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Yibo Wang

Publications and source records attributed to Yibo Wang.

At least 163 records · Page 9Linked to original sources
arXiv2024

Delayed and fast rising radio flares from an optical and X-ray detected tidal disruption event in the center of a dwarf galaxy

AT2018cqh is a unique tidal disruption event (TDE) candidate discovered in a dwarf galaxy. Both the light curve fitting and galaxy scaling relationships suggest a central black hole mass in the range of 5.9 175 days, a flattening lasting about 544 days, and a phase with another steep rise. The rapid rise in radio flux coupled with the slow decay in the X-ray emission points to a delayed launching of outflow, perhaps due to a transition in the accretion state. However, known accretion models can hardly explain the origins of the secondary radio flare that is rising even more rapidly in comparison with the initial one. If confirmed, AT2018cqh would be a rare TDE in a dwarf galaxy exhibiting optical, X-ray and radio flares. We call for continued multi-frequency radio observations to monitor its spectral and temporal evolution, which may help to reveal new physical processes that are not included in standard TDE models.

astro-ph.HE↗
arXiv2023

kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning

Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags. Recently, Large Language Models (LLMs) have achieved impressive performance in synthesizing computer programs based on a natural language prompt, mitigating the gap between natural language and structured programs. Our paper focuses on harnessing the capabilities of LLMs for semantic parsing tasks, addressing the following three key research questions: 1) How can LLMs be effectively utilized for semantic parsing tasks? 2) What defines an effective prompt? and 3) How can LLM overcome the length constraint and streamline prompt design by including all examples as prompts? We introduce k Nearest Neighbor In-Context Learning(kNN-ICL), which simplifies prompt engineering by allowing it to be built on top of any design strategy while providing access to all demo examples. Extensive experiments show that: 1)Simple ICL without kNN search can achieve a comparable performance with strong supervised models on the TOP tasks, and 2) kNN-ICL significantly improves the comprehension of complex requests by seamlessly integrating ICL with a nearest-neighbor approach. Notably, this enhancement is achieved without the need for additional data or specialized prompts.

cs.CL↗
arXiv2023

JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction

Product attribute value extraction is an important task in e-Commerce which can help several downstream applications such as product search and recommendation. Most previous models handle this task using sequence labeling or question answering method which rely on the sequential position information of values in the product text and are vulnerable to data discrepancy between training and testing. This limits their generalization ability to real-world scenario in which each product can have multiple descriptions across various shopping platforms with different composition of text and style. They also have limited zero-shot ability to new values. In this paper, we propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE to predict values without the necessity of position information of values in the text. Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text. Besides, two variants of our model are designed for open-world and closed-world scenarios. In addition, copy mechanism introduced in the first variant based on value generation can improve its zero-shot ability for identifying unseen values. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines and its generalization ability of predicting new values.

cs.CL↗
arXiv2023

Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification

Aspect-based meeting transcript summarization aims to produce multiple summaries, each focusing on one aspect of content in a meeting transcript. It is challenging as sentences related to different aspects can mingle together, and those relevant to a specific aspect can be scattered throughout the long transcript of a meeting. The traditional summarization methods produce one summary mixing information of all aspects, which cannot deal with the above challenges of aspect-based meeting transcript summarization. In this paper, we propose a two-stage method for aspect-based meeting transcript summarization. To select the input content related to specific aspects, we train a sentence classifier on a dataset constructed from the AMI corpus with pseudo-labeling. Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary. Experimental results on the AMI corpus outperform many strong baselines, which verifies the effectiveness of our proposed method.

cs.CL↗
arXiv2023

Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation

Large Language Models (LLMs) can generate biased and toxic responses. Yet most prior work on LLM gender bias evaluation requires predefined gender-related phrases or gender stereotypes, which are challenging to be comprehensively collected and are limited to explicit bias evaluation. In addition, we believe that instances devoid of gender-related language or explicit stereotypes in inputs can still induce gender bias in LLMs. Thus, in this work, we propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes. This approach employs three types of inputs generated through three distinct strategies to probe LLMs, aiming to show evidence of explicit and implicit gender biases in LLMs. We also utilize explicit and implicit evaluation metrics to evaluate gender bias in LLMs under different strategies. Our experiments demonstrate that an increased model size does not consistently lead to enhanced fairness and all tested LLMs exhibit explicit and/or implicit gender bias, even when explicit gender stereotypes are absent in the inputs.

