OpenRaiser/Intern-Atlas huggingface dataset:
DF* PageRank: Improved Incrementally Expanding Approaches for Updating PageRank on Dynamic Graphs
Venue: arXiv cs.DC 2024
Year: 2024

PageRank is a widely used centrality measure that assesses the significance of vertices in a graph by considering their connections and the importance of those connections. Efficiently updating PageRank on dynamic graphs is essential for various applications due to the increasing scale of datasets. This technical report introduces our improved Dynamic Frontier (DF) and Dynamic Frontier with Pruning (DF-P) approaches. Given a batch update comprising edge insertions and deletions, these approaches iteratively identify vertices likely to change their ranks with minimal overhead. On a server featuring a 64-core AMD EPYC-7742 processor, our approaches outperform Static and Dynamic Traversal PageRank by 5.2x/15.2x and 1.3x/3.5x respectively - on real-world dynamic graphs, and by 7.2x/9.6x and 4.0x/5.6x on large static graphs with random batch updates. Furthermore, our approaches improve performance at a rate of 1.8x/1.7x for every doubling of threads.
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
Venue: arXiv cs.CV 2024
Year: 2024

Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction. Due to its potentially rich semantic information, RGB image is commonly fused to enhance the completion effect. Image-guided depth completion involves three key challenges: 1) how to effectively fuse the two modalities; 2) how to better recover depth information; and 3) how to achieve real-time prediction for practical autonomous driving. To solve the above problems, we propose a concise but effective network, named CENet, to achieve high-performance depth completion with a simple and elegant structure. Firstly, we use a fast guidance module to fuse the two sensor features, utilizing abundant auxiliary features extracted from the color space. Unlike other commonly used complicated guidance modules, our approach is intuitive and low-cost. In addition, we find and analyze the optimization inconsistency problem for observed and unobserved positions, and a decoupled depth prediction head is proposed to alleviate the issue. The proposed decoupled head can better output the depth of valid and invalid positions with very few extra inference time. Based on the simple structure of dual-encoder and single-decoder, our CENet can achieve superior balance between accuracy and efficiency. In the KITTI depth completion benchmark, our CENet attains competitive performance and inference speed compared with the state-of-the-art methods. To validate the generalization of our method, we also evaluate on indoor NYUv2 dataset, and our CENet still achieve impressive results. The code of this work will be available at https://github.com/lmomoy/CHNet.
Toward the Identifiability of Comparative Deep Generative Models
Venue: arXiv cs.LG 2024
Year: 2024

Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of comparing data sets from different sources. One such example is the setting of contrastive analysis that focuses on describing patterns that are enriched in a target data set compared to a background data set. The practical deployment of those models often assumes that DGMs naturally infer interpretable and modular latent representations, which is known to be an issue in practice. Consequently, existing methods often rely on ad-hoc regularization schemes, although without any theoretical grounding. Here, we propose a theory of identifiability for comparative DGMs by extending recent advances in the field of non-linear independent component analysis. We show that, while these models lack identifiability across a general class of mixing functions, they surprisingly become identifiable when the mixing function is piece-wise affine (e.g., parameterized by a ReLU neural network). We also investigate the impact of model misspecification, and empirically show that previously proposed regularization techniques for fitting comparative DGMs help with identifiability when the number of latent variables is not known in advance. Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization. We empirically validate our theory and new methodology using simulated data as well as a recent data set of genetic perturbations in cells profiled via single-cell RNA sequencing.
Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets
Venue: arXiv cs.CR 2024
Year: 2024

This paper considers the problem of the private release of sample means of speed values from traffic datasets. Our key contribution is the development of user-level differentially private algorithms that incorporate carefully chosen parameter values to ensure low estimation errors on real-world datasets, while ensuring privacy. We test our algorithms on ITMS (Intelligent Traffic Management System) data from an Indian city, where the speeds of different buses are drawn in a potentially non-i.i.d. manner from an unknown distribution, and where the number of speed samples contributed by different buses is potentially different. We then apply our algorithms to large synthetic datasets, generated based on the ITMS data. Here, we provide theoretical justification for the observed performance trends, and also provide recommendations for the choices of algorithm subroutines that result in low estimation errors. Finally, we characterize the best performance of pseudo-user creation-based algorithms on worst-case datasets via a minimax approach; this then gives rise to a novel procedure for the creation of pseudo-users, which optimizes the worst-case total estimation error. The algorithms discussed in the paper are readily applicable to general spatio-temporal IoT datasets for releasing a differentially private mean of a desired value.
Correction to "Private Information Retrieval Over Gaussian MAC"
Venue: arXiv cs.IT 2024
Year: 2024

