arXiv subjects

Nuo Chen

Publications and source records attributed to Nuo Chen.

At least 163 records · Page 9Linked to original sources

arXiv2023

A selection of Hα emitters at z = 2.1-2.5 using the Ks-band photometry of ZFOURGE

Large and less-biased samples of star-forming galaxies are essential to investigate galaxy evolution. H$\rmα$ emission line is one of the most reliable tracers of star-forming galaxies because its strength is directly related to recent star formation. However, it is observationally expensive to construct large samples of H$\rmα$ emitters by spectroscopic or narrow-band imaging survey at high-redshifts. In this work, we demonstrate a method to extract H$\rmα$ fluxes of galaxies at $z=2.1$-$2.5$ from $K_s$ broad-band photometry of ZFOURGE catalog. Combined with 25-39 other filters, we estimate the emission line fluxes by SED fitting with stellar population models that incorporate emission-line strengths. 2005 galaxies are selected as H$\rmα$ emitters by our method and their fluxes show good agreement with previous measurements in the literature. On the other hand, there are more H$\rmα$ luminous galaxies than previously reported. The discrepancy can be explained by extended H$\rmα$ profiles of massive galaxies and a luminosity dependence of dust attenuation, which are not taken into account in the previous work. We also find that there are a large number of low-mass galaxies with much higher specific star formation rate (sSFR) than expected from the extrapolated star formation main sequence. Such low-mass galaxies exhibit larger ratios between H$\rmα$ and UV fluxes compared to more massive high sSFR galaxies. This result implies that a ``starburst'' mode may differ among galaxies: low-mass galaxies appear to assemble their stellar mass via short-duration bursts while more massive galaxies tend to experience longer-duration ($>10\ \mathrm{Myr}$) bursts.

astro-ph.GA↗

arXiv2022

Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax

Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and then studied the effectiveness of NPIs. In this work, we focus on mobility modeling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a micro perspective prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph. we propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.

cs.LG↗

arXiv2022

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.

cs.SE↗

arXiv2022

Topology-enabled highly efficient beam combination

Beam combination with high efficiency is desirable to overcome the power limit of single electromagnetic sources, enabling long-distance optical communication and high-power laser. The efficiency of coherent beam combination is severely limited by the phase correlation between different input light beams. Here, we theoretically proposed and experimentally demonstrated a new mechanism for beam combining, the topology-enabled beam combination (TEBC), from multiple spatial channels with high efficiency based on a unidirectional topological edge state. We show that the topologically protected power orthogonal excitation arising from both the unidirectional edge states and the energy conservation ensures -0.31dB (93%) efficiency experimentally for a multi-channel combination of coherent microwaves at 9.1-9.3 GHz. Moreover, we demonstrate broadband, phase insensitive, and high-efficiency beam combination using the TEBC mechanism with one single topological photonic crystal device, which significantly reduces the device footprint and design complexity. Our scheme transcends the limits of the required phase correlations in the scenario of coherent beam combination and the number of combined channels in the scenario of incoherent beam combination.

physics.optics↗

arXiv2022

Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks

Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official qrels files; this paper reports results based on the corrected qrels files. The noise is due to a fatal bug in the backend of our relevance assessment interface. More specifically, at WWW-2, WWW-3, and WWW-4, two versions of pool files were created for each English topic: a PRI ("prioritised") file, which uses the NTCIRPOOL script to prioritise likely relevant documents, and a RND ("randomised") file, which randomises the pooled documents. This was done for the purpose of studying the effect of document ordering for relevance assessors. However, the programmer who wrote the interface backend assumed that a combination of a topic ID and a document rank in the pool file uniquely determines a document ID; this is obviously incorrect as we have two versions of pool files. The outcome is that all the PRI-based relevance labels for the WWW-2 test collection are incorrect (while all the RND-based relevance labels are correct), and all the RND-based relevance labels for the WWW-3 and WWW-4 test collections are incorrect (while all the PRI-based relevance labels are correct). This bug was finally discovered at the NTCIR-16 WWW-4 task when the first seven authors of this paper served as Gold assessors (i.e., topic creators who define what is relevant) and closely examined the disagreements with Bronze assessors (i.e., non-topic-creators; non-experts). We would like to apologise to the WWW participants and the NTCIR chairs for the inconvenience and confusion caused due to this bug.

