arXiv subjects

Jingyuan Zhang

Publications and source records attributed to Jingyuan Zhang.

89 records · Page 5Linked to original sources

arXiv2023

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. However, previous works have only achieved limited success by unifying a few tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), which fall short of being authentic UIE models particularly when extracting other general schemas such as quadruples and quintuples. Additionally, these models used an implicit structural schema instructor, which could lead to incorrect links between types, hindering the model's generalization and performance in low-resource scenarios. In this paper, we redefine the authentic UIE with a formal formulation that encompasses almost all extraction schemas. To the best of our knowledge, we are the first to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE. To avoid interference between different types, we reset the position ids and attention mask matrices. RexUIE shows strong performance under both full-shot and few-shot settings and achieves State-of-the-Art results on the tasks of extracting complex schemas.

cs.CL↗

arXiv2023

On the edge reconstruction of the characteristic and permanental polynomials of a simple graph

As a variant of the Ulam's vertex reconstruction conjecture and the Harary's edge reconstruction conjecture, Cvetković and Schwenk posed independently the following problem: Can the characteristic polynomial of a simple graph $G$ with vertex set $V$ be reconstructed from the characteristic polynomials of all subgraphs in $\{G-v|v\in V\}$ for $|V|\geq 3$? This problem is still open. A natural problem is: Can the characteristic polynomial of a simple graph $G$ with edge set $E$ be reconstructed from the characteristic polynomials of all subgraphs in $\{G-e|e\in E\}$? In this paper, we prove that if $|V|\neq |E|$, then the characteristic polynomial of $G$ can be reconstructed from the characteristic polynomials of all subgraphs in $\{G-uv, G-u-v|uv\in E\}$, and the similar result holds for the permanental polynomial of $G$. We also prove that the Laplacian (resp. signless Laplacian) characteristic polynomial of $G$ can be reconstructed from the Laplacian (resp. signless Laplacian) characteristic polynomials of all subgraphs in $\{G-e|e\in E\}$ (resp. if $|V|\neq |E|$).

math.CO↗

arXiv2023

PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios. Secondly, existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem. Additionally, they are difficult to apply in situations where unseen semantic entity categories emerge. To address the first challenge, we propose a new large-scale human-annotated dataset named Complex Layout form for key information EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity categories. To solve the second challenge, we introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios. PPN leverages the implicit clues between semantic entities to assist extracting, and its parallel extraction mechanism allows it to extract multiple results simultaneously and efficiently. Experiments on the CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods while also offering a much faster inference speed.

cs.AI↗

arXiv2023

Counting spanning trees of (1, N)-periodic graphs

Let $N\geq 2$ be an integer, a (1, $N$)-periodic graph $G$ is a periodic graph whose vertices can be partitioned into two sets $V_1=\{v\midσ(v)=v\}$ and $V_2=\{v\midσ^i(v)\neq v\ \mbox{for any}\ 1<i<N\}$, where $σ$ is an automorphism with order $N$ of $G$. The subgraph of $G$ induced by $V_1$ is called a fixed subgraph. Yan and Zhang [Enumeration of spanning trees of graphs with rotational symmetry, J. Comb. Theory Ser. A, 118(2011): 1270-1290] studied the enumeration of spanning trees of a special type of (1, $N$)-periodic graphs with $V_1=\emptyset$ for any non-trivial automorphism with order $N$. In this paper, we obtain a concise formula for the number of spanning trees of (1, $N$)-periodic graphs. Our result can reduce to Yan and Zhang's when $V_1$ is empty. As applications, we give a new closed formula for the spanning tree generating function of cobweb lattices, and obtain formulae for the number of spanning trees of circulant graphs $C_n(s_1,s_2,\ldots,s_k)$ and $K_2\bigvee C_n(s_1,s_2,\ldots,s_k)$.

