floor. Our modulation approach is denoted as Chirp-OOK since the tags reflect the chirp signal by employing the on-off keying modulation. It allows that receiver can decode under the noise floor and the bitrate varies flexibly as the communication range changes. We have implemented the prototype system based on the universal software radio peripheral (USRP) platform. Extensive experiment results demonstrate the effectiveness of the proposed system. Our system provides valid communication distance up to $27m$, which is 7 times as compared with normal backscatter system. The system significantly increases the backscatter communication stability, by supporting bitrate adaptation ranges from 0.33kbps to 1.2Mbps, and guaranteeing the bit error rate (BER) is below 1%.

eess.SP↗

arXiv2019

Non-autoregressive Transformer by Position Learning

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative process. Experimental results show that PNAT achieves top results on machine translation and paraphrase generation tasks, outperforming several strong baselines.

cs.CL↗

arXiv2019

Importance-Aware Learning for Neural Headline Editing

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such a neural headline editing model, we collected a dataset which contains articles with original headlines and professionally edited headlines. However, it is expensive to collect a large number of professionally edited headlines. To solve this low-resource problem, we design an encoder-decoder model which leverages large scale pre-trained language models. We further improve the pre-trained model's quality by introducing a headline generation task as an intermediate task before the headline editing task. Also, we propose Self Importance-Aware (SIA) loss to address the different levels of editing in the dataset by down-weighting the importance of easily classified tokens and sentences. With the help of Pre-training, Adaptation, and SIA, the model learns to generate headlines in the professional editor's style. Experimental results show that our method significantly improves the quality of headline editing comparing against previous methods.

cs.CL↗

arXiv2019

Visual Relationship Detection with Relative Location Mining

Visual relationship detection, as a challenging task used to find and distinguish the interactions between object pairs in one image, has received much attention recently. In this work, we propose a novel visual relationship detection framework by deeply mining and utilizing relative location of object-pair in every stage of the procedure. In both the stages, relative location information of each object-pair is abstracted and encoded as auxiliary feature to improve the distinguishing capability of object-pairs proposing and predicate recognition, respectively; Moreover, one Gated Graph Neural Network(GGNN) is introduced to mine and measure the relevance of predicates using relative location. With the location-based GGNN, those non-exclusive predicates with similar spatial position can be clustered firstly and then be smoothed with close classification scores, thus the accuracy of top $n$ recall can be increased further. Experiments on two widely used datasets VRD and VG show that, with the deeply mining and exploiting of relative location information, our proposed model significantly outperforms the current state-of-the-art.

cs.CV↗

arXiv2019

Kernelized Bayesian Softmax for Text Generation

Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each token. However, a word may have multiple senses according to different context, some of which might be distinct. In this paper, we propose KerBS, a novel approach for learning better embeddings for text generation. KerBS embodies two advantages: (a) it employs a Bayesian composition of embeddings for words with multiple senses; (b) it is adaptive to semantic variances of words and robust to rare sentence context by imposing learned kernels to capture the closeness of words (senses) in the embedding space. Empirical studies show that KerBS significantly boosts the performance of several text generation tasks.

cs.CL↗

arXiv2019

New methodology based on energy flux similarity for large-eddy simulation of transitional and turbulent flows

A new methodology based on energy flux similarity is suggested in this paper for large eddy simulation (LES) of transitional and turbulent flows. Existing knowledge reveals that the energy cascade generally exists in transitional and turbulent flows with different distributions, and the characteristic quantity of scale interaction in energy cascade processes is energy flux. Therefore, energy flux similarity is selected as the basic criterion to secure the flow field getting from LES highly similar to the real flow field. Through a priori tests, we find that the energy flux from the tensor-diffusivity (TD) model has high similarity with the real energy flux. Then, we modify the modelled energy flux from the TD model and obtain uniform formulas of energy flux similarity corresponding to different filter widths and locations in the wall-bounded turbulence. To secure the robustness of simulation and the LES results similar to the real flow, we apply the energy flux similarity method (EFSM) to the Smagorinsky model in the LES of compressible turbulent channel flow, compressible flat-plate flow, and flow over a compressible ramp. The a posteriori tests show that, overall, EFSM can better predict these flows than other subgrid-scale models. In the simulation of turbulent channel flow, EFSM can accurately predict the mean stream-wise velocity, Reynolds stress, and affluent coherent structures. In LES of compressible flat-plate flow, EFSM could provide accurate simulation results of the onset of transition and transition peak, skin friction, and mean stream-wise velocity in cases with three different grid scales. Meanwhile, for flow over a compressible ramp, EFSM could correctly describe the process of bypass transition, locations of separation and reattachment in the corner region, and abundant coherent vortex structures, etc.

