arXiv subjects

Lu Zhang

Publications and source records attributed to Lu Zhang.

At least 397 records · Page 22Linked to original sources

arXiv2020

ModeNet: Mode Selection Network For Learned Video Coding

In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression. Inspired by traditional video coding, ModeNet purpose is to enable competition among several coding modes. The proposed ModeNet learns and conveys a pixel-wise partitioning of the frame, used to assign each pixel to the most suited coding mode. ModeNet is trained alongside the different coding modes to minimize a rate-distortion cost. It is a flexible component which can be generalized to other systems to allow competition between different coding tools. Mod-eNet interest is studied on a P-frame coding task, where it is used to design a method for coding a frame given its prediction. ModeNet-based systems achieve compelling performance when evaluated under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding track conditions.

cs.NE↗

arXiv2020

A Fixation-based 360° Benchmark Dataset for Salient Object Detection

Fixation prediction (FP) in panoramic contents has been widely investigated along with the booming trend of virtual reality (VR) applications. However, another issue within the field of visual saliency, salient object detection (SOD), has been seldom explored in 360° (or omnidirectional) images due to the lack of datasets representative of real scenes with pixel-level annotations. Toward this end, we collect 107 equirectangular panoramas with challenging scenes and multiple object classes. Based on the consistency between FP and explicit saliency judgements, we further manually annotate 1,165 salient objects over the collected images with precise masks under the guidance of real human eye fixation maps. Six state-of-the-art SOD models are then benchmarked on the proposed fixation-based 360° image dataset (F-360iSOD), by applying a multiple cubic projection-based fine-tuning method. Experimental results show a limitation of the current methods when used for SOD in panoramic images, which indicates the proposed dataset is challenging. Key issues for 360° SOD is also discussed. The proposed dataset is available at https://github.com/PanoAsh/F-360iSOD.

cs.CV↗

arXiv2020

Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e.g., tracking noise and prediction errors, etc.). Although the partially observable Markov decision process (POMDP) provides a systematic way to incorporate these uncertainties, it quickly becomes computationally intractable when scaled to the real-world large-size problem. In this paper, we present an efficient uncertainty-aware decision-making (EUDM) framework, which generates long-term lateral and longitudinal behaviors in complex driving environments in real-time. The computation complexity is controlled to an appropriate level by two novel techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree) structure and conditional focused branching (CFB) mechanism. The key idea is utilizing domain-specific expert knowledge to guide the branching in both action and intention space. The proposed framework is validated using both onboard sensing data captured by a real vehicle and an interactive multi-agent simulation platform. We also release the code of our framework to accommodate benchmarking.

cs.RO↗

arXiv2020

Binary Probability Model for Learning Based Image Compression

In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables. Previous works model the latents with a Gaussian or a Laplace distribution. Inspired by binary arithmetic coding , we propose to signal the latents with three binary values and one integer, with different probability models. A relaxation method is designed to perform gradient-based training. The richer probability model results in a better entropy coding leading to lower rate. Experiments under the Challenge on Learned Image Compression (CLIC) test conditions demonstrate that this method achieves 18% rate saving compared to Gaussian or Laplace models.

eess.IV↗

arXiv2020

Bi-parameter trilinear Fourier multipliers and pseudo-differential operators with flag symbols

The main purpose of this paper is to study $L^r$ Hölder type estimates for a bi-parameter trilinear Fourier multiplier with flag singularity, and the analogous pseudo-differential operator, when the symbols are in a certain product form. More precisely, for $f,g,h\in \mathcal{S}(\mathbb{R}^{2})$, the bi-parameter trilinear flag Fourier multiplier operators we consider are defined by $$ T_{m_1,m_2}(f,g,h)(x):=\int_{\mathbb{R}^{6}}m_1(ξ,η,ζ)m_2(η,ζ)\hat f(ξ) \hat g(η)\hat h(ζ)e^{2πi(ξ+η+ζ)\cdot x}dξdηdζ, $$ when $m_1,m_2$ are two bi-parameter symbols. We will show that our problem can be reduced to establish the $L^r$ estimate for the special multiplier $m_1(ξ_1, η_1, ζ_1) m_2(η_2, ζ_2)$ (see Theorem 1.7). We also study these $L^r$ estimates for the corresponding bi-parameter trilinear pseudo-differential operators defined by $$ T_{ab}(f,g,h)(x):=\int_{\mathbb{R}^6}a(x,ξ,η,ζ)b(x,η,ζ)\hat f(ξ)\hat g(η)\hat h(ζ)e^{2πi x(ξ+η+ζ)}dξdηdζ, $$ where the smooth symbols $a,b$ satisfy certain bi-parameter Hörmander conditions. We will also show that the $L^r$ estimate holds for $T_{ab}$ as long as the $L^r$ estimate for the flag multiplier operator holds when the multiplier has the special form $m_1(ξ_1, η_1, ζ_1) m_2(η_2, ζ_2)$ (see Theorem 1.10). The bi-parameter and trilinear flag Fourier multipliers considered in this paper do not satisfy the conditions of the classical bi-parameter trilinear Fourier multipliers considered by Muscalu, Tao, Thiele and the second author [21, 22]. They may also be viewed as the bi-parameter trilinear variants of estimates obtained for the one-parameter flag paraproducts by Muscalu [18].

