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Yao Xu

Publications and source records attributed to Yao Xu.

At least 55 records · Page 3Linked to original sources

Collective Vector Clocks: Low-Overhead Transparent Checkpointing for MPI

Taking snapshots of the state of a distributed computation is useful for off-line analysis of the computational state, for later restarting from the saved snapshot, for cloning a copy of the computation, and for migration to a new cluster. The problem is made more difficult when supporting collective operations across processes, such as barrier, reduce operations, scatter and gather, etc. Some processes may have reached the barrier or other collective operation, while other processes wait a long time to reach that same barrier or collective operation. At least two solutions are well-known in the literature: (I) draining in-flight network messages and then freezing the network at checkpoint time; and (ii) adding a barrier prior to the collective operation, and either completing the operation or aborting the barrier if not all processes are present. Both solutions suffer important drawbacks. The code in the first solution must be updated whenever one ports to a newer network. The second solution implies additional barrier-related network traffic prior to each collective operation. This work presents a third solution that avoids both drawbacks. There is no additional barrier-related traffic, and the solution is implemented entirely above the network layer. The work is demonstrated in the context of transparent checkpointing of MPI libraries for parallel computation, where each of the first two solutions have already been used in prior systems, and then abandoned due to the aforementioned drawbacks. Experiments demonstrate the low runtime overhead of this new, network-agnostic approach. The approach is also extended to non-blocking, collective operations in order to handle overlapping of computation and communication.

cs.DC↗

Implementation-Oblivious Transparent Checkpoint-Restart for MPI

This work presents experience with traditional use cases of checkpointing on a novel platform. A single codebase (MANA) transparently checkpoints production workloads for major available MPI implementations: "develop once, run everywhere". The new platform enables application developers to compile their application against any of the available standards-compliant MPI implementations, and test each MPI implementation according to performance or other features.

cs.DC↗

MGeo: Multi-Modal Geographic Pre-Training Method

As a core task in location-based services (LBS) (e.g., navigation maps), query and point of interest (POI) matching connects users' intent with real-world geographic information. Recently, pre-trained models (PTMs) have made advancements in many natural language processing (NLP) tasks. Generic text-based PTMs do not have enough geographic knowledge for query-POI matching. To overcome this limitation, related literature attempts to employ domain-adaptive pre-training based on geo-related corpus. However, a query generally contains mentions of multiple geographic objects, such as nearby roads and regions of interest (ROIs). The geographic context (GC), i.e., these diverse geographic objects and their relationships, is therefore pivotal to retrieving the most relevant POI. Single-modal PTMs can barely make use of the important GC and therefore have limited performance. In this work, we propose a novel query-POI matching method Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Besides, there is no publicly available benchmark for this topic. In order to facilitate further research, we build a new open-source large-scale benchmark Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS). The queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses on GeoTES demonstrate that our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs, even when the queries' GC is not provided. Our code and dataset are publicly available at https://github.com/PhantomGrapes/MGeo.

cs.CL↗

GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark

With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has never been a benchmark to build a unified standard. In this work, we propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks, including geographic textual similarity on recall, geographic textual similarity on rerank, geographic elements tagging, geographic composition analysis, geographic where what cut, and geographic entity alignment. We also pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.

cs.CL↗

Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios

In this letter, an orthogonal time frequency space (OTFS) based non-orthogonal multiple access (NOMA) scheme is investigated for the coordinated direct and relay transmission system, where a source directly communicates with a near user with high mobile speed, and it needs the relaying assistance to serve the far user also having high mobility. Due to the coexistence of signal superposition coding and multi-domain transformation, the performance of OTFS-based NOMA is usually challenging to be measured from a theoretical perspective. To accurately evaluate the system performance of the proposed scheme, we derive the closed-form expressions for the outage probability and the outage sum rate by using the Inversion formula and characteristic function. Numerical results verify the performance superiority and the effectiveness of the proposed scheme.