physics.flu-dyn↗

arXiv2019

Beam Acquisition and Training in Millimeter Wave Networks with Narrowband Pilots

This paper studies initial beam acquisition in a millimeter wave network consisting of multiple access points (APs) and mobile devices. A training protocol for joint estimation of transmit and receive beams is presented with a general frame structure consisting of an initial access sub-frame followed by data transmission sub-frames. During the initial subframe, APs and mobiles sweep through a set of beams and determine the best transmit and receive beams via a handshake. All pilot signals are narrowband (tones), and the mobiles are distinguished by their assigned pilot frequencies. Both non-coherent and coherent beam estimation methods based on, respectively, power detection and maximum likelihood (ML) are presented. To avoid exchanging information about beamforming vectors between APs and mobiles, a local maximum likelihood (LML) algorithm is also presented. An efficient fast Fourier transform implementation is proposed for ML and LML to achieve high-resolution. A system-level optimization is performed in which the frame length, training time, and training bandwidth are selected to maximize a rate objective taking into account blockage and mobility. Simulation results based on a realistic network topology are presented to compare the performance of different estimation methods and training codebooks, and demonstrate the effectiveness of the proposed protocol.

eess.SP↗

arXiv2019

Rethinking Text Attribute Transfer: A Lexical Analysis

Text attribute transfer is modifying certain linguistic attributes (e.g. sentiment, style, authorship, etc.) of a sentence and transforming them from one type to another. In this paper, we aim to analyze and interpret what is changed during the transfer process. We start from the observation that in many existing models and datasets, certain words within a sentence play important roles in determining the sentence attribute class. These words are referred to as \textit{the Pivot Words}. Based on these pivot words, we propose a lexical analysis framework, \textit{the Pivot Analysis}, to quantitatively analyze the effects of these words in text attribute classification and transfer. We apply this framework to existing datasets and models and show that: (1) the pivot words are strong features for the classification of sentence attributes; (2) to change the attribute of a sentence, many datasets only requires to change certain pivot words; (3) consequently, many transfer models only perform the lexical-level modification, while leaving higher-level sentence structures unchanged. Our work provides an in-depth understanding of linguistic attribute transfer and further identifies the future requirements and challenges of this task\footnote{Our code can be found at https://github.com/FranxYao/pivot_analysis}.

cs.CL↗

arXiv2019

Unsupervised Paraphrasing by Simulated Annealing

Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model paraphrase generation as an optimization problem and propose a sophisticated objective function, involving semantic similarity, expression diversity, and language fluency of paraphrases. Then, UPSA searches the sentence space towards this objective by performing a sequence of local editing. Our method is unsupervised and does not require parallel corpora for training, so it could be easily applied to different domains. We evaluate our approach on a variety of benchmark datasets, namely, Quora, Wikianswers, MSCOCO, and Twitter. Extensive results show that UPSA achieves the state-of-the-art performance compared with previous unsupervised methods in terms of both automatic and human evaluations. Further, our approach outperforms most existing domain-adapted supervised models, showing the generalizability of UPSA.

cs.CL↗

arXiv2019

Discovery of the TeV Emission from the JetInteraction Regions of SS 433 with HAWC

The High Altitude Water Cherenkov (HAWC) observatory recently published the discovery of SS 433 as a TeV source, reporting the observation of multi-TeV gamma-ray emission from the jet interaction regions e1 and w1, suggesting in-situ particle acceleration. This showed the first direct evidence of acceleration in jets at energies greater than a few TeV. SS 433 was the first microquasar to be discovered and is still considered special in that the accretion is supercritical and the luminosity of the system is very high ($\sim10^{40}$ erg s$^{-2}$). The lobes of the supernova remnant W 50 in which the jets terminate, about 40 parsecs from the central binary, are expected to accelerate charged particles, and indeed radio and X-ray emission consistent with electron synchrotron emission in a magnetic field have been observed. SS 433 has also been a strong candidate for hadronic acceleration due to spectroscopic evidence of ionized nuclei in the inner jets. However, multiwavelength fits including the HAWC measurements favor the leptonic production of the observed gamma rays. Here, we present new follow-up measurements of the jet interaction regions of SS 433 using the most recent data from HAWC.

