arXiv subjects

Yuchen Zhang

Publications and source records attributed to Yuchen Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2022

LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension

The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing and analyzing large collections of documents, automating such tasks with NLP tools emerges as a key challenge. Many popular language models, such as BERT or RoBERTa, are general-purpose models, which have limitations on processing specialized legal terminology and syntax. In addition, legal documents may contain specialized vocabulary from other domains, such as medical terminology in personal injury text. Here, we propose LegalRelectra, a legal-domain language model that is trained on mixed-domain legal and medical corpora. We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text. Our training architecture implements the Electra framework, but utilizes Reformer instead of BERT for its generator and discriminator. We show that this improves the model's performance on processing long passages and results in better long-range text comprehension.

cs.CL↗

arXiv2022

Decomposing the Fundamentals of Creepy Stories

Fear is a universal concept; people crave it in urban legends, scary movies, and modern stories. Open questions remain, however, about why these stories are scary and more generally what scares people. In this study, we explore these questions by analyzing tens of thousands of scary stories on forums (known as subreddits) in a social media website, Reddit. We first explore how writing styles have evolved to keep these stories fresh before we analyze the stable core techniques writers use to make stories scary. We find that writers have changed the themes of their stories over years from haunted houses to school-related themes, body horror, and diseases. Yet some features remain stable; words associated with pseudo-human nouns, such as clown or devil are more common in scary stories than baselines. In addition, we collect a range of datasets that annotate sentences containing fear. We use these data to develop a high-accuracy fear detection neural network model, which is used to quantify where people express fear in scary stories. We find that sentences describing fear, and words most often seen in scary stories, spike at particular points in a story, possibly as a way to keep the readers on the edge of their seats until the story's conclusion. These results provide a new understanding of how authors cater to their readers, and how fear may manifest in stories.

cs.CY↗

arXiv2022

Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources

Due to the increasing popularity of electric vehicles (EVs) and the technological advancement of EV electronics, the vehicle-to-grid (V2G) technique and large-scale scheduling algorithms have been developed to achieve a high level of renewable energy and power grid stability. This paper proposes a deep reinforcement learning (DRL) method for the continuous charging/discharging coordination strategy in aggregating large-scale EVs in V2G mode with renewable energy sources (RES). The DRL coordination strategy can efficiently optimize the electric vehicle aggregator's (EVA's) real-time charging/discharging power with the state of charge (SOC) constraints of the EVA and the individual EV. Compared with uncontrolled charging, the load variance is reduced by 97.37$\%$ and the charging cost by 76.56$\%$. The DRL coordination strategy further demonstrates outstanding transfer learning ability to microgrids with RES and large-scale EVA, as well as the complicated weekly scheduling. The DRL coordination strategy demonstrates flexible, adaptable, and scalable performance for the large-scale V2G under realistic operating conditions.

eess.SY↗

arXiv2022

Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization

Developing robust vision-guided controllers for quadrupedal robots in complex environments, with various obstacles, dynamical surroundings and uneven terrains, is very challenging. While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world. Our key insight is that aside from the discrepancy in the domain gap, in visual appearance between the simulation and the real world, the latency from the control pipeline is also a major cause of difficulty. In this paper, we propose Multi-Modal Delay Randomization (MMDR) to address this issue when training RL agents. Specifically, we simulate the latency of real hardware by using past observations, sampled with randomized periods, for both proprioception and vision. We train the RL policy for end-to-end control in a physical simulator without any predefined controller or reference motion, and directly deploy it on the real A1 quadruped robot running in the wild. We evaluate our method in different outdoor environments with complex terrains and obstacles. We demonstrate the robot can smoothly maneuver at a high speed, avoid the obstacles, and show significant improvement over the baselines. Our project page with videos is at https://mehooz.github.io/mmdr-wild/.

cs.RO↗

arXiv2022

Distributed Estimation for Interconnected Systems with Arbitrary Coupling Structures

This paper is concerned with the problem of distributed estimation for time-varying interconnected dynamic systems with arbitrary coupling structures. To guarantee the robustness of the designed estimators, novel distributed stability conditions are proposed with only local information and the information from neighbors. Then, simplified stability conditions which do not require timely exchange of neighbors' estimator gain information is further developed for systems with delayed communication. By merging these subsystem-level stability conditions and the optimization-based estimator gain design, the distributed, stable and optimal estimators are proposed. Quite notably, these optimization solutions can be easily obtained by standard software packages, and it is also shown that the designed estimators are scalable in the sense of adding or subtracting subsystems. Finally, an illustrative example is employed to show the effectiveness of the proposed methods.