cs.CL↗
arXiv2023

Aperture Diffraction for Compact Snapshot Spectral Imaging

We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras. Then we introduce a new optical design that each point in the object space is multiplexed to discrete encoding locations on the mosaic filter sensor by diffraction-based spatial-spectral projection engineering generated from the orthogonal mask. The orthogonal projection is uniformly accepted to obtain a weakly calibration-dependent data form to enhance modulation robustness. Meanwhile, the Cascade Shift-Shuffle Spectral Transformer (CSST) with strong perception of the diffraction degeneration is designed to solve a sparsity-constrained inverse problem, realizing the volume reconstruction from 2D measurements with Large amount of aliasing. Our system is evaluated by elaborating the imaging optical theory and reconstruction algorithm with demonstrating the experimental imaging under a single exposure. Ultimately, we achieve the sub-super-pixel spatial resolution and high spectral resolution imaging. The code will be available at: https://github.com/Krito-ex/CSST.

cs.CV↗
arXiv2023

Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables

Question answering on tabular data (a.k.a TableQA), which aims at generating answers to questions grounded on a provided table, has gained significant attention recently. Prior work primarily produces concise factual responses through information extraction from individual or limited table cells, lacking the ability to reason across diverse table cells. Yet, the realm of free-form TableQA, which demands intricate strategies for selecting relevant table cells and the sophisticated integration and inference of discrete data fragments, remains mostly unexplored. To this end, this paper proposes a generalized three-stage approach: Table-to- Graph conversion and cell localizing, external knowledge retrieval, and the fusion of table and text (called TAG-QA), to address the challenge of inferring long free-form answers in generative TableQA. In particular, TAG-QA (1) locates relevant table cells using a graph neural network to gather intersecting cells between relevant rows and columns, (2) leverages external knowledge from Wikipedia, and (3) generates answers by integrating both tabular data and natural linguistic information. Experiments showcase the superior capabilities of TAG-QA in generating sentences that are both faithful and coherent, particularly when compared to several state-of-the-art baselines. Notably, TAG-QA surpasses the robust pipeline-based baseline TAPAS by 17% and 14% in terms of BLEU-4 and PARENT F-score, respectively. Furthermore, TAG-QA outperforms the end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score, respectively.

cs.CL↗
arXiv2023

Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been proposed to shape the NER as a machine reading comprehension problem (also termed MRC-based NER), in which entity recognition is achieved by answering the formulated questions related to pre-defined entity types through MRC, based on the contexts. However, these works ignore the label dependencies among entity types, which are critical for precisely recognizing named entities. In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER. We decompose MRC-based NER into multiple tasks and use a self-attention module to capture label dependencies. Comprehensive experiments on both nested NER and flat NER datasets are conducted to validate the effectiveness of the proposed Multi-NER. Experimental results show that Multi-NER can achieve better performance on all datasets.

cs.CL↗
arXiv2023

Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach

Current data-driven predictive control (DDPC) methods heavily rely on data collected in open-loop operation with elaborate design of inputs. However, due to safety or economic concerns, systems may have to be under feedback control, where only closed-loop data are available. In this context, it remains challenging to implement DDPC using closed-loop data. In this paper, we propose a new DDPC method using closed-loop data by means of instrumental variables (IVs). By drawing from closed-loop subspace identification, the use of two forms of IVs is suggested to address the closed-loop issues caused by feedback control and the correlation between inputs and noise. Furthermore, a new DDPC formulation with a novel IV-inspired regularizer is proposed, where a balance between control cost minimization and weighted least-squares data fitting can be made for improvement of control performance. Numerical examples and application to a simulated industrial furnace showcase the improved performance of the proposed DDPC based on closed-loop data.