cs.IR↗

arXiv2022

Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension

Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i.e., passage and question. The analysis shows that the representation of source sequence becomes more coarse-grained from finegrained as the encoding layer increases. It is generally believed that with the growing number of layers in deep neural networks, the encoding process will gather relevant information for each location increasingly, resulting in more coarse-grained representations, which adds the likelihood of similarity to other locations (referring to homogeneity). Such phenomenon will mislead the model to make wrong judgement and degrade the performance. In this paper, we argue that it would be better if the predictor could exploit representations of different granularity from the encoder, providing different views of the source sequences, such that the expressive power of the model could be fully utilized. To this end, we propose a novel approach called Adaptive Bidirectional Attention-Capsule Network (ABA-Net), which adaptively exploits the source representations of different levels to the predictor. Furthermore, due to the better representations are at the core for boosting MRC performance, the capsule network and self-attention module are carefully designed as the building blocks of our encoders, which provides the capability to explore the local and global representations, respectively. Experimental results on three benchmark datasets, i.e., SQuAD 1.0, SQuAD 2.0 and COQA, demonstrate the effectiveness of our approach. In particular, we set the new state-of-the-art performance on the SQuAD 1.0 dataset

cs.CL↗

arXiv2022

Automatic Prosody Annotation with Pre-Trained Text-Speech Model

Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability. However, the acquisition of prosodic boundary labels relies on manual annotation, which is costly and time-consuming. In this paper, we propose to automatically extract prosodic boundary labels from text-audio data via a neural text-speech model with pre-trained audio encoders. This model is pre-trained on text and speech data separately and jointly fine-tuned on TTS data in a triplet format: {speech, text, prosody}. The experimental results on both automatic evaluation and human evaluation demonstrate that: 1) the proposed text-speech prosody annotation framework significantly outperforms text-only baselines; 2) the quality of automatic prosodic boundary annotations is comparable to human annotations; 3) TTS systems trained with model-annotated boundaries are slightly better than systems that use manual ones.

cs.SD↗

arXiv2022

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows given the speech documents. In this task, our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering. To this end, instead of directly adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which effectively ingests cross-modal information to achieve fine-grained representations of the speech and language modalities. Moreover, we propose a simple and novel mechanism, termed Dual Attention, by encouraging better alignments between audio and text to ease the process of knowledge transfer. To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations. The performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of cross-modal information integration. Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering tasks.

cs.CL↗

arXiv2022

Design optimization of band-pass filter based on parity-time symmetry coupled-resonant

Integrated optical filter based on microring resonators plays a critical role in many applications, ranging from wavelength division multiplexing and switching to channel routing. Bandwidth tunable filters are capable of meeting the on-demand flexible operations in complex situations, due to their advantages of scalability, multi-function, and energy-saving. It has been investigated recently that parity-time (PT) symmetry coupled-resonant systems can be applied to the bandwidth-tunable filters. However, due to the trade-off between the bandwidth-tunable contrast ratio and insertion loss of system, the bandwidth-tunable contrast ratio of this method is severely limited. Here, the bandwidth-tunable contrast ratio is defined as the maximum bandwidth divided by the minimum bandwidth. In this work, we show that high bandwidth-tunable contrast ratio and low insertion loss of system can be achieved simultaneously by increasing the coupling strength between the input port and the resonant. System characterizations under different coupling states reveal that the low insertion loss can be obtained when the system initially operates at the over-coupling condition. A high bandwidth-tunable contrast ratio PT-symmetry band-pass filter with moderate insertion loss is shown on the Silicon platform. Our scheme provides an effective method to reduce the insertion loss of on-chip tunable filters, which is also applicable to the high-order cascaded microring systems.

physics.optics↗

arXiv2022

Detecting Recolored Image by Spatial Correlation

Image forensics, aiming to ensure the authenticity of the image, has made great progress in dealing with common image manipulation such as copy-move, splicing, and inpainting in the past decades. However, only a few researchers pay attention to an emerging editing technique called image recoloring, which can manipulate the color values of an image to give it a new style. To prevent it from being used maliciously, the previous approaches address the conventional recoloring from the perspective of inter-channel correlation and illumination consistency. In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Through theoretical and numerical analysis, we find that the recoloring operation will inevitably destroy the spatial correlation between pixels, implying a new prior of statistical discriminability. Based on such fact, we generate a set of spatial correlation features and learn the informative representation from the set via a convolutional neural network. To train our network, we use three recoloring methods to generate a large-scale and high-quality data set. Extensive experimental results in two recoloring scenes demonstrate that the spatial correlation features are highly discriminative. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.

cs.CV↗

arXiv2022

Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling

Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages: e.g., mask language modeling objective requires local understanding of the masked token and the span-extraction objective requires global understanding and reasoning of the input passage/paragraph and question, leading to the discrepancy between pre-training and xMRC. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap in a self-supervised manner. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel sequences via unsupervised cross-lingual instance-wise training signals during pre-training. By these means, our methods not only bridge the gap between pretrain-finetune, but also enhance PLMs to better capture the alignment between different languages. Extensive experiments prove that our method achieves clearly superior results on multiple xSL benchmarks with limited pre-training data. Our methods also surpass the previous state-of-the-art methods by a large margin in few-shot data settings, where only a few hundred training examples are available.

