arXiv subjects

Li Li

Publications and source records attributed to Li Li.

At least 613 records · Page 34Linked to original sources

arXiv2020

Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in predicting patch correctness on a deduplicated dataset of 1000 labeled patches. Our study shows that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature.

cs.SE↗

arXiv2020

Anchor: Locating Android Framework-specific Crashing Faults

Android framework-specific app crashes are hard to debug. Indeed, the callback-based event-driven mechanism of Android challenges crash localization techniques that are developed for traditional Java programs. The key challenge stems from the fact that the buggy code location may not even be listed within the stack trace. For example, our empirical study on 500 framework-specific crashes from an open benchmark has revealed that 37 percent of the crash types are related to bugs that are outside the stack traces. Moreover, Android programs are a mixture of code and extra-code artifacts such as the Manifest file. The fact that any artifact can lead to failures in the app execution creates the need to position the localization target beyond the code realm. In this paper, we propose Anchor, a two-phase suspicious bug location suggestion tool. Anchor specializes in finding crash-inducing bugs outside the stack trace. Anchor is lightweight and source code independent since it only requires the crash message and the apk file to locate the fault. Experimental results, collected via cross-validation and in-the-wild dataset evaluation, show that Anchor is effective in locating Android framework-specific crashing faults.

cs.SE↗

arXiv2020

Decomposition of the Total Effect for Two Mediators: A Natural Counterfactual Interaction Effect Framework

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total causal effect of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where the two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural counterfactual interaction effect that captures the two-way and three-way interactions for both scenarios that extend the two-way mediated interactions in literature. We develop a unified approach for decomposing the total effect into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we illustrate the proposed decomposition method using a real data analysis where the two mediators are causally sequential.

stat.ME↗

arXiv2020

Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control

The design of cooperative adaptive cruise control is critical in mixed traffic flow, where connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist. Compared with pure CAVs, the major challenge is how to handle the prediction uncertainty of HDVs, which can cause significant state deviation of CAVs from planned trajectories. In most existing studies, model predictive control (MPC) is utilized to replan CAVs' trajectories to mitigate the deviation at each time step. However, as the replan process is usually conducted by solving an optimization problem with information through inter-vehicular communication, MPC methods suffer from heavy computational and communicational burdens. To address this limitation, a robust platoon control framework is proposed based on tube MPC in this paper. The prediction uncertainty is dynamically mitigated by the feedback control and restricted inside a set with a high probability. When the uncertainty exceeds the set or additional external disturbance emerges, the feedforward control is triggered to plan a ``tube'' (a sequence of the set), which can bound CAVs' actual trajectories. As the replan process is usually not required, the proposed method is much more efficient regarding computation and communication, compared with the MPC method. Comprehensive simulations are provided to validate the effectiveness of the proposed framework.

eess.SY↗

arXiv2020

Active Crowd Counting with Limited Supervision

To learn a reliable people counter from crowd images, head center annotations are normally required. Annotating head centers is however a laborious and tedious process in dense crowds. In this paper, we present an active learning framework which enables accurate crowd counting with limited supervision: given a small labeling budget, instead of randomly selecting images to annotate, we first introduce an active labeling strategy to annotate the most informative images in the dataset and learn the counting model upon them. The process is repeated such that in every cycle we select the samples that are diverse in crowd density and dissimilar to previous selections. In the last cycle when the labeling budget is met, the large amount of unlabeled data are also utilized: a distribution classifier is introduced to align the labeled data with unlabeled data; furthermore, we propose to mix up the distribution labels and latent representations of data in the network to particularly improve the distribution alignment in-between training samples. We follow the popular density estimation pipeline for crowd counting. Extensive experiments are conducted on standard benchmarks i.e. ShanghaiTech, UCF CC 50, MAll, TRANCOS, and DCC. By annotating limited number of images (e.g. 10% of the dataset), our method reaches levels of performance not far from the state of the art which utilize full annotations of the dataset.

cs.CV↗

arXiv2020

Evaluating Incidence and Impact Estimates of the COVID-19 Outbreak from Wuhan before Lockdown

Background: Wuhan, China was the epicenter of COVID-19 pandemic. The goal of current study is to understand the infection transmission dynamics before intervention measures were taken. Methods: Data and key events were searched through pubmed and internet. Epidemiological data were calculated using data extracted from a variety of data sources. Results: We established a timeline showing by January 1, 2020, Chinese authorities had been presented convincing evidence of human-to-human transmission; however, it was not until January 20, 2020 that this information was shared with the public. Our study estimated that there would have been 10989 total infected cases if interventions were taken on January 2, 2020, versus 239875 cases when lockdown was put in place on January 23, 2020. Conclusions: China's withholding of key information about the 2020 COVID-19 outbreak and its delayed response ultimately led to the largest public health crisis of this century and could have been avoided with earlier countermeasures.

q-bio.PE↗

arXiv2020

Software tools for quantum control: Improving quantum computer performance through noise and error suppression

