The textbook algorithm for real-weighted single-source shortest paths takes \(O(mn)\) time on a graph with \(m\) edges and \(n\) vertices. The breakthrough algorithm by Fineman takes \(\tilde{O}(mn^{8/9})\) randomized time. The running time was subsequently improved to \(\tilde{O}(mn^{4/5})\) by Huang, Jin, and Quanrud.

The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a key benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench has become the dominant benchmark in this domain, it suffers from several limitations: it has not been updated since its release, is restricted to only 12 repositories, and relies heavily on manual effort for constructing test instances and setting up executable environments, significantly limiting its scalability. We present SWE-bench-Live, a live-updatable benchmark designed to address these limitations. SWE-bench-Live currently includes 1,890 tasks derived from real GitHub issues created since 2024, spanning 223 repositories. Each task is accompanied by a dedicated Docker image to ensure reproducible execution. Additionally, we introduce an automated curation pipeline that streamlines the entire process from instance creation to environment setup, removing manual bottlenecks and enabling scalability and continuous updates. We evaluate a range of state-of-the-art models and agent frameworks on SWE-bench-Live, offering detailed empirical insights into their real-world bug-fixing capabilities. By providing a fresh, diverse, and executable benchmark grounded in live repository activity, SWE-bench-Live supports reliable, large-scale assessment of code LLMs and code agents in realistic development settings.

The textbook algorithm for single-source shortest paths with real-valued edge weights runs in O (mn ) time on a graph with m edges and n vertices. A recent breakthrough algorithm by Fineman [11] takes Õ(mn8/9) randomized time. We present an Õ(mn4/5) randomized time algorithm building on ideas from [11].

Abstract Graph substitution is a key optimization technique used in deep learning frameworks. Traditional search-based methods are one way to address the problem of graph substitution. However, with the ongoing expansion of deep neural networks (DNNs), the exploration of their vast equivalent graph search space becomes increasingly time-consuming. In this paper, we propose two heuristic methods to accelerate the search process in graph substitution, offering a relatively novel direction compared to existing methods. The first method employs a Memory-Augmented heuristic to optimize computation graphs. To further enhance the efficiency of computation graph optimization, the second method uses the simulated annealing method. This method adds computation graphs with degraded performance into the candidate set with a certain probability. The experimental results show that without significant compromise of inference performance, these two methods can find graph substitutions delivering similar DNN computing performance compared to existing searching methods, while the overall searching time can be reduced from hours to seconds. The source code is available at https://github.com/hudevictor/MAS-SAS .

Communication overhead has become one of the major bottlenecks in the distributed training of modern deep neural networks. With such consideration, various quantization-based stochastic gradient descent (SGD) solvers have been proposed and widely adopted, among which SignSGD with majority vote shows a promising direction because of its communication efficiency and robustness against Byzantine attackers. However, SignSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. In this article, a sufficient condition for the convergence of the sign-based gradient descent method is derived, based on which a novel magnitude-driven stochastic-sign-based gradient compressor is proposed to address the non-convergence issue of SignSGD. The convergence of the proposed method is established in the presence of arbitrary data heterogeneity. The Byzantine resilience of sign-based gradient descent methods is quantified, and the error-feedback mechanism is further incorporated to boost the learning performance. Experimental results on the MNIST dataset, the CIFAR-10 dataset, and the Tiny-ImageNet dataset corroborate the effectiveness of the proposed methods.

Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We present several contributions for localized densest subgraph discovery, which seeks dense subgraphs located nearby given seed sets of nodes. We first introduce a generalization of a recent anchored densest subgraph problem, extending this previous objective to hypergraphs and also adding a tunable locality parameter that controls the extent to which the output set overlaps with seed nodes. Our primary technical contribution is to prove when it is possible to obtain a strongly-local algorithm for solving this problem, meaning that the runtime depends only on the size of the input set. We provide a strongly-local algorithm that applies whenever the locality parameter is not too small, and show via counterexample why strongly-local algorithms are impossible below a certain threshold. Along the way to proving our results for localized densest subgraph discovery, we also provide several advances in solving global dense subgraph discovery objectives. This includes the first strongly polynomial time algorithm for the densest supermodular set problem and a flow-based exact algorithm for a heavy and dense subgraph discovery problem in graphs with arbitrary node weights. We demonstrate our algorithms on several web-based data analysis tasks.

Software version migration and program translation are an important and costly part of the lifecycle of large codebases.Traditional machine translation relies on parallel corpora for supervised translation, which is not feasible for program translation due to a dearth of aligned data.Recent unsupervised neural machine translation techniques have overcome data limitations by included techniques such as back translation and low level compiler intermediate representations (IR).These methods face significant challenges due to the noise in code snippet alignment and the diversity of IRs respectively.In this paper we propose a novel model called Code Distillation (CoDist) whereby we capture the semantic and structural equivalence of code in a language agnostic intermediate representation.Distilled code serves as a translation pivot for any programming language, leading by construction to parallel corpora which scale to all available source code by simply applying the distillation compiler.We demonstrate that our approach achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks, with an average absolute increase of 12.7% on the TransCoder GeeksforGeeks translation benchmark compare to TransCoder-ST.