eess.SY↗

arXiv2022

Building Embedded Systems Like It's 1996

Embedded devices are ubiquitous. However, preliminary evidence shows that attack mitigations protecting our desktops/servers/phones are missing in embedded devices, posing a significant threat to embedded security. To this end, this paper presents an in-depth study on the adoption of common attack mitigations on embedded devices. Precisely, it measures the presence of standard mitigations against memory corruptions in over 10k Linux-based firmware of deployed embedded devices. The study reveals that embedded devices largely omit both user-space and kernel-level attack mitigations. The adoption rates on embedded devices are multiple times lower than their desktop counterparts. An equally important observation is that the situation is not improving over time. Without changing the current practices, the attack mitigations will remain missing, which may become a bigger threat in the upcoming IoT era. Throughout follow-up analyses, we further inferred a set of factors possibly contributing to the absence of attack mitigations. The exemplary ones include massive reuse of non-protected software, lateness in upgrading outdated kernels, and restrictions imposed by automated building tools. We envision these will turn into insights towards improving the adoption of attack mitigations on embedded devices in the future.

cs.CR↗

arXiv2022

Performance Analysis for Covert Communications Under Faster-than-Nyquist Signaling

In this letter, we analyze the performance of covert communications under faster-than-Nyquist (FTN) signaling in the Rayleigh block fading channel. Both Bayesian criterion- and Kullback-Leibler (KL) divergence-based covertness constraints are considered. Especially, for KL divergence-based one, we prove that both the maximum transmit power and covert rate under FTN signaling are higher than those under Nyquist signaling. Numerical results coincide with our analysis and validate the advantages of FTN signaling to realize covert data transmission.

cs.IT↗

arXiv2021

The Influence of Data Pre-processing and Post-processing on Long Document Summarization

Long document summarization is an important and hard task in the field of natural language processing. A good performance of the long document summarization reveals the model has a decent understanding of the human language. Currently, most researches focus on how to modify the attention mechanism of the transformer to achieve a higher ROUGE score. The study of data pre-processing and post-processing are relatively few. In this paper, we use two pre-processing methods and a post-processing method and analyze the effect of these methods on various long document summarization models.

cs.CL↗

arXiv2021

Kalman-Like Filter under Binary Sensors

This paper is concerned with the linear/nonlinear Kalman-like filtering problem under binary sensors. Since innovation represents new information in the sensor measurement and serves to correct the prediction for the Kalman-like filter (KLF), a novel uncertain measurement model is proposed such that the innovation generated from binary sensor can be captured. When considering linear dynamic systems, a conservative estimation error covariance with adjustable parameters is constructed by matrix inequality, and then an optimal filter gain is derived by minimizing its trace. Meanwhile, the optimal selection criterion of an adjustable parameter is developed by minimizing the upper bound of the conservative estimation error covariance. When considering nonlinear dynamic systems, a conservative estimation error covariance with adjustable parameters is also constructed via unscented transform and matrix inequalities. Then, following the idea of designing KLF in linear dynamic systems, the nonlinear filter gain and the optimal adjustable parameter are designed. Finally, $O_2$ content estimation and nonlinear numerical system are employed to show the effectiveness and advantages of the proposed methods.

eess.SY↗

arXiv2021

Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification; 2) Linear techniques perform better than non-linear techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification; 4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison.

cs.HC↗

arXiv2021

Facilitating Parallel Fuzzing with mutually-exclusive Task Distribution

Fuzz testing, or fuzzing, has become one of the de facto standard techniques for bug finding in the software industry. In general, fuzzing provides various inputs to the target program to discover unhandled exceptions and crashes. In business sectors where the time budget is limited, software vendors often launch many fuzzing instances in parallel as common means of increasing code coverage. However, most of the popular fuzzing tools in their parallel mode-naively run multiple instances concurrently, without elaborate distribution of workload. This can lead different instances to explore overlapped code regions, eventually reducing the benefits of concurrency. In this paper, we propose a general model to describe parallel fuzzing. This model distributes mutually-exclusive but similarly-weighted tasks to different instances, facilitating concurrency and also fairness across instances. Following this model, we develop a solution, called AFL-EDGE, to improve the parallel mode of AFL, considering a round of mutations to a unique seed as a task and adopting edge coverage to define the uniqueness of a seed. We have implemented AFL-EDGE on top of AFL and evaluated the implementation with AFL on 9 widely used benchmark programs. It shows that AFL-EDGE can benefit the edge coverage of AFL. In a 24-hour test, the increase of edge coverage brought by AFL-EDGE to AFL ranges from 9.49% to 10.20%, depending on the number of instances. As a side benefit, we discovered 14 previously unknown bugs.