Manipulating quantum computing hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise limits the performance and capabilities of noisy intermediate-scale quantum (NISQ) devices, as well as any future quantum computing technologies. Fortunately quantum control enables efficient execution of quantum logic operations and algorithms with built-in robustness to errors, without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in quantum computing research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the quantum computing software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of central product features including a flexible optimization toolkit, filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions using these tools in real experimental settings for both trapped-ion and superconducting quantum computer hardware.

quant-ph↗

arXiv2020

Phase-Matching Quantum Cryptographic Conferencing

Quantum cryptographic conferencing (QCC) holds promise for distributing information-theoretic secure keys among multiple users over long distance. Limited by the fragility of Greenberger-Horne-Zeilinger (GHZ) state, QCC networks based on directly distributing GHZ states at long distance still face big challenge. Another two potential approaches are measurement device independent QCC and conference key agreement with single-photon interference, which was proposed based on the post-selection of GHZ states and the post-selection of W state, respectively. However, implementations of the former protocol are still heavily constrained by the transmission rate $η$ of optical channels and the complexity of the setups for post-selecting GHZ states. Meanwhile, the latter protocol cannot be cast to a measurement device independent prepare-and-measure scheme. Combining the idea of post-selecting GHZ state and recently proposed twin-field quantum key distribution protocols, we report a QCC protocol based on weak coherent state interferences named phase-matching quantum cryptographic conferencing, which is immune to all detector side-channel attacks. The proposed protocol can improve the key generation rate from $\mathrm{O}(η^N)$ to $\mathrm{O}(η^{N-1})$ compared with the measurement device independent QCC protocols. Meanwhile, it can be easily scaled up to multiple parties due to its simple setup.

quant-ph↗

arXiv2020

A New Modal Autoencoder for Functionally Independent Feature Extraction

Autoencoders have been widely used for dimensional reduction and feature extraction. Various types of autoencoders have been proposed by introducing regularization terms. Most of these regularizations improve representation learning by constraining the weights in the encoder part, which maps input into hidden nodes and affects the generation of features. In this study, we show that a constraint to the decoder can also significantly improve its performance because the decoder determines how the latent variables contribute to the reconstruction of input. Inspired by the structural modal analysis method in mechanical engineering, a new modal autoencoder (MAE) is proposed by othogonalising the columns of the readout weight matrix. The new regularization helps to disentangle explanatory factors of variation and forces the MAE to extract fundamental modes in data. The learned representations are functionally independent in the reconstruction of input and perform better in consecutive classification tasks. The results were validated on the MNIST variations and USPS classification benchmark suite. Comparative experiments clearly show that the new algorithm has a surprising advantage. The new MAE introduces a very simple training principle for autoencoders and could be promising for the pre-training of deep neural networks.

cs.LG↗

arXiv2020

A User-Based Charge and Subsidy Scheme for Single O-D Network Mobility Management

We propose a path guidance system with a user-based charge and subsidy (UBCS) scheme for single O-D network mobility management. Users who are willing to join the scheme (subscribers) can submit travel requests along with their VOTs to the system before traveling. Those who are not willing to join (outsiders) only need to submit travel requests to the system. Our system will give all users path guidance from their origins to their destinations, and collect a \emph{path payment} from the UBCS subscribers. Subscribers will be charged or subsided in a way that renders the UBCS strategy-proof, revenue-neutral, and Pareto-improving. A numerical example shows that the UBCS scheme is equitable and progressive.

eess.SY↗

arXiv2020

CL-ADMM: A Cooperative Learning Based Optimization Framework for Resource Management in MEC

We consider the problem of intelligent and efficient resource management framework in mobile edge computing (MEC), which can reduce delay and energy consumption, featuring distributed optimization and efficient congestion avoidance mechanism. In this paper, we present a Cooperative Learning framework for resource management in MEC from an Alternating Direction Method of Multipliers (ADMM) perspective, called CL-ADMM framework. First, in order to caching task efficiently in a group, a novel task popularity estimating scheme is proposed, which is based on semi-Markov process model, then a greedy task cooperative caching mechanism has been established, which can effectively reduce delay and energy consumption. Secondly, for addressing group congestion, a dynamic task migration scheme based on cooperative improved Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Thirdly, for minimizing delay and energy consumption for resources allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM based scheme to address this problem, which can reduce the complexity of problem with a new set of auxiliary variables, these sub-problems are all convex problems, and can be solved by using a primal-dual approach, guaranteeing its convergences. Then we prove that the convergence by using Lyapunov theory. Numerical results demonstrate the effectiveness of the CL-ADMM and it can effectively reduce delay and energy consumption for MEC.

