arXiv subjects

Junjie Wang

Publications and source records attributed to Junjie Wang.

At least 235 records · Page 13Linked to original sources

arXiv2023

Hierarchical Motion Planning under Probabilistic Temporal Tasks and Safe-Return Constraints

Safety is crucial for robotic missions within an uncertain environment. Common safety requirements such as collision avoidance are only state-dependent, which can be restrictive for complex missions. In this work, we address a more general formulation as safe-return constraints, which require the existence of a return-policy to drive the system back to a set of safe states with high probability. The robot motion is modeled as a Markov Decision Process (MDP) with probabilistic labels, which can be highly non-ergodic. The robotic task is specified as Linear Temporal Logic (LTL) formulas over these labels, such as surveillance and transportation. We first provide theoretical guarantees on the re-formulation of such safe-return constraints, and a baseline solution based on computing two complete product automata. Furthermore, to tackle the computational complexity, we propose a hierarchical planning algorithm that combines the feature-based symbolic and temporal abstraction with constrained optimization. It synthesizes simultaneously two dependent motion policies: the outbound policy minimizes the overall cost of satisfying the task with a high probability, while the return policy ensures the safe-return constraints. The problem formulation is versatile regarding the robot model, task specifications and safety constraints. The proposed hierarchical algorithm is more efficient and can solve much larger problems than the baseline solution, with only a slight loss of optimality. Numerical validations include simulations and hardware experiments of a search-and-rescue mission and a planetary exploration mission over various system sizes.

cs.RO↗

arXiv2023

Spectral conditions for graphs having all (fractional) $[a,b]$-factors

Let $a\leq b$ be two positive integers. We say that a graph $G$ has all $[a,b]$-factors if it has an $h$-factor for every function $h: V(G)\rightarrow \mathbb{Z}^+$ such that $a\le h(v) \le b$ for all $v\in V(G)$ and $\sum_{v\in V(G)}h(v)\equiv 0\pmod 2$, and has all fractional $[a,b]$-factors if it has a fractional $p$-factor for every $p: V(G) \rightarrow \mathbb{Z}^+$ such that $a\le p(v)\le b$ for all $v\in V(G)$. In this paper, we provide tight spectral radius conditions for graphs having all $[a,b]$-factors ($3\leq a<b$) and all fractional $[a,b]$-factors ($1\leq a<b$), respectively.

math.CO↗

arXiv2023

Superconductivity in an Orbital-reoriented SnAs Square Lattice: a Case Study of Li0.6Sn2As2 and NaSnAs

Searching for functional square lattices in layered superconductor systems offers an explicit clue to modify the electron behavior and find exotic properties. The trigonal SnAs3 structural units in SnAs-based systems are relatively conformable to distortion, which provides the possibility to achieve structurally topological transformation and higher superconducting transition temperatures. In the present work, the functional As square lattice was realized and activated in Li0.6Sn2As2 and NaSnAs through a topotactic structural transformation of trigonal SnAs3 to square SnAs4 under pressure, resulting in a record-high Tc among all synthesized SnAs-based compounds. Meanwhile, the conductive channel transfers from the out-of-plane pz orbital to the in-plane px+py orbitals, facilitating electron hopping within the square 2D lattice and boosting the superconductivity. The reorientation of p-orbital following a directed local structure transformation provides an effective strategy to modify layered superconductors.

cond-mat.supr-con↗

arXiv2022

Prediction of surface reconstructions using MAGUS

In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher (MAGUS). In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111) and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground states as well as a new SiC surface model in an extremely Si-rich environment.

cond-mat.mtrl-sci↗

arXiv2022

Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing

Automated GUI testing is widely used to help ensure the quality of mobile apps. However, many GUIs require appropriate text inputs to proceed to the next page which remains a prominent obstacle for testing coverage. Considering the diversity and semantic requirement of valid inputs (e.g., flight departure, movie name), it is challenging to automate the text input generation. Inspired by the fact that the pre-trained Large Language Model (LLM) has made outstanding progress in text generation, we propose an approach named QTypist based on LLM for intelligently generating semantic input text according to the GUI context. To boost the performance of LLM in the mobile testing scenario, we develop a prompt-based data construction and tuning method which automatically extracts the prompts and answers for model tuning. We evaluate QTypist on 106 apps from Google Play and the result shows that the passing rate of QTypist is 87%, which is 93% higher than the best baseline. We also integrate QTypist with the automated GUI testing tools and it can cover 42% more app activities, 52% more pages, and subsequently help reveal 122% more bugs compared with the raw tool.