math-ph↗

arXiv2023

On the edge reconstruction of six digraph polynomials

Let $G=(V,E)$ be a digraph having no loops and no multiple arcs, with vertex set $V=\{v_1,v_2,\ldots,v_n\}$ and arc set $E=\{e_1,e_2,\ldots,e_m\}$. Denote the adjacency matrix and the vertex in-degree diagonal matrix of $G$ by $A=(a_{ij})_{n\times n}$ and $D=diag(d^+(v_1),d^+(v_2),\cdots,d^+(v_n))$, where $a_{ij}=1$ if $(v_i,v_j)\in E(G)$ and $a_{ij}=0$ otherwise, and $d^+(v_i)$ is the number of arcs with head $v_i$. Set $f_1(G;x)=\det(xI-A), f_2(G;x)=\det(xI-D+A),f_3(G;x)=\det(xI-D-A),f_4(G;x)={\rm per}(xI-A), f_5(G;x)={\rm per}(xI-D+A),f_6(G;x)={\rm per}(xI-D-A)$, where $\det(X)$ and ${\rm per}(X)$ denote the determinant and the permanent of a square matrix $X$, respectively. In this paper, we consider a variant of the Ulam's vertex reconstruction conjecture and the Harary's edge reconstruction conjecture, and prove that, for any $1\leq i\leq 6$, \begin{equation*} (m-n)f_i(G;x)+xf_i'(G;x)=\sum\limits_{e\in E}f_i(G-e;x), \end{equation*} which implies that if $m\neq n$, then $f_i(G;x)$ can be reconstructed from $\{f_i(G-e;x)|e\in E\}$.

math.CO↗

arXiv2023

InfiniStore: Elastic Serverless Cloud Storage

Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides limited elasticity. We present a new cloud storage service called ServerlessMemory, which stores data using the memory of serverless functions. ServerlessMemory employs a sliding-window-based memory management strategy inspired by the garbage collection mechanisms used in the programming language to effectively segregate hot/cold data and provides fine-grained elasticity, good performance, and a pay-per-access cost model with extremely low cost. We then design and implement InfiniStore, a persistent and elastic cloud storage system, which seamlessly couples the function-based ServerlessMemory layer with a persistent, inexpensive cloud object store layer. InfiniStore enables durability despite function failures using a fast parallel recovery scheme built on the autoscaling functionality of a FaaS (Function-as-a-Service) platform. We evaluate InfiniStore extensively using both microbenchmarking and two real-world applications. Results show that InfiniStore has more performance benefits for objects larger than 10 MB compared to AWS ElastiCache and Anna, and InfiniStore achieves 26.25% and 97.24% tenant-side cost reduction compared to InfiniCache and ElastiCache, respectively.

cs.DC↗

arXiv2023

Closed-Loop Transcription via Convolutional Sparse Coding

Autoencoding has achieved great empirical success as a framework for learning generative models for natural images. Autoencoders often use generic deep networks as the encoder or decoder, which are difficult to interpret, and the learned representations lack clear structure. In this work, we make the explicit assumption that the image distribution is generated from a multi-stage sparse deconvolution. The corresponding inverse map, which we use as an encoder, is a multi-stage convolution sparse coding (CSC), with each stage obtained from unrolling an optimization algorithm for solving the corresponding (convexified) sparse coding program. To avoid computational difficulties in minimizing distributional distance between the real and generated images, we utilize the recent closed-loop transcription (CTRL) framework that optimizes the rate reduction of the learned sparse representations. Conceptually, our method has high-level connections to score-matching methods such as diffusion models. Empirically, our framework demonstrates competitive performance on large-scale datasets, such as ImageNet-1K, compared to existing autoencoding and generative methods under fair conditions. Even with simpler networks and fewer computational resources, our method demonstrates high visual quality in regenerated images. More surprisingly, the learned autoencoder performs well on unseen datasets. Our method enjoys several side benefits, including more structured and interpretable representations, more stable convergence, and scalability to large datasets. Our method is arguably the first to demonstrate that a concatenation of multiple convolution sparse coding/decoding layers leads to an interpretable and effective autoencoder for modeling the distribution of large-scale natural image datasets.