cs.IT↗

Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation

Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in modelling non-linear systems. However, traditional deep learning algorithms completely discard the inherent graph structure within the metro system. Graph-based deep learning algorithms could utilise the graph structure but raise a few challenges, such as how to determine the weights of the edges and the shallow receptive field caused by the over-smoothing issue. To further improve these challenges, this study proposes a model based on GraphSAGE with an edge weights learner applied. The edge weights learner utilises socially meaningful features to generate edge weights. Hypergraph and temporal exploitation modules are also constructed as add-ons for better performance. A comparison study is conducted on the proposed algorithm and other state-of-art graph neural networks, where the proposed algorithm could improve the performance.

cs.LG↗

Scalable colored sub-ambient radiative coolers based on a polymer-Tamm photonic structure

Daytime radiative coolers cool objects below the air temperature without any electricity input, while most of them are limited by a silvery or whitish appearance. Colored daytime radiative coolers (CDRCs) with diverse colors, scalable manufacture, and sub-ambient cooling have not been achieved. We introduce a polymer-Tamm photonic structure to enable a high infrared emittance and an engineered absorbed solar irradiance, governed by the quality factor (Q-factor). We theoretically determine the theoretical thresholds for sub-ambient cooling through yellow, magenta, and cyan CDRCs. We experimentally fabricate and observe a temperature drop of 2.6-8.8 degrees Celsius on average during daytime and 4.0-4.4degrees Celsius during nighttime. Furthermore, we demonstrate a scalable-manufactured magenta CDRC with a width of 60 cm and a length of 500 cm by a roll-to-roll deposition technique. This work provides guidelines for large-scale CDRCs and offers unprecedented opportunities for potential applications with energy-saving, aesthetic, and visual comfort demands.

physics.optics↗

An efficient polynomial-time approximation scheme for parallel multi-stage open shops

Various new scheduling problems have been arising from practical production processes and spawning new research areas in the scheduling field. We study the parallel multi-stage open shops problem, which generalizes the classic open shop scheduling and parallel machine scheduling problems. Given m identical k-stage open shops and a set of n jobs, we aim to process all jobs on these open shops with the minimum makespan, i.e., the completion time of the last job, under the constraint that job preemption is not allowed. We present an efficient polynomial-time approximation scheme (EPTAS) for the case when both m and k are constant. The main idea for our EPTAS is the combination of several categorization, scaling, and linear programming rounding techniques. Jobs and/or operations are first scaled and then categorized carefully into multiple types so that different types of jobs and/or operations are scheduled appropriately without increasing the makespan too much.

cs.DS↗

A polynomial-time approximation scheme for parallel two-stage flowshops under makespan constraint

As a hybrid of the Parallel Two-stage Flowshop problem and the Multiple Knapsack problem, we investigate the scheduling of parallel two-stage flowshops under makespan constraint, which was motivated by applications in cloud computing and introduced by Chen et al. [3] recently. A set of two-stage jobs are selected and scheduled on parallel two-stage flowshops to achieve the maximum total profit while maintaining the given makespan constraint. We give a positive answer to an open question about its approximability proposed by Chen et al. [3]. More specifically, based on guessing strategies and rounding techniques for linear programs, we present a polynomial-time approximation scheme (PTAS) for the case when the number of flowshops is a fixed constant.

cs.DS↗

MANA-2.0: A Future-Proof Design for Transparent Checkpointing of MPI at Scale

MANA-2.0 is a scalable, future-proof design for transparent checkpointing of MPI-based computations. Its network transparency ("network-agnostic") feature ensures that MANA-2.0 will provide a viable, efficient mechanism for transparently checkpointing MPI applications on current and future supercomputers. MANA-2.0 is an enhancement of previous work, the original MANA, which interposes MPI calls, and is a work in progress intended for production deployment. MANA-2.0 implements a series of new algorithms and features that improve MANA's scalability and reliability, enabling transparent checkpoint-restart over thousands of MPI processes. MANA-2.0 is being tested on today's Cori supercomputer at NERSC using Cray MPICH library over the Cray GNI network, but it is designed to work over any standard MPI running over an arbitrary network. Two widely-used HPC applications were selected to demonstrate the enhanced features of MANA-2.0: GROMACS, a molecular dynamics simulation code with frequent point-to-point communication, and VASP, a materials science code with frequent MPI collective communication. Perhaps the most important lesson to be learned from MANA-2.0 is a series of algorithms and data structures for library-based transformations that enable MPI-based computations over MANA-2.0 to reliably survive the checkpoint-restart transition.

