arXiv subjects

Xinxin Liu

Publications and source records attributed to Xinxin Liu.

25 records · Page 2Linked to original sources

arXiv2024

Image Reflection on Process Graphs -- A Novel Approach for the Completeness of an Axiomatization of 1-Free Regular Expressions Modulo Bisimilarity

We analyze a phenomenon called ``image reflection'' on a type of characterization graphs -- LLEE charts -- of 1-free regular expressions. Due to the correspondence between 1-free regular expressions and the provable solutions of LEE/LLEE charts, this observation naturally leads to a new proof for the completeness of the proof system \MilIfree\ for 1-free regular expressions modulo bisimulation equivalence. The critical part of the previous proof is to show that bisimulation collapse, which plays the role in linking the provable solutions of two LLEE charts, is still an LLEE chart. The difference of our proof, compared to the previous one, is that we do not rely on the graph transformations from LLEE charts into their bisimulation collapses by merging two carefully-selected bisimilar nodes in each transformation step. Instead, we directly show that the bisimulation collapse of an LLEE chart possesses an LEE/LLEE structure based on its set of images mapped through the bisimulation function from the LLEE chart, and the constrained relation between the images and their so-called ``well-structured'' looping-back charts pre-images on the LLEE chart. Our approach provides a novel angle to look at this problem and related problems, and can also be used for simplifying the graph transformations in the proof of the completeness problem of the proof system \Mil\ for regular expressions modulo bisimulation equivalence, which had remained open until very recently.

cs.LO↗

arXiv2022

A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling

Document-level relation extraction (RE) aims to identify relations between entities across multiple sentences. Most previous methods focused on document-level RE under full supervision. However, in real-world scenario, it is expensive and difficult to completely label all relations in a document because the number of entity pairs in document-level RE grows quadratically with the number of entities. To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning framework - shift and squared ranking loss positive-unlabeled (SSR-PU) learning. We use positive-unlabeled (PU) learning on document-level RE for the first time. Considering that labeled data of a dataset may lead to prior shift of unlabeled data, we introduce a PU learning under prior shift of training data. Also, using none-class score as an adaptive threshold, we propose squared ranking loss and prove its Bayesian consistency with multi-label ranking metrics. Extensive experiments demonstrate that our method achieves an improvement of about 14 F1 points relative to the previous baseline with incomplete labeling. In addition, it outperforms previous state-of-the-art results under both fully supervised and extremely unlabeled settings as well.

cs.CL↗

arXiv2021

Consistent RDMA-Friendly Hashing on Remote Persistent Memory

Coalescing RDMA and Persistent Memory (PM) delivers high end-to-end performance for networked storage systems, which requires rethinking the design of efficient hash structures. In general, existing hashing schemes separately optimize RDMA and PM, thus partially addressing the problems of RDMA Access Amplification and High-Overhead PM Consistency. In order to address these problems, we propose a continuity hashing, which is a "one-stone-two-birds" design to optimize both RDMA and PM. The continuity hashing leverages a fine-grained contiguous shared region, called SBuckets, to provide standby positions for the neighbouring two buckets in case of hash collisions. In the continuity hashing, remote read only needs a single RDMA read to directly fetch the home bucket and the neighbouring SBuckets, which contain all the positions of maintaining a key-value item, thus alleviating RDMA access amplification. Continuity hashing further leverages indicators that can be atomically modified to support log-free PM consistency for all the write operations. Evaluation results demonstrate that compared with state-of-the-art schemes, continuity hashing achieves high throughput (i.e., 1.45X -- 2.43X improvement), low latency (about 1.7X speedup) and the smallest number of PM writes with various workloads, while has acceptable load factors of about 70%.

cs.DC↗

arXiv2021

A Complete Axiomatisation for Divergence Preserving Branching Congruence of Finite-State Behaviours

We present an equational inference system for finite-state expressions, and prove that the system is sound and complete with respect to divergence preserving branching congruence, closing a problem that has been open since 1993. The inference system refines Rob van Glabbeek's simple and elegant complete axiomatisation for branching bisimulation congruence of finite-state behaviours by joining four simple axioms after dropping one axiom which is unsound under the more refined divergence sensitive semantics.

cs.LO↗

arXiv2019

Write-Optimized and Consistent RDMA-based NVM Systems

In order to deliver high performance in cloud computing, we generally exploit and leverage RDMA (Remote Direct Memory Access) in networking and NVM (Non-Volatile Memory) in end systems. Due to no involvement of CPU, one-sided RDMA becomes efficient to access the remote memory, and NVM technologies have the strengths of non-volatility, byte-addressability and DRAM-like latency. In order to achieve end-to-end high performance, many efforts aim to synergize one-sided RDMA and NVM. Due to the need to guarantee Remote Data Atomicity (RDA), we have to consume extra network round-trips, remote CPU participation and double NVM writes. In order to address these problems, we propose a zero-copy log-structured memory design for Efficient Remote Data Atomicity, called Erda. In Erda, clients directly transfer data to the destination address at servers via one-sided RDMA writes without redundant copy and remote CPU consumption. To detect the incompleteness of fetched data, we verify a checksum without client-server coordination. We further ensure metadata consistency by leveraging an 8-byte atomic update in the hash table, which also contains the address information for the stale data. When a failure occurs, the server properly restores to a consistent version. Experimental results show that compared with Redo Logging (a CPU involvement scheme) and Read After Write (a network dominant scheme), Erda reduces NVM writes approximately by 50%, as well as significantly improves throughput and decreases latency.

cs.DC↗

arXiv2019

Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

cs.CV↗

arXiv2018

Oblique Stripe Removal in Remote Sensing Images via Oriented Variation

Destriping is a classical problem in remote sensing image processing. Although considerable effort has been made to remove stripes, few of the existing methods can eliminate stripe noise with arbitrary orientations. This situation makes the removal of oblique stripes in the higher-level remote sensing products become an unfinished and urgent issue. To overcome the challenging problem, we propose a novel destriping model which is self-adjusted to different orientations of stripe noise. First of all, the oriented variation model is designed to accomplish the stripe orientation approximation. In this model, the stripe direction is automatically estimated and then imbedded into the constraint term to depict the along-stripe smoothness of the stripe component. Mainly based on the oriented variation model, a whole destriping framework is proposed by jointly employing an L1-norm constraint and a TV regularization to separately capture the global distribution property of stripe component and the piecewise smoothness of the clean image. The qualitative and quantitative experimental results of both orientation and destriping aspects confirm the effectiveness and stability of the proposed method.

cs.CV↗