cs.CL↗

arXiv2022

On Performance Loss of DOA Measurement Using Massive MIMO Receiver with Mixed-ADCs

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including beyond fifth generation (B5G) and sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this paper, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramer-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement.

cs.IT↗

arXiv2021

From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension

Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages. The recent approaches use training data only in a resource-rich language like English to fine-tune large-scale cross-lingual pre-trained language models. Due to the big difference between languages, a model fine-tuned only by a source language may not perform well for target languages. Interestingly, we observe that while the top-1 results predicted by the previous approaches may often fail to hit the ground-truth answers, the correct answers are often contained in the top-k predicted results. Based on this observation, we develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning (AA-CL) mechanism is developed to learn the fine difference between the accurate answer and other candidates. Our extensive experiments show that our model significantly outperforms a series of strong baselines on two cross-lingual MRC benchmark datasets.

cs.LG↗

arXiv2021

Non-Hermitian singularities induced single-mode depletion and soliton formation in microresonators

On-chip manipulation of single resonance over broad background comb spectra of microring resonators is indispensable, ranging from tailoring laser emission, optical signal processing to non-classical light generation, yet challenging without scarifying the quality factor or inducing additional dispersive effects. Here, we propose an experimentally feasible platform to realize on-chip selective depletion of single resonance in microring with decoupled dispersion and dissipation, which are usually entangled by Kramer-Kroning relation. Thanks to the existence of non-Hermitian singularity, unsplit but significantly increased dissipation of the selected resonance is achieved due to the simultaneous collapse of eigenvalues and eigenvectors, fitting elegantly the requirement of pure single-mode depletion. With delicate yet experimentally feasible parameters, we show explicit evidence of modulation instability as well as deterministic single soliton generation in microresonators induced by depletion in normal and anomalous dispersion regime, respectively. Our findings connect non-Hermitian singularities to wide range of applications associated with selective single mode manipulation in microwave photonics, quantum optics, ultrafast optics and beyond.

physics.optics↗

arXiv2021

Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array

In this paper, we present a new scenario of direction of arrival (DOA) estimation using massive multiple-input multiple-output (MIMO) receive array with low-resolution analog-to-digital convertors (ADCs), which can strike a good balance between performance and circuit cost. Based on the linear additive quantization noise model (AQNM), the effect of low-resolution ADCs on the methods, such as Root-MUSIC method, is analyzed. Also, the closed-form expression of Cramer-Rao lower bound (CRLB) is derived to evaluate the performance loss caused by the low-resolution ADCs. The simulation results show that the Root-MUSIC methods can achieve the corresponding CRLB. Furthermore, 2-3 bits are acceptable for most applications if the 1dB performance loss.

cs.IT↗

arXiv2021

Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering

Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks.

cs.CL↗

arXiv2021

Towards Visual Explainable Active Learning for Zero-Shot Classification

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attributes. Designing a suitable class-attribute matrix is the key to the subsequent procedure, but this design process is tedious and trial-and-error with no guidance. This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems. This approach promotes human-AI teaming with four actions (ask, explain, recommend, respond) in each interaction loop. The machine asks contrastive questions to guide humans in the thinking process of attributes. A novel visualization called semantic map explains the current status of the machine. Therefore analysts can better understand why the machine misclassifies objects. Moreover, the machine recommends the labels of classes for each attribute to ease the labeling burden. Finally, humans can steer the model by modifying the labels interactively, and the machine adjusts its recommendations. The visual explainable active learning approach improves humans' efficiency of building zero-shot classification models interactively, compared with the method without guidance. We justify our results with user studies using the standard benchmarks for zero-shot classification.

cs.HC↗

arXiv2021

Text Anchor Based Metric Learning for Small-footprint Keyword Spotting

Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, for metric learning, due to data limitations, the speech anchor is highly susceptible to the acoustic environment and speakers. Also, we note that the 1D-CNN models have limited capability to capture long-term temporal acoustic features. To address the above problems, we propose to utilize text anchors to improve the stability of anchors. Furthermore, a new type of model (LG-Net) is exquisitely designed to promote long-short term acoustic feature modeling based on 1D-CNN and self-attention. Experiments are conducted on Google Speech Commands Dataset version 1 (GSCDv1) and 2 (GSCDv2). The results demonstrate that the proposed text anchor based metric learning method shows consistent improvements over speech anchor on representative CNN-based models. Moreover, our LG-Net model achieves SOTA accuracy of 97.67% and 96.79% on two datasets, respectively. It is encouraged to see that our lighter LG-Net with only 74k parameters obtains 96.82% KWS accuracy on the GSCDv1 and 95.77% KWS accuracy on the GSCDv2.

cs.SD↗