astro-ph.HE↗

arXiv2019

Correct-and-Memorize: Learning to Translate from Interactive Revisions

State-of-the-art machine translation models are still not on par with human translators. Previous work takes human interactions into the neural machine translation process to obtain improved results in target languages. However, not all model-translation errors are equal -- some are critical while others are minor. In the meanwhile, the same translation mistakes occur repeatedly in a similar context. To solve both issues, we propose CAMIT, a novel method for translating in an interactive environment. Our proposed method works with critical revision instructions, therefore allows human to correct arbitrary words in model-translated sentences. In addition, CAMIT learns from and softly memorizes revision actions based on the context, alleviating the issue of repeating mistakes. Experiments in both ideal and real interactive translation settings demonstrate that our proposed \method enhances machine translation results significantly while requires fewer revision instructions from human compared to previous methods.

cs.CL↗

arXiv2019

Large-scale traffic signal control using machine learning: some traffic flow considerations

This paper uses supervised learning, random search and deep reinforcement learning (DRL) methods to control large signalized intersection networks. The traffic model is Cellular Automaton rule 184, which has been shown to be a parameter-free representation of traffic flow, and is the most efficient implementation of the Kinematic Wave model with triangular fundamental diagram. We are interested in the steady-state performance of the system, both spatially and temporally: we consider a homogeneous grid network inscribed on a torus, which makes the network boundary-free, and drivers choose random routes. As a benchmark we use the longest-queue-first (LQF) greedy algorithm. We find that: (i) a policy trained with supervised learning with only two examples outperforms LQF, (ii) random search is able to generate near-optimal policies, (iii) the prevailing average network occupancy during training is the major determinant of the effectiveness of DRL policies. When trained under free-flow conditions one obtains DRL policies that are optimal for all traffic conditions, but this performance deteriorates as the occupancy during training increases. For occupancies > 75% during training, DRL policies perform very poorly for all traffic conditions, which means that DRL methods cannot learn under highly congested conditions. We conjecture that DRL's inability to learn under congestion might be explained by a property of urban networks found here, whereby even a very bad policy produces an intersection throughput higher than downstream capacity. This means that the actual throughput tends to be independent of the policy. Our findings imply that it is advisable for current DRL methods in the literature to discard any congested data when training, and that doing this will improve their performance under all traffic conditions.

cs.AI↗

arXiv2019

Imitation Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted in parallel, without directly considering previous context. In this paper, we propose an imitation learning framework for non-autoregressive machine translation, which still enjoys the fast translation speed but gives comparable translation performance compared to its auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and WMT16 datasets. Our proposed model achieves a significant speedup over the autoregressive models, while keeping the translation quality comparable to the autoregressive models. By sampling sentence length in parallel at inference time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and 30.68 BLEU on IWSLT16 En$\rightarrow$De.

cs.CL↗

arXiv2019

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of AutoCross. It is shown that AutoCross can significantly enhance the performance of both linear and deep models.

cs.LG↗

arXiv2019

Generating Sentences from Disentangled Syntactic and Semantic Spaces

Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE's latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax-transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.

cs.CL↗

arXiv2019

Dynamically Fused Graph Network for Multi-hop Reasoning

Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from scattered text among two or more documents. In this paper, we propose Dynamically Fused Graph Network(DFGN), a novel method to answer those questions requiring multiple scattered evidence and reasoning over them. Inspired by human's step-by-step reasoning behavior, DFGN includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores along the entity graph dynamically built from the text, and gradually finds relevant supporting entities from the given documents. We evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning. DFGN achieves competitive results on the public board. Furthermore, our analysis shows DFGN produces interpretable reasoning chains.

cs.CL↗

arXiv2019

On self-conjugate $(s, s+1,\ldots, s+k)$-core partitions

Simultaneous core partitions have been widely studied since Anderson's work on the enumeration of $(s,t)$-core partitions. Amdeberhan and Leven showed that the number of $(s,s+1, \ldots, s+k)$-core partitions is equal to the number of $(s, k)$-Dyck paths. In this paper, we prove that self-conjugate $(s,s+1, \ldots, s+k)$-core partitions are equinumerous with symmetric $(s, k)$-Dyck paths, confirming a conjecture posed by Cho, Huh and Sohn.

math.CO↗

arXiv2019

Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation

The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback--Leibler (KL) divergence between the posterior and prior. In NLP, VAEs are extremely difficult to train due to the problem of KL collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a VAE for text. In this paper, we propose to use the Wasserstein autoencoder (WAE) for probabilistic sentence generation, where the encoder could be either stochastic or deterministic. We show theoretically and empirically that, in the original WAE, the stochastically encoded Gaussian distribution tends to become a Dirac-delta function, and we propose a variant of WAE that encourages the stochasticity of the encoder. Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.

cs.CL↗