In the above article \cite{shmuel2021private}, the authors introduced a PIR scheme for the Additive White Gaussian Noise (AWGN) Multiple Access Channel (MAC), both with and without fading. The authors utilized the additive nature of the channel and leveraged the linear properties and structure of lattice codes to retrieve the desired message without the servers acquiring any knowledge about the retrieved message's index. Theorems 3 and 4 in \cite{shmuel2021private} contain an error arising from the incorrect usage of the modulo operator. Moreover, the proofs assume a one-to-one mapping function, $ϕ(\cdot)$, between a message $W_j\in\mathbb{F}_p^L$ and the elements of $\cC$, mistakenly suggesting that the user possesses all the required information in advance. % \st{However, this is not the case.} \textcolor{black}{To deal with that, we defined $ϕ(\cdot)$ as a one-to-one mapping function between a vector of $l$ information bits and a lattice point $λ\in\cC$}. Herein, we present the corrected versions of these theorems.
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences
Venue: arXiv cs.LG 2024
Year: 2024

This study explores the application of self-supervised learning techniques for event sequences. It is a key modality in various applications such as banking, e-commerce, and healthcare. However, there is limited research on self-supervised learning for event sequences, and methods from other domains like images, texts, and speech may not easily transfer. To determine the most suitable approach, we conduct a detailed comparative analysis of previously identified best-performing methods. We find that neither the contrastive nor generative method is superior. Our assessment includes classifying event sequences, predicting the next event, and evaluating embedding quality. These results further highlight the potential benefits of combining both methods. Given the lack of research on hybrid models in this domain, we initially adapt the baseline model from another domain. However, upon observing its underperformance, we develop a novel method called the Multimodal-Learning Event Model (MLEM). MLEM treats contrastive learning and generative modeling as distinct yet complementary modalities, aligning their embeddings. The results of our study demonstrate that combining contrastive and generative approaches into one procedure with MLEM achieves superior performance across multiple metrics.
Motion-induced error reduction for high-speed dynamic digital fringe projection system
Venue: arXiv cs.CV 2024
Year: 2024

In phase-shifting profilometry (PSP), any motion during the acquisition of fringe patterns can introduce errors because it assumes both the object and measurement system are stationary. Therefore, we propose a method to pixel-wise reduce the errors when the measurement system is in motion due to a motorized linear stage. The proposed method introduces motion-induced error reduction algorithm, which leverages the motor's encoder and pinhole model of the camera and projector. 3D shape measurement is possible with only three fringe patterns by applying geometric constraints of the digital fringe projection system. We address the mismatch problem due to the motion-induced camera pixel disparities and reduce phase-shift errors. These processes are easy to implement and require low computational cost. Experimental results demonstrate that the presented method effectively reduces the errors even in non-uniform motion.
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing
Venue: arXiv cs.CV 2024
Year: 2024

Recently, reference-based image super-resolution (RefSR) has shown excellent performance in image super-resolution (SR) tasks. The main idea of RefSR is to utilize additional information from the reference (Ref) image to recover the high-frequency components in low-resolution (LR) images. By transferring relevant textures through feature matching, RefSR models outperform existing single image super-resolution (SISR) models. However, their performance significantly declines when a domain gap between Ref and LR images exists, which often occurs in real-world scenarios, such as satellite imaging. In this letter, we introduce a Domain Matching (DM) module that can be seamlessly integrated with existing RefSR models to enhance their performance in a plug-and-play manner. To the best of our knowledge, we are the first to explore Domain Matching-based RefSR in remote sensing image processing. Our analysis reveals that their domain gaps often occur in different satellites, and our model effectively addresses these challenges, whereas existing models struggle. Our experiments demonstrate that the proposed DM module improves SR performance both qualitatively and quantitatively for remote sensing super-resolution tasks.
Approaching Maximum Likelihood Decoding Performance via Reshuffling ORBGRAND
Venue: arXiv cs.IT 2024
Year: 2024

Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance loss in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.
Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction
Venue: arXiv cs.LG 2024
Year: 2024

Remaining useful life prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multi-head attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the C-MAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.
A Multi-Period Topology and Design Optimization Approach for District Heating Networks
Venue: arXiv cs.CE 2024
Year: 2024

The transition to 4th generation district heating creates a growing need for scalable, automated design tools that accurately capture the spatial and temporal details of heating network operation. This paper presents an automated design approach for the optimal design of district heating networks that combines scalable density-based topology optimization with a multi-period approach. In this way, temporal variations in demand, supply, and heat losses can be taken into account while optimizing the network design based on a nonlinear physics model. The transition of the automated design approach from worst-case to multi-period shows a design progression from separate branched networks to a single integrated meshed network topology connecting all producers. These integrated topologies emerge without imposing such structures a priori. They increase network connectivity, and allow for more flexible shifting of heat loads between different producers and heat consumers, resulting in more cost-effective use of heat. In a case study, this integrated design resulted in an increase in waste heat share of 42.8 % and a subsequent reduction in project cost of 17.9 %. We show how producer unavailability can be accounted for in the automated design at the cost of a 3.1 % increase in the cost of backup capacity. The resulting optimized network designs of this approach connect multiple low temperature heat sources in a single integrated network achieving high waste heat utilization and redundancy, highlighting the applicability of the approach to next-generation district heating networks.
LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection
Venue: arXiv cs.CR 2024
Year: 2024