cs.SE↗

arXiv2022

The Metamorphosis: Automatic Detection of Scaling Issues for Mobile Apps

As the bridge between users and software, Graphical User Interface (GUI) is critical to the app accessibility. Scaling up the font or display size of GUI can help improve the visual impact, readability, and usability of an app, and is frequently used by the elderly and people with vision impairment. Yet this can easily lead to scaling issues such as text truncation, component overlap, which negatively influence the acquirement of the right information and the fluent usage of the app. Previous techniques for UI display issue detection and cross-platform inconsistency detection cannot work well for these scaling issues. In this paper, we propose an automated method, dVermin, for scaling issue detection, through detecting the inconsistency of a view under the default and a larger display scale. The evaluation result shows that dVermin achieves 97% precision and 97% recall in issue page detection, and 84% precision and 91% recall for issue view detection, outperforming two state-of-the-art baselines by a large margin. We also evaluate dVermin with popular Android apps on F-droid, and successfully uncover 21 previously-undetected scaling issues with 20 of them being confirmed/fixed.

cs.SE↗

arXiv2022

Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning

The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging. Although the recurrent structure utilized in current advances helps to capture local dynamics, modeling only state transitions without an explicit understanding of environmental context limits the generalization ability of the dynamics model. To address this issue, we propose a Prototypical Context-Aware Dynamics (ProtoCAD) model, which captures the local dynamics by time consistent latent context and enables dynamics generalization in high-dimensional control tasks. ProtoCAD extracts useful contextual information with the help of the prototypes clustered over batch and benefits model-based RL in two folds: 1) It utilizes a temporally consistent prototypical regularizer that encourages the prototype assignments produced for different time parts of the same latent trajectory to be temporally consistent instead of comparing the features; 2) A context representation is designed which combines both the projection embedding of latent states and aggregated prototypes and can significantly improve the dynamics generalization ability. Extensive experiments show that ProtoCAD surpasses existing methods in terms of dynamics generalization. Compared with the recurrent-based model RSSM, ProtoCAD delivers 13.2% and 26.7% better mean and median performance across all dynamics generalization tasks.

cs.LG↗

arXiv2022

Bloch band structures and linear response theory of nonlinear systems

We investigate the Bloch bands and develop a linear response theory for nonlinear systems, where the interplay between topological parameters and nonlinearity leads to new band structures. The nonlinear system under consideration is described by the Qi-Wu-Zhang model with Kerr-type nonlinearity, which can be treated as a nonlinear version of Chern insulator. We explore the eigenenergies of the Hamiltonian and discuss its Bloch band structures as well as the condition of gap closing. A cone structure in the ground Bloch band and tubed structure in the excited Bloch band is found. We also numerically calculate the linear response of the nonlinear Chern insulator to external fields, finding that these new band structures break the condition of adiabatic evolution and make the linear response not quantized. This feature of response can be understood by examining the dynamics of the nonlinear system.

quant-ph↗

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

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective

We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis. Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. It not only adds generalization ability to models but also significantly reduces the number of parameters. Our method shares the merits of efficient training and deployment. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification. Our model achieves this success with only 235M parameters, which is substantially smaller than state-of-the-art models with billions of parameters. The code and pre-trained models are available at https://github.com/IDEA-CCNL/Fengshenbang-LM .

cs.CL↗

arXiv2022

LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.

eess.AS↗

arXiv2022

Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice.

cs.SE↗

arXiv2022

Spectral radius conditions for fractional $[a,b]$-covered graphs

A graph $G$ is called fractional $[a,b]$-covered if for every edge $e$ of $G$ there is a fractional $[a,b]$-factor with the indicator function $h$ such that $h(e)=1$. In this paper, we provide tight spectral radius conditions for graphs being fractional $[a,b]$-covered.

math.CO↗

arXiv2022

TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene

Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the relationship between the length of target speech and the size of the convolution kernels. In this paper, we combine Selective Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive field of convolution kernels to solve the problem of variable length of target voice while keeping low-complexity. GridMask is a data augmentation strategy by masking part of the raw data or feature area. It can enhance the generalization of the model as the role of dropout. In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we adopt AutoML to search best structure of TC-SKNet and hyperparameters of GridMask for improving the classification performance. As a result, a peak accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters only use 20.9 K.