cs.SE↗

arXiv2021

Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching

In this paper, we present our initial efforts for building a code-switching (CS) speech recognition system leveraging existing acoustic models (AMs) and language models (LMs), i.e., no training required, and specifically targeting intra-sentential switching. To achieve such an ambitious goal, new mechanisms for foreign pronunciation generation and language model (LM) enrichment have been devised. Specifically, we have designed an automatic approach to obtain high quality pronunciation of foreign language (FL) words in the native language (NL) phoneme set using existing acoustic phone decoders and an LSTM-based grapheme-to-phoneme (G2P) model. Improved accented pronunciations have thus been obtained by learning foreign pronunciations directly from data. Furthermore, a code-switching LM was deployed by converting the original NL LM into a CS LM using translated word pairs and borrowing statistics for the NL LM. Experimental evidence clearly demonstrates that our approach better deals with accented foreign pronunciations than techniques based on human labeling. Moreover, our best system achieves a 55.5% relative word error rate reduction from 34.4%, obtained with a conventional monolingual ASR system, to 15.3% on an intra-sentential CS task without harming the monolingual recognition accuracy.

eess.AS↗

arXiv2021

A Chandra Survey of $z\geq4.5$ Quasars

X-ray observations provide a unique probe of the accretion disk corona of supermassive black holes (SMBHs). In this paper, we present a uniform \emph{Chandra} X-ray data analysis of a sample of 152 $z\geq4.5$ quasars. We firmly detect 46 quasars of this sample in 0.5-2~keV above 3~$σ$ and calculate the upper limits of the X-ray flux of the remaining. We also estimate the power law photon index of the X-ray spectrum of 31 quasars. 24 of our sample quasars are detected in the FIRST or NVSS radio surveys; all of them are radio-loud. We statistically compare the X-ray properties of our $z\geq4.5$ quasars to other X-ray samples of AGN at different redshifts. The relation between the rest-frame X-ray luminosity and other quasar parameters, such as the bolometric luminosity, UV luminosity, or SMBH mass, show large scatters. These large scatters can be attributed to the narrow luminosity range at the highest redshift, the large measurement error based on relatively poor X-ray data, and the inclusion of radio-loud quasars in the sample. The $L_{\rm X}-L_{\rm UV}$ relationship is significantly sub-linear. We do not find a significant redshift evolution of the $L_{\rm X}-L_{\rm UV}$ relation, expressed either in the slope of this relation, or the departure of individual AGNs from the best-fit $α_{\rm OX}-L_{\rm UV}$ relation ($Δα_{\rm OX}$). The median value of the X-ray photon index is $Γ\approx1.79$, which does not show redshift evolution from $z=0$ to $z\sim7$. The X-ray and UV properties of the most distant quasars could potentially be used as a standard candle to constrain cosmological models. The large scatter of our sample on the Hubble diagram highlights the importance of future large unbiased deep X-ray and radio surveys in using quasars in cosmological studies.

astro-ph.HE↗

arXiv2021

The resolution of Euclidean massless field operators of higher spins on $\Bbb R^6$ and the $L^2$ method

The resolution of $4$-dimensional massless field operators of higher spins was constructed by Eastwood-Penrose-Wells by using the twistor method. Recently physicists are interested in $6$-dimensional physics including the massless field operators of higher spins on Lorentzian space $\Bbb R^{5,1}$. Its Euclidean version $\mathscr{D}_0$ and their function theory are discussed in \cite{wangkang3}. In this paper, we construct an exact sequence of Hilbert spaces as weighted $L^2$ spaces resolving $\mathscr{D}_0$: $$L^2_φ(\Bbb R^6, \mathscr{V}_0)\overset{\mathscr{D}_0}\longrightarrow L^2_φ(\Bbb R^6,\mathscr{V}_1)\overset{\mathscr{D}_1}\longrightarrow L^2_φ(\Bbb R^6, \mathscr{V}_2)\overset{\mathscr{D}_2}\longrightarrow L^2_φ(\Bbb R^6, \mathscr{V}_3)\longrightarrow 0,$$ with suitable operators $\mathscr{D}_l$ and vector spaces $\mathscr{V}_l$. Namely, we can solve $\mathscr{D}_{l}u=f$ in $L^2_φ(\Bbb R^6, \mathscr{V}_{l})$ when $\mathscr{D}_{l+1} f=0$ for $f\in L^2_φ(\Bbb R^6, \mathscr{V}_{l+1})$. This is proved by using the $L^2$ method in the theory of several complex variables, which is a general framework to solve overdetermined PDEs under the compatibility condition. To apply this method here, it is necessary to consider weighted $L^2$ spaces, an advantage of which is that any polynomial is $L^2_φ$ integrable. As a corollary, we prove that $$ P(\Bbb R^6, \mathscr{V}_0)\overset{\mathscr{D}_0}\longrightarrow P(\Bbb R^6,\mathscr{V}_1)\overset{\mathscr{D}_1}\longrightarrow P(\Bbb R^6, \mathscr{V}_2)\overset{\mathscr{D}_2}\longrightarrow P(\Bbb R^6, \mathscr{V}_3)\longrightarrow 0$$ is a resolution, where $P(\Bbb R^6, \mathscr{V}_l)$ is the space of all $\mathscr{V}_l$-valued polynomials. This provides an analytic way to construct a resolution of a differential operator acting on vector valued polynomials.