Program translation, i.e. transcompilation has been attracting increasing attention from researchers due to its enormous application value.However, we observe that current program translating models still make elementary syntax errors, particularly when the source language uses syntax elements not present in the target language, which is exactly what developers are concerned about while may not be well exposed by frequently used metrics such as BLEU, CodeBLEU and Computation Accuracy.In this paper, we focus on evaluating the model's ability to address these basic syntax errors and developed an novel active defects probing suite, the Syntactic Unit Tests (SUT) and highly interpretable evaluation harness including Syntax Unit Test Accuracy (SUT Acc) metric and Syntax Element Test Score (SETS), to help diagnose and promote progress in this area.Our Syntactic Unit Test fills the gap in the community for a fine-grained evaluation dataset for program translation.Experimental analysis shows that our evaluation harness is more accurate, reliable, and in line with human judgments compared to previous metrics.

We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communities in social and information networks. The idea of $\mu$-conductance is similar to the traditional graph conductance, but disregards sets with small volume. We derive a sequence of optimization problems including a low-rank semi-definite program from which we can derive a lower bound on the optimal $\mu$-conductance value. These ideas give the first theoretically sound bound on the behavior of the network community profile for a wide range of cluster sizes. The algorithm scales up to graphs with hundreds of thousands of nodes and we demonstrate how our framework validates the predicted structures of real-world graphs.

Yufan Huang, Yanzhe Zhang, Jiaao Chen, Xuezhi Wang, Diyi Yang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Recent advances in social media and information technology have enabled much faster dissemination of information, while at the same time raise concerns about privacy leakage after various privacy breaches. Therefore, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. Very recently, the rigorous framework of differential privacy has been introduced to measure the privacy guarantees of gossip protocols in the simplified complete network scenario. In this work, we extend the study to general networks. First, lower bounds of the differential privacy guarantees are derived for the gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantity the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present in the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.

The existence of shadows in very high resolution satellite images obstructs image interpretation and the following applications, such as target detection and recognition. Traditional shadow detection methods consider only the pixel-level properties, such as color and intensity of image pixels, and thus, may produce errors around object boundaries. To overcome this problem, a novel shadow detection algorithm based on extended random walker (ERW) is proposed by jointly integrating both shadow property and spatial correlations among adjacent pixels. First, a set of training samples is automatically generated via an improved Otsu-based thresholding method. Then, the support vector machine is applied to obtain an initial detection map, which categorizes all the pixels in the scene into shadow and nonshadow. Finally, the initial detection map is refined with the ERW model, which can simultaneously characterize the shadow property and spatial information in satellite images to further improve shadow detection accuracy. Experiments performed on five real remote sensing images demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of detection accuracy.

In this work, we investigate information spreading in multiplex networks, adopting the gossip (random-walk) based model. Two key features of multiplex networks allow potentially much faster information spreading: availability of multiple channels and communication actions for each user, and more choices on neighbor contacting. As a first work in this area, we explore the impact of layer number, layer similarity, and average node degree on the efficiency of information spreading, and theoretically prove our results. Another observation is that multiplex network structure can improve network connectivity. Simulation results are provided to support and complement theoretical analysis.

In this paper, a novel spectral-spatial very high resolution images shadow detection algorithm based on random walker is proposed. First, a set of training samples is obtained by an improved Otsu based thresholding method automatically. Then, a widely used pixel-wise classifier, i.e., the Support Vector Machine (SVM), is applied to obtain an initial binary classification map. Finally, the initial classification map is refined with the extended random walker model, which can jointly integrating both the spectral characteristics and spatial-correlation among adjacent pixels to further improve shadow detection accuracy. Experimental results performed on real data sets demonstrate the superiority of the proposed method over several state-of-the-art methods.

In this work, we study the information spreading time in multiplex networks, adopting the gossip (random-walk) based information spreading model. A new metric called multiplex conductance is defined based on the multiplex network structure and used to quantify the information spreading time in a general multiplex network in the idealized setting. Multiplex conductance is then evaluated for some interesting multiplex networks to facilitate understanding in this new area. Finally, the tradeoff between the information spreading efficiency improvement and the layer cost is examined to explain the user's social behavior and motivate effective multiplex network designs.

An important application for modern large-scale networks is to spread the information efficiently to the largest audience. To better understand the theoretical underpinnings, a novel graph metric named mobile conductance was proposed in our previous work to evaluate the information spreading time of a connected mobile network. By capturing the details of both network structure and mobility pattern, this metric essentially determines the network bottleneck for conducting information flow under general network mobility. Despite major relaxation on node mobility, only slight relaxation on network connectivity was made in our previous work. In this paper, we make another major relaxation on the network connectivity by extending the mobile-conductance based analytical model to the sparse setting, hence offering a unified view. Interestingly, a penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. By jointly considering mobility and connectivity, we derive the mobile conductance for various mobility models with general connectivity. Using these analytical results, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to compensate for network connectivity deficiency.

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.