cs.NI↗

arXiv2020

Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19

Given the existing COVID-19 pandemic worldwide, it is critical to systematically study the interactions between hosts and coronaviruses including SARS-Cov, MERS-Cov, and SARS-CoV-2 (cause of COVID-19). We first created four host-pathogen interaction (HPI)-Outcome postulates, and generated a HPI-Outcome model as the basis for understanding host-coronavirus interactions (HCI) and their relations with the disease outcomes. We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes. Accordingly, we annotated and categorized different coronaviruses, hosts, and phenotypes using ontologies and identified their relations. Various COVID-19 phenotypes are hypothesized to be caused by the backend HCI mechanisms. To further identify the causal HCI-outcome relations, we collected 35 experimentally-verified HCI protein-protein interactions (PPIs), and applied literature mining to identify additional host PPIs in response to coronavirus infections. The results were formulated in a logical ontology representation for integrative HCI-outcome understanding. Using known PPIs as baits, we also developed and applied a domain-inferred prediction method to predict new PPIs and identified their pathological targets on multiple organs. Overall, our proposed ontology-based integrative framework combined with computational predictions can be used to support fundamental understanding of the intricate interactions between human patients and coronaviruses (including SARS-CoV-2) and their association with various disease outcomes.

q-bio.OT↗

arXiv2020

Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.

cs.CV↗

arXiv2020

Magnetophonons & type-B Goldstones from Hydrodynamics to Holography

We perform a detailed analysis of a large class of effective holographic models with broken translations at finite charge density and magnetic field. We exhaustively discuss the dispersion relations of the hydrodynamic modes at zero magnetic field and successfully match them to the predictions from charged hydrodynamics. At finite magnetic field, we identify the presence of an expected type-B Goldstone boson $\mathrm{Re}[ω]\sim k^2$, known as magnetophonon and its gapped partner -- the magnetoplasmon. We discuss their properties in relation to the effective field theory and hydrodynamics expectations. Finally, we compute the optical conductivities and the quasinormal modes at finite magnetic field. We observe that the pinning frequency of the magneto-resonance peak increases with the magnetic field, in agreement with experimental data on certain 2D materials, revealing the quantum nature of the holographic pinning mechanism.

hep-th↗

arXiv2020

Decomposition of Total Effect with the Notion of Natural Counterfactual Interaction Effect

Mediation analysis serves as a crucial tool to obtain causal inference based on directed acyclic graphs, which has been widely employed in the areas of biomedical science, social science, epidemiology and psychology. Decomposition of total effect provides a deep insight to fully understand the casual contribution from each path and interaction term. Since the four-way decomposition method was proposed to identify the mediated interaction effect in counterfactual framework, the idea had been extended to a more sophisticated scenario with non-sequential multiple mediators. However, the method exhibits limitations as the causal structure contains direct causal edges between mediators, such as inappropriate modeling of dependence and non-identifiability. We develop the notion of natural counterfactual interaction effect and find that the decomposition of total effect can be consistently realized with our proposed notion. Furthermore, natural counterfactual interaction effect overcomes the drawbacks and possesses a clear and significant interpretation, which may largely improve the capacity of researchers to analyze highly complex causal structures.

stat.ME↗

arXiv2020

Quantum Optimization with a Novel Gibbs Objective Function and Ansatz Architecture Search

The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a prescription for choosing the variational parameters of the circuit. We propose modifications to both. First, we define the Gibbs objective function and show that it is superior to the energy expectation value for use as an objective function in tuning the variational parameters. Second, we describe an Ansatz Architecture Search (AAS) algorithm for searching the discrete space of quantum circuit architectures near the QAOA to find a better ansatz. Applying these modifications for a complete graph Ising model results in a $244.7\%$ median relative improvement in the probability of finding a low-energy state while using $33.3\%$ fewer two-qubit gates. For Ising models on a 2d grid we similarly find $44.4\%$ median improvement in the probability with a $20.8\%$ reduction in the number of two-qubit gates. This opens a new research field of quantum circuit architecture design for quantum optimization algorithms.

quant-ph↗

arXiv2020

Vanishing Point Guided Natural Image Stitching

Recently, works on improving the naturalness of stitching images gain more and more extensive attention. Previous methods suffer the failures of severe projective distortion and unnatural rotation, especially when the number of involved images is large or images cover a very wide field of view. In this paper, we propose a novel natural image stitching method, which takes into account the guidance of vanishing points to tackle the mentioned failures. Inspired by a vital observation that mutually orthogonal vanishing points in Manhattan world can provide really useful orientation clues, we design a scheme to effectively estimate prior of image similarity. Given such estimated prior as global similarity constraints, we feed it into a popular mesh deformation framework to achieve impressive natural stitching performances. Compared with other existing methods, including APAP, SPHP, AANAP, and GSP, our method achieves state-of-the-art performance in both quantitative and qualitative experiments on natural image stitching.

cs.CV↗

arXiv2020

The Calderón Problem For The Fractional Magnetic Operator

We introduce the fractional magnetic operator involving a magnetic potential and an electric potential. We formulate an inverse problem for the fractional magnetic operator. We determine the electric potential from the exterior partial measurements of the associated Dirichlet-to-Neumann map by using Runge approximation property.

math.AP↗