math-ph↗

arXiv2021

Task-Oriented Dialogue as Dataflow Synthesis

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset and code for replicating experiments are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.

cs.CL↗

arXiv2021

Adversarial example generation with AdaBelief Optimizer and Crop Invariance

Deep neural networks are vulnerable to adversarial examples, which are crafted by applying small, human-imperceptible perturbations on the original images, so as to mislead deep neural networks to output inaccurate predictions. Adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, most existing adversarial attacks often achieve relatively low success rates on adversarially trained networks and advanced defense models. In this paper, we propose AdaBelief Iterative Fast Gradient Method (ABI-FGM) and Crop-Invariant attack Method (CIM) to improves the transferability of adversarial examples. ABI-FGM and CIM can be readily integrated to build a strong gradient-based attack to further boost the success rates of adversarial examples for black-box attacks. Moreover, our method can also be naturally combined with other gradient-based attack methods to build a more robust attack to generate more transferable adversarial examples against the defense models. Extensive experiments on the ImageNet dataset demonstrate the method's effectiveness. Whether on adversarially trained networks or advanced defense models, our method has higher success rates than state-of-the-art gradient-based attack methods.

cs.CV↗

arXiv2020

Chandra Detection of Three X-ray Bright Quasars at z>5

We report Chandra detection of three UV bright radio quiet quasars at $z\gtrsim5$. We have collected a sufficient number of photons to extract an X-ray spectrum of each quasar to measure their basic X-ray properties, such as the X-ray flux, power law photon index ($Γ$), and optical-to-X-ray spectral slope ($α_{\rm OX}$). J074749+115352 at $z=5.26$ is the X-ray brightest radio-quiet quasar at $z>5$. It may have a short timescale variation (on a timescale of $\sim3800\rm~s$ in the observer's frame, or $\sim600\rm~s$ in the rest frame) which is however largely embedded in the statistical noise. We extract phase folded spectra of this quasar. There are two distinguishable states: a "high soft" state with an average X-ray flux $\sim2.7$ times of the "low hard" state, and a significantly steeper X-ray spectral slope ($Γ=2.40_{-0.32}^{+0.33}$ vs $1.78_{-0.24}^{+0.25}$). We also compare the three quasars detected in this paper to other quasar samples. We find that J074749+115352, with a SMBH mass of $M_{\rm SMBH}\approx1.8\times10^9\rm~M_\odot$ and an Eddington ratio of $λ_{\rm Edd}\approx2.3$, is extraordinarily X-ray bright. It has an average $α_{\rm OX}=-1.46\pm0.02$ and a 2-10 keV bolometric correction factor of $L_{\rm bol}/L_{\rm2-10keV}=42.4\pm5.8$, both significantly depart from some well defined scaling relations. We compare $Γ$ of the three quasars to other samples at different redshifts, and do not find any significant redshift evolution based on the limited sample of $z>5$ quasars with reliable measurements of the X-ray spectral properties.

astro-ph.HE↗

arXiv2020

The estimate of sensitivity for large infrared telescopes based on measured sky brightness and atmospheric extinction

In order to evaluate the ground-based infrared telescope sensitivity affected by the noise from the atmosphere, instruments and detectors, we construct a sensitivity model that can calculate limiting magnitudes and signal-to-noise ratio ($S/N$). The model is tested with tentative measurements of $\rm M'$-band sky brightness and atmospheric extinction obtained at the Ali and Daocheng sites. We find that the noise caused by an excellent scientific detector and instruments at $-135^\circ \rm C$ can be ignored compared to the $\rm M'$-band sky background noise. Thus, when $S/N=3$ and total exposure time is 1 second for 10 m telescopes, the magnitude limited by the atmosphere is $13.01^{\rm m}$ at Ali and $12.96^{\rm m}$ at Daocheng. Even under less-than-ideal circumstances, i.e., the readout noise of a deep cryogenic detector is less than $200e^-$ and the instruments are cooled to below $-87.2^\circ \rm C$, the above magnitudes decrease by $0.056^{\rm m}$ at most. Therefore, according to observational requirements with a large telescope in a given infrared band, astronomers can use this sensitivity model as a tool for guiding site surveys, detector selection and instrumental thermal-control.

astro-ph.IM↗