math.OC↗
arXiv2023

Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting

Automatically detecting software failures is an important task and a longstanding challenge. It requires finding failure-inducing test cases whose test input can trigger the software's fault, and constructing an automated oracle to detect the software's incorrect behaviors. Recent advancement of large language models (LLMs) motivates us to study how far this challenge can be addressed by ChatGPT, a state-of-the-art LLM. Unfortunately, our study shows that ChatGPT has a low probability (28.8%) of finding correct failure-inducing test cases for buggy programs. A possible reason is that finding failure-inducing test cases requires analyzing the subtle code differences between a buggy program and its correct version. When these two versions have similar syntax, ChatGPT is weak at recognizing subtle code differences. Our insight is that ChatGPT's performance can be substantially enhanced when ChatGPT is guided to focus on the subtle code difference. We have an interesting observation that ChatGPT is effective in inferring the intended behaviors of a buggy program. The intended behavior can be leveraged to synthesize programs, in order to make the subtle code difference between a buggy program and its correct version (i.e., the synthesized program) explicit. Driven by this observation, we propose a novel approach that synergistically combines ChatGPT and differential testing to find failure-inducing test cases. We evaluate our approach on Quixbugs (a benchmark of buggy programs), and compare it with state-of-the-art baselines, including direct use of ChatGPT and Pynguin. The experimental result shows that our approach has a much higher probability (77.8%) of finding correct failure-inducing test cases, 2.7X as the best baseline.

cs.SE↗
arXiv2023

Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions

Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc.However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time. At the same time, when pretraining is performed on synthetic datasets, the data distribution of the synthetic datasets is far from the data distribution in the real scene. These all pose a huge challenge to the current pixel-level text segmentation algorithms.To alleviate the above problems, we propose a self-supervised scene text segmentation algorithm with layered decoupling of representations derived from the object-centric manner to segment images into texts and background. In our method, we propose two novel designs which include Region Query Module and Representation Consistency Constraints adapting to the unique properties of text as complements to Auto Encoder, which improves the network's sensitivity to texts.For this unique design, we treat the polygon-level masks predicted by the text localization model as extra input information, and neither utilize any pixel-level mask annotations for training stage nor pretrain on synthetic datasets.Extensive experiments show the effectiveness of the method proposed. On several public scene text datasets, our method outperforms the state-of-the-art unsupervised segmentation algorithms.

cs.CV↗
arXiv2023

Two Candidate Obscured Tidal Disruption Events Coincident with High-energy Neutrinos

Recently, three optical tidal disruption event (TDE) candidates discovered by the Zwicky Transient Facility (ZTF) have been suggested to be coincident with high-energy neutrinos. They all exhibit unusually strong dust infrared (IR) echoes, with their peak times matching the neutrino arrival time even better than the optical peaks. We hereby report on two new TDE candidates that are spatially and temporally coincident with neutrinos by matching our sample of mid-infrared outbursts in nearby galaxies (MIRONG) with Gold alerts of IceCube high-energy neutrino events up to June 2022. The two candidates show negligible optical variability according to their ZTF light curves and can therefore be classified as part of the growing population of obscured TDE candidates. The chance probability of finding two such candidates about $\sim3\%$ by redistributing the MIRONG sources randomly in the SDSS footprint, which will be as low as $\sim0.1\%$ (or $\sim0.2\%$) if we limit to sources with increased fluxes (or variability amplitudes) comparable with the matched two sources. Our findings further support the potential connection between high-energy neutrinos and TDEs in dusty environments by increasing the total number of neutrino-associated TDE and TDE candidates to five, although the underlying physics remains poorly understood.

astro-ph.HE↗
arXiv2023

Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline

We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color. Accordingly, it is far more challenging to distinguish insubstantial objects in a single static frame and the collaborative representation of spatial and temporal information is crucial. Thus, we construct an IOD-Video dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges. In addition, we develop a spatio-temporal aggregation framework for IOD, in which different backbones are deployed and a spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along the time axis. Experiments conducted on IOD-Video dataset demonstrate that spatio-temporal aggregation can significantly improve the performance of IOD. We hope our work will attract further researches into this valuable yet challenging task. The code will be available at: \url{https://github.com/CalayZhou/IOD-Video}.

cs.CV↗
arXiv2023

Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.