cs.CV↗

arXiv2021

Broadened phonon-assisted wide-band radiation and subsequent low-threshold self-absorption coherent modulation in the high-entropy glass system doped with Nd3+ ions

For crystalline materials with long-range orders, the phonon modes involved in the phonon-assisted radiation process are generally involving one or several phonons with specific vibration frequencies 1-4. In some glassy material, the phonon modes broaden in a short range 5-7. However, the locally distinct chemical environments or mass disorder in high-entropy systems can induce an anharmonic phonon-phonon coupling and composition disorder, which leads to significant phonon broadening 8,9.The terminology of high-entropy comes from the high configuration entropy larger than 1.5R (R is the gas constant), which results from randomly distributed multiple nearly equal components in a crystal lattice 10,11. Inspired by the high-entropy strategy, we deployed a high-entropy glass system (HEGS) doped with neodymium ions, which exhibits a complex structure with tetrahedral voids filled by different ions, including, Li+, Zn2+, Si4+, P5+, S6+, etc. Phonon spectral broadening up to thousands of wavenumbers in the HEGS allows strong wide-band absorption in both the near-infrared and mid-infrared ranges and assists the system radiation, i.e., broadened phonon-assisted wide-band radiation (BPAWR). The subsequent low-threshold self-absorption coherence modulation (SACM) was also observed in the HEGS, modulated by changing excitation wavelengths, sample size, and doping concentrations. The time delay of the BPAWR signal is up to 1.66 ns relative to the zero-delay signal, while the time delay of the Raman process is typically in the order of fs to ps, rarely up to 10 ps 12-15.The BPAWR-SACM can be applied to realize signal amplification of the centered non-absorption band when dual-wavelength lasers pump the HEGS sample, and signal amplification can be up to 26.02 dB. The spectral characteristics of the BPAWR and the dynamics of the energy distribution of the excited species are investigated in detail.

physics.optics↗

arXiv2020

Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing

Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications. Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major challenge for serverless execution frameworks, which will need to rapidly scale and schedule tasks at high throughput, while minimizing data movement across tasks. We demonstrate that, for serverless parallel computations, decentralized scheduling enables scheduling to be distributed across Lambda executors that can schedule tasks in parallel, and brings multiple benefits, including enhanced data locality, reduced network I/Os, automatic resource elasticity, and improved cost effectiveness. We describe the implementation and deployment of our new serverless parallel framework, called Wukong, on AWS Lambda. We show that Wukong achieves near-ideal scalability, executes parallel computation jobs up to 68.17x faster, reduces network I/O by multiple orders of magnitude, and achieves 92.96% tenant-side cost savings compared to numpywren.

cs.DC↗

arXiv2020

InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache

Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerging serverless computing paradigm provides a well-suited, cost-effective platform for object caching. We present InfiniCache, a first-of-its-kind in-memory object caching system that is completely built and deployed atop ephemeral serverless functions. InfiniCache exploits and orchestrates serverless functions' memory resources to enable elastic pay-per-use caching. InfiniCache's design combines erasure coding, intelligent billed duration control, and an efficient data backup mechanism to maximize data availability and cost-effectiveness while balancing the risk of losing cached state and performance. We implement InfiniCache on AWS Lambda and show that it: (1) achieves 31 -- 96X tenant-side cost savings compared to AWS ElastiCache for a large-object-only production workload, (2) can effectively provide 95.4% data availability for each one hour window, and (3) enables comparative performance seen in a typical in-memory cache.