cs.DC↗

Convergence rates in homogenization of parabolic systems with locally periodic coefficients

In this paper we study the quantitative homogenization of second-order parabolic systems with locally periodic (in both space and time) coefficients. The $O(\varepsilon)$ scale-invariant error estimate in $L^2(0, T; L^{\frac{2d}{d-1}}(Ω))$ is established in $C^{1, 1}$ cylinders under minimum smoothness conditions on the coefficients. This process relies on critical estimates of smoothing operators. We also develop a new construction of flux correctors in the parabolic manner and a sharp estimate for temporal boundary layers.

math.AP↗

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: Demonstration for a PET/CT imaging procedure

This paper describes the development and validation of a Monte Carlo (MC) dose computing module dedicated to organ dose calculations of patients undergoing nuclear medicine (NM) internal radiation exposures involving 18F-FDG PET/CT examination. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features. To validate the code, we compared dose distributions from the 0.511-MeV point photon source calculated for a water phantom as well as a patient PET/CT phantom against a well-tested MC code, GATE. The water-phantom results show excellent agreement, suggesting that the radiation physics module in the new NM code is adequate. To demonstrate the clinical utility and advantage of ARCHER-NM, one set of PET/CT data for an adult male NM patient is calculated using the new code. Radiosensitive organs in the CT dataset are segmented using a CNN-based tool called DeepViewer. The PET image intensity maps are converted to radioactivity distributions to allow for MC radiation transport dose calculations at the voxel level. The dose rate maps and corresponding statistical uncertainties were calculated for the duration of PET image acquisition. The dose rate results of the 18F-FDG PET imaging patient show that ARCHER-NM's results agree very well with those of the GATE within 0.58% to 4.11%. Most impressively, ARCHER-NM obtains such results in less than 0.5 minutes while it takes GATE as much as 376 minutes. This is the first study presenting GPU-accelerated patient-specific MC internal radiation dose rate calculations for clinically realistic 18F-FDG PET/CT imaging cases involving auto-segmentation of whole-body PET/CT images. This study suggests that modern computing tools -- ARCHER-NM and DeepViewer -- are accurate and fast enough for routine internal dosimetry in NM clinics.

physics.med-ph↗

Approximation algorithms for the directed path partition problems

Given a directed graph $G = (V, E)$, the $k$-path partition problem is to find a minimum collection of vertex-disjoint directed paths each of order at most $k$ to cover all the vertices of $V$. The problem has various applications in facility location, network monitoring, transportation and others. Its special case on undirected graphs has received much attention recently, but the general directed version is seemingly untouched in the literature. We present the first $k/2$-approximation algorithm, for any $k \ge 3$, based on a novel concept of augmenting path to minimize the number of singletons in the partition. When $k \ge 7$, we present an improved $(k+2)/3$-approximation algorithm based on the maximum path-cycle cover followed by a careful $2$-cycle elimination process. When $k = 3$, we define the second novel kind of augmenting paths and propose an improved $13/9$-approximation algorithm.

cs.DS↗

The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion

Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors with heterogenous characteristics has not been fully explored, due to the lack of availability of big amounts of perfectly aligned multi-sensor image data with diverse scenes of high resolutions, especially for synthetic aperture radar (SAR) data and optical imagery. To promote the development of deep learning based SAR-optical fusion approaches, we release the QXS-SAROPT dataset, which contains 20,000 pairs of SAR-optical image patches. We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images. These images cover three port cities: San Diego, Shanghai and Qingdao. Here, we present a detailed introduction of the construction of the dataset, and show its two representative exemplary applications, namely SAR-optical image matching and SAR ship detection boosted by cross-modal information from optical images. As a large open SAR-optical dataset with multiple scenes of a high resolution, we believe QXS-SAROPT will be of potential value for further research in SAR-optical data fusion technology based on deep learning.