cs.SD↗

arXiv2022

Putting Them under Microscope: A Fine-Grained Approach for Detecting Redundant Test Cases in Natural Language

Natural language (NL) documentation is the bridge between software managers and testers, and NL test cases are prevalent in system-level testing and other quality assurance activities. Due to reasons such as requirements redundancy, parallel testing, and tester turnover within long evolving history, there are inevitably lots of redundant test cases, which significantly increase the cost. Previous redundancy detection approaches typically treat the textual descriptions as a whole to compare their similarity and suffer from low precision. Our observation reveals that a test case can have explicit test-oriented entities, such as tested function Components, Constraints, etc; and there are also specific relations between these entities. This inspires us with a potential opportunity for accurate redundancy detection. In this paper, we first define five test-oriented entity categories and four associated relation categories and re-formulate the NL test case redundancy detection problem as the comparison of detailed testing content guided by the test-oriented entities and relations. Following that, we propose Tscope, a fine-grained approach for redundant NL test case detection by dissecting test cases into atomic test tuple(s) with the entities restricted by associated relations. To serve as the test case dissection, Tscope designs a context-aware model for the automatic entity and relation extraction. Evaluation on 3,467 test cases from ten projects shows Tscope could achieve 91.8% precision, 74.8% recall, and 82.4% F1, significantly outperforming state-of-the-art approaches and commonly-used classifiers. This new formulation of the NL test case redundant detection problem can motivate the follow-up studies to further improve this task and other related tasks involving NL descriptions.

cs.SE↗

arXiv2022

Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling

This paper proposes a novel stroke-based rendering (SBR) method that translates images into vivid oil paintings. Previous SBR techniques usually formulate the oil painting problem as pixel-wise approximation. Different from this technique route, we treat oil painting creation as an adaptive sampling problem. Firstly, we compute a probability density map based on the texture complexity of the input image. Then we use the Voronoi algorithm to sample a set of pixels as the stroke anchors. Next, we search and generate an individual oil stroke at each anchor. Finally, we place all the strokes on the canvas to obtain the oil painting. By adjusting the hyper-parameter maximum sampling probability, we can control the oil painting fineness in a linear manner. Comparison with existing state-of-the-art oil painting techniques shows that our results have higher fidelity and more realistic textures. A user opinion test demonstrates that people behave more preference toward our oil paintings than the results of other methods. More interesting results and the code are in https://github.com/TZYSJTU/Im2Oil.

cs.CV↗

arXiv2022

Insulator-metal-superconductor transition in medium-entropy van der Waals compound MEPSe3 (ME=Fe, Mn, Cd, and In) under high pressures

MPX3 (M=metals, X=S or Se) represents a large family of van der Waals (vdW) materials featuring with P-P dimers of ~2.3 Å separation. Its electrical transport property and structure can hardly be tuned by the intentional chemical doping and ionic intercalation. Here, we employ an entropy-enhancement strategy to successfully obtain a series of medium-entropy compounds MEPSe3 (ME=Fe, Mn, Cd and In), in which the electrical and magnetic properties changed simultaneously. Lone-pair electrons of P emerge due to the dissociation of the dimers as evidenced by a 35% elongation in the P-P interatomic distance. The band gap widens from 0.1 eV to 0.7 eV by this dissociation. Under external physical pressure up to ~50 GPa, a giant collapse of up to 15% in the c-axis happens, which is in contrast to the in-plane shrinkage of their counterparts Fe/MnPSe3. It leads to the recombination of P3- with lone pair electrons into a P-P dimer and the smallest bulk modulus of 28 GPa in MPX3. The MEPSe3 transits from a spin-glass insulator to metal, and to superconductor, which is rarely observed in the MPX3. Our findings highlight the P-P dimer as an indicator to probe diverse electronic structure and the effectiveness of entropy-enhancement in materials science.

cond-mat.supr-con↗

arXiv2022

Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss

This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct a dynamic masking strategy based on knowledge in Masked Language Modeling (MLM) with whole word masking. Furthermore, by observing the specific structure of the dataset, the pre-trained Erlangshen applies propensity-corrected loss (PCL) in the fine-tuning phase. Overall, we achieve 72.54 points in F1 Score and 78.90 points in Accuracy on the test set. Our code is publicly available at: https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/hf-ds/fengshen/examples/clue_sim.

cs.CL↗