cs.IR↗
arXiv2023

DBFed: Debiasing Federated Learning Framework based on Domain-Independent

As digital transformation continues, enterprises are generating, managing, and storing vast amounts of data, while artificial intelligence technology is rapidly advancing. However, it brings challenges in information security and data security. Data security refers to the protection of digital information from unauthorized access, damage, theft, etc. throughout its entire life cycle. With the promulgation and implementation of data security laws and the emphasis on data security and data privacy by organizations and users, Privacy-preserving technology represented by federated learning has a wide range of application scenarios. Federated learning is a distributed machine learning computing framework that allows multiple subjects to train joint models without sharing data to protect data privacy and solve the problem of data islands. However, the data among multiple subjects are independent of each other, and the data differences in quality may cause fairness issues in federated learning modeling, such as data bias among multiple subjects, resulting in biased and discriminatory models. Therefore, we propose DBFed, a debiasing federated learning framework based on domain-independent, which mitigates model bias by explicitly encoding sensitive attributes during client-side training. This paper conducts experiments on three real datasets and uses five evaluation metrics of accuracy and fairness to quantify the effect of the model. Most metrics of DBFed exceed those of the other three comparative methods, fully demonstrating the debiasing effect of DBFed.

cs.LG↗
arXiv2023

AT 2023clx: the Faintest and Closest Optical Tidal Disruption Event Discovered in Nearby Star-forming Galaxy NGC 3799

We report the discovery of a faint optical tidal disruption event (TDE) in the nearby star-forming galaxy NGC 3799. Identification of the TDE is based on its position at the galaxy nucleus, a light curve declining as t^-5/3, a blue continuum with an almost constant blackbody temperature of ~12,000K, and broad (~15,000kms^-1) Balmer lines and characteristic He~II 4686A emission. The light curve of AT 2023clx peaked at an absolute magnitude of -17.16mag in the g-band and a maximum blackbody bolometric luminosity of 4.56*10^42 ergs^-1, making it the faintest TDE discovered to date. With a redshift of 0.01107 and a corresponding luminosity distance of 47.8Mpc, it is also the closest optical TDE ever discovered to our best knowledge. Furthermore, our analysis of Swift/XRT observations of AT 2023clx yields a very tight 3 sigma upper limit of 9.53*10^39 ergs^-1 in the range 0.3--10keV. AT 2023clx, together with very few other faint TDEs such as AT 2020wey, prove that there are probably a large number of faint TDEs yet to be discovered at higher redshifts, which is consistent with the prediction of luminosity functions (LFs). The upcoming deeper optical time-domain surveys, such as the Legacy Survey of Space and Time (LSST) and the Wide-Field Survey Telescope (WFST) will discover more TDEs at even lower luminosities, allowing for a more precise constraint of the low-end of the LF.

astro-ph.HE↗
arXiv2023

The space cold atom interferometer for testing the equivalence principle in the China Space Station

The precision of the weak equivalence principle (WEP) test using atom interferometers (AIs) is expected to be extremely high in microgravity environment. The microgravity scientific laboratory cabinet (MSLC) in the China Space Station (CSS) can provide a higher-level microgravity than the CSS itself, which provides a good experimental environment for scientific experiments that require high microgravity. We designed and realized a payload of a dual-species cold rubidium atom interferometer. The payload is highly integrated and has a size of 460 mm * 330 mm * 260 mm. It will be installed in the MSLC to carry out high-precision WEP test experiment. In this article, we introduce the constraints and guidelines of the payload design, the compositions and functions of the scientific payload, the expected test precision in space, and some results of the ground test experiments

physics.atom-ph↗
arXiv2023

Projection-free Online Learning with Arbitrary Delays

Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints. However, previous studies assume that any queried gradient is revealed immediately, which may not hold in practice and limits their applications. To address this limitation, we generalize the online Frank-Wolfe (OFW) algorithm and the online smooth projection-free (OSPF) algorithm, which are state-of-the-art LO-based projection-free online algorithms for non-smooth and smooth functions respectively, into a delayed setting where queried gradients can be delayed by arbitrary rounds. Specifically, the main idea of our generalized OFW is to perform an update similar to the original OFW after receiving any delayed gradient, and play the latest decision for each round. Moreover, the essential change on OSPF is to replace the sum of queried gradients, which is originally utilized in each update, with the sum of available gradients. Despite their simplicities, our novel analysis shows that under a relatively large amount of delay, the generalized OFW and OSPF enjoy the same regret bound as OFW and OSPF in the non-delayed setting, respectively.

cs.LG↗