math.CA↗

arXiv2020

Modeling Programs Hierarchically with Stack-Augmented LSTM

Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are initially designed for natural languages, have been generally used for modeling programming languages. However, different from natural languages, programming languages contain explicit and hierarchical structure that is hard to learn by traditional statistical language models. To address this challenge, we propose a novel Stack-Augmented LSTM neural network for programming language modeling. Adding a stack memory component into the LSTM network enables our model to capture the hierarchical information of programs through the PUSH and POP operations, which further allows our model capturing the long-term dependency in the programs. We evaluate the proposed model on three program analysis tasks, i.e., code completion, program classification, and code summarization. Evaluation results show that our proposed model outperforms baseline models in all the three tasks, indicating that by capturing the structural information of programs with a stack, our proposed model can represent programs more precisely.

cs.SE↗

arXiv2020

Medusa: Blockchain Powered Log Storage System

Blockchain is one of the most heavily invested technologies in recent years. Due to its tamper-proof and decentralization properties, blockchain has become an ideal utility for data storage that is applicable in many real world industrial scenarios. One important scenario is web log, which is treated as sources of technical significance and commercial revenues in major internet companies. In this paper, we illustrate our design of a web log storage system based on HyperLedger. HyperLedger yields higher throughput and lower latency compared with other blockchain systems. Alongside its efficiency advantages, HyperLeger is a permissioned blockchain, which is an ideal fit for enterprise software design scenario.

cs.CR↗

arXiv2020

An energy-based discontinuous Galerkin method for semilinear wave equations

We generalize the energy-based discontinuous Galerkin method proposed in [SIAM J. Num. Anal., 53(6):2705-2726, 2015.] to second-order semilinear wave equations. A stability and convergence analysis is presented along with numerical experiments demonstrating optimal convergence for certain choices of the interelement fluxes. Applications to the sine-Gordon equation include simulations of breathers, kink, and anti-kink solitons.

math.NA↗

arXiv2020

A Study of Bug Resolution Characteristics in Popular Programming Languages

This paper presents a large-scale study that investigates the bug resolution characteristics among popular Github projects written in different programming languages. We explore correlations but, of course, we cannot infer causation. Specifically, we analyse bug resolution data from approximately 70 million Source Line of Code, drawn from 3 million commits to 600 GitHub projects, primarily written in 10 programming languages. We find notable variations in apparent bug resolution time and patch (fix) size. While interpretation of results from such large-scale empirical studies is inherently difficult, we believe that the differences in medians are sufficiently large to warrant further investigation, replication, re-analysis and follow up research. For example, in our corpus, the median apparent bug resolution time (elapsed time from raise to resolve) for Ruby was 4X that for Go and 2.5X for Java. We also found that patches tend to touch more files for the corpus of strongly typed and for statically typed programs. However, we also found evidence for a lower elapsed resolution time for bug resolution committed to projects constructed from statically typed languages. These findings, if replicated in subsequent follow on studies, may shed further empirical light on the debate about the importance of static typing.

cs.SE↗

arXiv2019

Automatic Testing and Improvement of Machine Translation

This paper presents TransRepair, a fully automatic approach for testing and repairing the consistency of machine translation systems. TransRepair combines mutation with metamorphic testing to detect inconsistency bugs (without access to human oracles). It then adopts probability-reference or cross-reference to post-process the translations, in a grey-box or black-box manner, to repair the inconsistencies. Our evaluation on two state-of-the-art translators, Google Translate and Transformer, indicates that TransRepair has a high precision (99%) on generating input pairs with consistent translations. With these tests, using automatic consistency metrics and manual assessment, we find that Google Translate and Transformer have approximately 36% and 40% inconsistency bugs. Black-box repair fixes 28% and 19% bugs on average for Google Translate and Transformer. Grey-box repair fixes 30% bugs on average for Transformer. Manual inspection indicates that the translations repaired by our approach improve consistency in 87% of cases (degrading it in 2%), and that our repairs have better translation acceptability in 27% of the cases (worse in 8%).