eess.IV↗

Boosting ship detection in SAR images with complementary pretraining techniques

Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hardly to obtain a good ship detector because of different imaging perspective and geometry. In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset. On the other hand, to handle the problem of different imaging geometry between optical and SAR images, we propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning on the optical-SAR matching task. Finally, observing that the OSD pretraining based SAR ship detector has a better recall on sea area while the OSM pretraining based SAR ship detector can reduce false alarms on land area, we combine the predictions of the two detectors through weighted boxes fusion to further improve detection results. Extensive experiments on four SAR ship detection datasets and two representative CNN-based detection benchmarks are conducted to show the effectiveness and complementarity of the two proposed detectors, and the state-of-the-art performance of the combination of the two detectors. The proposed method won the sixth place of ship detection in SAR images in 2020 Gaofen challenge.

cs.CV↗

Truncation-Free Matching System for Display Advertising at Alibaba

Matching module plays a critical role in display advertising systems. Without query from user, it is challenging for system to match user traffic and ads suitably. System packs up a group of users with common properties such as the same gender or similar shopping interests into a crowd. Here term crowd can be viewed as a tag over users. Then advertisers bid for different crowds and deliver their ads to those targeted users. Matching module in most industrial display advertising systems follows a two-stage paradigm. When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds. However, in applications such as display advertising at Alibaba, with very large volumes of crowds and ads, both stages of matching have to truncate the long-tailed parts for online serving, under limited latency. That's to say, not all ads have the chance to participate in online matching. This results in sub-optimal result for both advertising performance and platform revenue. In this paper, we study the truncation problem and propose a Truncation Free Matching System (TFMS). The basic idea is to decouple the matching computation from the online pipeline. Instead of executing the two-stage matching when user visits, TFMS utilizes a near-line truncation-free matching to pre-calculate and store those top valuable ads for each user. Then the online pipeline just needs to fetch the pre-stored ads as matching results. In this way, we can jump out of online system's latency and computation cost limitations, and leverage flexible computation resource to finish the user-ad matching. TFMS has been deployed in our productive system since 2019, bringing (i) more than 50% improvement of impressions for advertisers who encountered truncation before, (ii) 9.4% Revenue Per Mile gain, which is significant enough for the business.

cs.IR↗

Towards GANs' Approximation Ability

Generative adversarial networks (GANs) have attracted intense interest in the field of generative models. However, few investigations focusing either on the theoretical analysis or on algorithm design for the approximation ability of the generator of GANs have been reported. This paper will first theoretically analyze GANs' approximation property. Similar to the universal approximation property of the fully connected neural networks with one hidden layer, we prove that the generator with the input latent variable in GANs can universally approximate the potential data distribution given the increasing hidden neurons. Furthermore, we propose an approach named stochastic data generation (SDG) to enhance GANs'approximation ability. Our approach is based on the simple idea of imposing randomness through data generation in GANs by a prior distribution on the conditional probability between the layers. SDG approach can be easily implemented by using the reparameterization trick. The experimental results on synthetic dataset verify the improved approximation ability obtained by this SDG approach. In the practical dataset, four GANs using SDG can also outperform the corresponding traditional GANs when the model architectures are smaller.

cs.LG↗

Training few-shot classification via the perspective of minibatch and pretraining

Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained to learn the ability of handling classification tasks on extremely large or infinite episodes representing different classification task, each with a small labeled support set and its corresponding query set. In this work, we advance this few-shot classification paradigm by formulating it as a supervised classification learning problem. We further propose multi-episode and cross-way training techniques, which respectively correspond to the minibatch and pretraining in classification problems. Experimental results on a state-of-the-art few-shot classification method (prototypical networks) demonstrate that both the proposed training strategies can highly accelerate the training process without accuracy loss for varying few-shot classification problems on Omniglot and miniImageNet.

cs.LG↗