cs.DC↗

arXiv2019

In Search of a Fast and Efficient Serverless DAG Engine

Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies. Such analytics workflow jobs are increasingly characterized by short, fine-grained tasks with large fan-outs. These characteristics make them well-suited for a new cloud computing model called serverless computing or Function-as-a-Service (FaaS), which has become prevalent in recent years. The auto-scaling property of serverless computing platforms accommodates short tasks and bursty workloads, while the pay-per-use billing model of serverless computing providers keeps the cost of short tasks low. In this paper, we thoroughly investigate the problem space of DAG scheduling in serverless computing. We identify and evaluate a set of techniques to make DAG schedulers serverless-aware. These techniques have been implemented in Wukong, a serverless, DAG scheduler attuned to AWS Lambda. Wukong provides decentralized scheduling through a combination of static and dynamic scheduling. We present the results of an empirical study in which Wukong is applied to a range of microbenchmark and real-world DAG applications. Results demonstrate the efficacy of Wukong in minimizing the performance overhead introduced by AWS Lambda --- Wukong achieves competitive performance compared to a serverful DAG scheduler, while improving the performance of real-world DAG jobs by as much as 3.1X at larger scale.

cs.DC↗

arXiv2019

Evaluating the Utility of Document Embedding Vector Difference for Relation Learning

Recent work has demonstrated that vector offsets obtained by subtracting pretrained word embedding vectors can be used to predict lexical relations with surprising accuracy. Inspired by this finding, in this paper, we extend the idea to the document level, in generating document-level embeddings, calculating the distance between them, and using a linear classifier to classify the relation between the documents. In the context of duplicate detection and dialogue act tagging tasks, we show that document-level difference vectors have utility in assessing document-level similarity, but perform less well in multi-relational classification.

cs.CL↗

arXiv2019

Bus Manufacturing Workshop Scheduling Method with Routing Buffer

Aiming at solving the problem that the moving route is complicated and the scheduling is difficult in the routing buffer of the bus in the manufacturing workshop, a routing buffer mathematical programming model for bus manufacturing workshop is proposed. We design a moving approach for minimizing the total setup cost for moving in routing buffer. The framework and the solution ofthe optimization problem of such a bus manufacturing workshop scheduling with routing buffer arepresented. The evaluation results show that, comparing with the irregularly guided moving method, the proposed method can better guide the bus movement in routing buffer by reducing the total setup time of all buses processed at the next stage, and obtaining a better scheduling optimization solution with minimize maximum total completion time.

cs.DS↗

arXiv2017

A Hybrid ACO Algorithm for the Next Release Problem

In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO) for Next Release Problem (NRP). NRP, a NP-hard problem in requirement engineering, is to balance customer requests, resource constraints, and requirement dependencies by requirement selection. Inspired by the successes of Ant Colony Optimization algorithms (ACO) for solving NP-hard problems, we design our HACO to approximately solve NRP. Similar to traditional ACO algorithms, multiple artificial ants are employed to construct new solutions. During the solution construction phase, both pheromone trails and neighborhood information will be taken to determine the choices of every ant. In addition, a local search (first found hill climbing) is incorporated into HACO to improve the solution quality. Extensively wide experiments on typical NRP test instances show that HACO outperforms the existing algorithms (GRASP and simulated annealing) in terms of both solution uality and running time.

cs.NE↗

arXiv2016

Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion

Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and reviews. We observe that a product's compatibility issues with other products are frequently discussed in PCQA and such issues are more frequently addressed in accessories, i.e., via a yes/no question "Does this mouse work with windows 10?". In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA. This problem can naturally have a two-stage framework: first, we perform Complementary Entity (product) Recognition (CER) on yes/no questions; second, we identify the polarities of yes/no answers to assign the complementary entities a compatibility label (compatible, incompatible or unknown). We leverage an existing unsupervised method for the first stage and a 3-class classifier by combining a distant PU-learning method (learning from positive and unlabeled examples) together with a binary classifier for the second stage. The benefit of using distant PU-learning is that it can help to expand more implicit yes/no answers without using any human annotated data. We conduct experiments on 4 products to show that the proposed method is effective.

cs.CL↗

arXiv2015

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.

cs.LG↗

arXiv2014

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.

cs.SI↗