cs.SE↗

arXiv2019

Epitaxial growth and antiferromagnetism of Sn-substituted perovskite iridate SrIr$_{0.8}$Sn$_{0.2}$O$_3$

5d iridates have shown vast emergent phenomena due to a strong interplay among its lattice, charge and spin degrees of freedom, because of which the potential in spintronic application of the thin-film form is highly leveraged. Here we have epitaxially stabilized perovskite SrIr$_{0.8}$Sn$_{0.2}$O$_3$ on [001] SrTiO$_3$ substrates through pulsed laser deposition and systematically characterized the structural, electronic and magnetic properties. Physical properties measurements unravel an insulating ground state with a weak ferromagnetism in the compressively strained epitaxial film. The octahedral rotation pattern is identified by synchrotron x-ray diffraction, resolving a mix of $a^+b^-c^-$ and $a^-b^+c^-$ domains. X-ray magnetic resonant scattering directly demonstrates a G-type antiferromagnetic structure of the magnetic order and the spin canting nature of the weak ferromagnetism.

cond-mat.mtrl-sci↗

arXiv2019

TreeGen: A Tree-Based Transformer Architecture for Code Generation

A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model.

cs.LG↗

arXiv2019

Fairness through Equality of Effort

Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as demographic disparity, mistreatment disparity, calibration, causal-based measures such as total effect, direct and indirect discrimination, and counterfactual fairness, and fairness notions such as equality of opportunity and equal odds that consider both decisions in the training data and decisions made by predictive models. In this paper, we develop a new causal-based fairness notation, called equality of effort. Different from existing fairness notions which mainly focus on discovering the disparity of decisions between two groups of individuals, the proposed equality of effort notation helps answer questions like to what extend a legitimate variable should change to make a particular individual achieve a certain outcome level and addresses the concerns whether the efforts made to achieve the same outcome level for individuals from the protected group and that from the unprotected group are different. We develop algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort. We also develop an optimization-based method for removing discriminatory effects from the data if discrimination is detected. We conduct empirical evaluations to compare the equality of effort and existing fairness notion and show the effectiveness of our proposed algorithms.

cs.CY↗

arXiv2019

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.

cs.LG↗

arXiv2019

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at https://github.com/luzhang16/AR-CNN.

cs.CV↗

arXiv2019

Can Automated Program Repair Refine Fault Localization?

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, we observe that in the literature their only connection is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we explore their connection in the other direction, i.e., can program repair in turn help with fault localization? In this way,we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed).We have designed ProFL, a simplistic approach using patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning.

cs.SE↗

arXiv2019

Basic Performance Measurements of the Intel Optane DC Persistent Memory Module

Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system.

cs.DC↗

arXiv2019

Cartilage-binding antibodies induce pain through immune complex-mediated activation of neurons

Rheumatoid arthritis-associated joint pain is frequently observed independent of disease activity, suggesting unidentified pain mechanisms. We demonstrate that antibodies binding to cartilage, specific for collagen type II (CII) or cartilage oligomeric matrix protein (COMP), elicit mechanical hypersensitivity in mice, uncoupled from visual, histological and molecular indications of inflammation. Cartilage antibody-induced pain-like behavior does not depend on complement activation or joint inflammation, but instead on tissue antigen recognition and local immune complex (IC) formation. smFISH and IHC suggest that neuronal Fcgr1 and Fcgr2b mRNA are transported to peripheral ends of primary afferents. CII-ICs directly activate cultured WT but not FcRγ chain-deficient DRG neurons. In line with this observation, CII-IC does not induce mechanical hypersensitivity in FcRγ chain-deficient mice. Furthermore, injection of CII antibodies does not generate pain-like behavior in FcRγ chain-deficient mice or mice lacking activating FcγRs in neurons. In summary, this study defines functional coupling between autoantibodies and pain transmission that may facilitate the development of new disease-relevant pain therapeutics.

q-bio.NC↗