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.
Finding Challenging Metaphors that Confuse Pretrained Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Metaphors are considered to pose challenges for a wide spectrum of NLP tasks. This gives rise to the area of computational metaphor processing. However, it remains unclear what types of metaphors challenge current state-of-the-art models. In this paper, we test various NLP models on the VUA metaphor dataset and quantify to what extent metaphors affect models' performance on various downstream tasks. Analysis reveals that VUA includes a large number of metaphors that pose little difficulty to downstream tasks. We would like to shift the attention of researchers away from these metaphors to instead focus on challenging metaphors. To identify hard metaphors, we propose an automatic pipeline that identifies metaphors that challenge a particular model. Our analysis demonstrates that our detected hard metaphors contrast significantly with VUA and reduce the accuracy of machine translation by 16\%, QA performance by 4\%, NLI by 7\%, and metaphor identification recall by over 14\% for various popular NLP systems.
Flexible Parallel Neural Network Architecture Model for Early Prediction of Lithium Battery Life
Venue: arXiv cs.LG 2024
Year: 2024

The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios
Venue: arXiv cs.LG 2024
Year: 2024

Parameter-efficient fine-tuning (PEFT) techniques, such as adapter tuning, aim to fine-tune a pre-trained language model (PLM) using a minimal number of parameters for a specific task or profile. Although adapter tuning provides increased parameter efficiency compared to full-model fine-tuning, it introduces a small set of additional parameters attached to a PLM for each profile. This can become problematic in practical applications with multiple profiles, particularly when a significant increase in the number of profiles linearly boosts the total number of additional parameters. To mitigate this issue, we introduce X-PEFT, a novel PEFT method that leverages a multitude of given adapters by fine-tuning an extremely small set of compact tensors for a new profile, which serve as binary masks to adaptively select the given adapters. To efficiently validate our proposed method, we implement it using a large number of trained or untrained (random) adapters. We evaluate the performance of X-PEFT through LaMP and GLUE tasks and demonstrate that it either matches or surpasses the effectiveness of conventional adapter tuning, despite reducing the memory requirements per profile by a factor of 10,000 compared to it.
Improving device-aware Web services and their mobile clients through an aspect-oriented, model-driven approach
Venue: arXiv cs.SE 2024
Year: 2024

Context: Mobile devices have become an essential element in our daily lives, even for connecting to the Internet. Consequently, Web services have become extremely important when offering services through the Internet. However, current Web services are very inflexible as regards their invocation from different types of device, especially if we consider the need for them to be adaptable when being invoked from mobile devices. Objective: In this paper, we provide an approach for the creation of flexible Web services which can be invoked transparently from different device types and which return subsequent responses, as well as providing the client's adaptation as a result of the particular device characteristics and end-user preferences in a completely decoupled way. Method: Aspect-Oriented Programming and model-driven development have been used to reduce both the impact of service and client code adaptation for multiple devices as well as to facilitate the developer's task. Results: A model-driven methodology can be followed from system models to code, providing the Web service developer with the option of marking which services should be adapted to mobile devices in the UML models, and obtaining the decoupled adaptation code automatically from the models. Conclusion: We can conclude that the approach presented in this paper provides us with the possibility of following the development of mobile-aware Web services in an integrated platform, benefiting from the use of aspect-oriented techniques not only for maintaining device-related code completely decoupled from the main functionality one, but also allowing a modularized non-intrusive adaptation of mobile clients to the specific device characteristics as well as to final user preferences.
Reconstructing Close Human Interactions from Multiple Views
Venue: arXiv cs.CV 2024
Year: 2024

This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras. The difficulty arises from the noisy or false 2D keypoint detections due to inter-person occlusion, the heavy ambiguity in associating keypoints to individuals due to the close interactions, and the scarcity of training data as collecting and annotating motion data in crowded scenes is resource-intensive. We introduce a novel system to address these challenges. Our system integrates a learning-based pose estimation component and its corresponding training and inference strategies. The pose estimation component takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of each individual using a 3D conditional volumetric network. As the network doesn't need images as input, we can leverage known camera parameters from test scenes and a large quantity of existing motion capture data to synthesize massive training data that mimics the real data distribution in test scenes. Extensive experiments demonstrate that our approach significantly surpasses previous approaches in terms of pose accuracy and is generalizable across various camera setups and population sizes. The code is available on our project page: https://github.com/zju3dv/CloseMoCap.