OpenRaiser/Intern-Atlas huggingface dataset:
Egret: Reinforcement Mechanism for Sequential Computation Offloading in Edge Computing
Venue: arXiv cs.DC 2024
Year: 2024

As an emerging computing paradigm, edge computing offers computing resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical in reality. However, previous works assume user privacy information to be known or consider the number of users in edge scenarios to be static. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computing resources with different configurations to clients in turn. Clients independently choose which computing resources to purchase and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without considering clients' preferences. Experimental results show that the revenue of ECSP in Egret is only 1.29\% lower than Oracle and 23.43\% better than the state-of-the-art when the client arrives dynamically.
A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion
Venue: arXiv cs.CV 2024
Year: 2024

In image fusion tasks, images from different sources possess distinct characteristics. This has driven the development of numerous methods to explore better ways of fusing them while preserving their respective characteristics.Mamba, as a state space model, has emerged in the field of natural language processing. Recently, many studies have attempted to extend Mamba to vision tasks. However, due to the nature of images different from causal language sequences, the limited state capacity of Mamba weakens its ability to model image information. Additionally, the sequence modeling ability of Mamba is only capable of spatial information and cannot effectively capture the rich spectral information in images. Motivated by these challenges, we customize and improve the vision Mamba network designed for the image fusion task. Specifically, we propose the local-enhanced vision Mamba block, dubbed as LEVM. The LEVM block can improve local information perception of the network and simultaneously learn local and global spatial information. Furthermore, we propose the state sharing technique to enhance spatial details and integrate spatial and spectral information. Finally, the overall network is a multi-scale structure based on vision Mamba, called LE-Mamba. Extensive experiments show the proposed methods achieve state-of-the-art results on multispectral pansharpening and multispectral and hyperspectral image fusion datasets, and demonstrate the effectiveness of the proposed approach. Codes can be accessed at \url{https://github.com/294coder/Efficient-MIF}.
OWLOOP: Interfaces for Mapping OWL Axioms into OOP Hierarchies
Venue: arXiv cs.AI 2024
Year: 2024

The paper tackles the issue of mapping logic axioms formalised in the Ontology Web Language (OWL) within the Object-Oriented Programming (OOP) paradigm. The issues of mapping OWL axioms hierarchies and OOP objects hierarchies are due to OWL-based reasoning algorithms, which might change an OWL hierarchy at runtime; instead, OOP hierarchies are usually defined as static structures. Although programming paradigms based on reflection allow changing the OOP hierarchies at runtime and mapping OWL axioms dynamically, there are no currently available mechanisms that do not limit the reasoning algorithms. Thus, the factory-based paradigm is typically used since it decouples the OWL and OOP hierarchies. However, the factory inhibits OOP polymorphism and introduces a paradigm shift with respect to widely accepted OOP paradigms. We present the OWLOOP API, which exploits the factory to not limit reasoning algorithms, and it provides novel OOP interfaces concerning the axioms in an ontology. OWLOOP is designed to limit the paradigm shift required for using ontologies while improving, through OOP-like polymorphism, the modularity of software architectures that exploit logic reasoning. The paper details our OWL to OOP mapping mechanism, and it shows the benefits and limitations of OWLOOP through examples concerning a robot in a smart environment.
The intelligent prediction and assessment of financial information risk in the cloud computing model
Venue: arXiv cs.DC 2024
Year: 2024

Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the intersection of cloud computing and financial information processing, identifying risks and challenges faced by financial institutions in adopting cloud technology. It discusses the need for intelligent solutions to enhance data processing efficiency and accuracy while addressing security and privacy concerns. Drawing on regulatory frameworks, the report proposes policy recommendations to mitigate concentration risks associated with cloud computing in the financial industry. By combining intelligent forecasting and evaluation technologies with cloud computing models, the study aims to provide effective solutions for financial data processing and management, facilitating the industry's transition towards digital transformation.
Mean Field Correlated Imitation Learning
Venue: arXiv cs.MA 2024
Year: 2024

We investigate multi-agent imitation learning (IL) within the framework of mean field games (MFGs), considering the presence of time-varying correlated signals. Existing MFG IL algorithms assume demonstrations are sampled from Mean Field Nash Equilibria (MFNE), limiting their adaptability to real-world scenarios. For example, in the traffic network equilibrium influenced by public routing recommendations, recommendations introduce time-varying correlated signals into the game, not captured by MFNE and other existing correlated equilibrium concepts. To address this gap, we propose Adaptive Mean Field Correlated Equilibrium (AMFCE), a general equilibrium incorporating time-varying correlated signals. We establish the existence of AMFCE under mild conditions and prove that MFNE is a subclass of AMFCE. We further propose Correlated Mean Field Imitation Learning (CMFIL), a novel IL framework designed to recover the AMFCE, accompanied by a theoretical guarantee on the quality of the recovered policy. Experimental results, including a real-world traffic flow prediction problem, demonstrate the superiority of CMFIL over state-of-the-art IL baselines, highlighting the potential of CMFIL in understanding large population behavior under correlated signals.
LLeMpower: Understanding Disparities in the Control and Access of Large Language Models
Venue: arXiv cs.CY 2024
Year: 2024

Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.
Service Weaver: A Promising Direction for Cloud-native Systems?
Venue: arXiv cs.SE 2024
Year: 2024

Cloud-native and microservice architectures have taken over the development world by storm. While being incredibly scalable and resilient, microservice architectures also come at the cost of increased overhead to build and maintain. Google's Service Weaver aims to simplify the complexities associated with implementing cloud-native systems by introducing the concept of a single modular binary composed of agent-like components, thereby abstracting away the microservice architecture notion of individual services. While Service Weaver presents a promising approach to streamline the development of cloud-native applications and addresses nearly all significant aspects of conventional cloud-native systems, there are existing tradeoffs affecting the overall functionality of the system. Notably, Service Weaver's straightforward implementation and deployment of components alleviate the overhead of constructing a complex microservice architecture. However, it is important to acknowledge that certain features, including separate code bases, routing mechanisms, resiliency, and security, are presently lacking in the framework.
$\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
Venue: arXiv cs.CV 2024
Year: 2024

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collect a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects and develop a powerful 3D pipeline for the pre-processing of this data. We then present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities, such palm-vein combined with palm-print biometrics. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.
Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation
Venue: arXiv cs.CV 2024
Year: 2024

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this study, we leverage the 3D facial surface to construct a novel orientation-conditioned facial texture video representation which improves the motion robustness of existing video-based facial rPPG estimation methods. Our proposed method achieves a significant 18.2% performance improvement in cross-dataset testing on MMPD over our baseline using the PhysNet model trained on PURE, highlighting the efficacy and generalization benefits of our designed video representation. We demonstrate significant performance improvements of up to 29.6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion, emphasizing the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation. We validate the efficacy of our design decisions and the impact of different video processing steps through an ablation study. Our findings illustrate the potential strengths of exploiting the 3D facial surface as a general strategy for addressing dynamic and unconstrained subject motion in videos. The code is available at https://samcantrill.github.io/orientation-uv-rppg/.
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning
Venue: arXiv cs.IT 2024
Year: 2024

Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scalability challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allows multiple edge devices to transmit uplink signals simultaneously, enabling the parameter server to directly decode the average global model. However, existing AirComp solutions are intrinsically analog, while modern wireless systems predominantly adopt digital modulations. Consequently, careful constellation designs are necessary to accurately decode the sum model updates without ambiguity. In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs. We leverage autoencoder network structures and explore the joint optimization of transmitter and receiver components. Our approach fills an important gap in the context of accurately decoding the sum signal in digital modulation-based AirComp, which can advance the deployment of FL in contemporary wireless systems.
A Generic Approach to Fix Test Flakiness in Real-World Projects
Venue: arXiv cs.SE 2024
Year: 2024

Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order-Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approaches, i.e., leverage program analysis to detect and repair known test flakiness patterns and root causes, failing to generalize. To bridge the gap, we propose FlakyDoctor, a neuro-symbolic technique that combines the power of LLMs-generalizability-and program analysis-soundness-to fix different types of test flakiness. Our extensive evaluation using 873 confirmed flaky tests (332 OD and 541 ID) from 243 real-world projects demonstrates the ability of FlakyDoctor in repairing flakiness, achieving 57% (OD) and 59% (ID) success rate. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than ODRepair, and 17% more OD flaky tests than iFixFlakies. Regardless of underlying LLM, the non-LLM components of FlakyDoctor contribute to 12-31% of the overall performance, i.e., while part of the FlakyDoctor power is from using LLMs, they are not good enough to repair flaky tests in real-world projects alone. What makes the proposed technique superior to related research on test flakiness mitigation specifically and program repair, in general, is repairing 79 previously unfixed flaky tests in real-world projects. We opened pull requests for all cases with corresponding patches; 19 of them were accepted and merged at the time of submission.
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
Venue: arXiv cs.LG 2024
Year: 2024

Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology.
On the Efficiency of Privacy Attacks in Federated Learning
Venue: arXiv cs.CR 2024
Year: 2024

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three novel contributions. First, we systematically evaluate the computational costs for representative privacy attacks in federated learning, which exhibits a high potential to optimize efficiency. Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks. Third, we perform experiments on benchmark datasets and show that our proposed method can significantly reduce computational costs and maintain comparable attack success rates for state-of-the-art privacy attacks in federated learning. We provide the codes on GitHub at https://github.com/mlsysx/EPAFL.
LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization
Venue: arXiv cs.PF 2024
Year: 2024

High-Level Synthesis (HLS) enables rapid prototyping of complex hardware designs by translating C or C++ code to low-level RTL code. However, the testing and evaluation of HLS designs still typically rely on slow RTL-level simulators that can take hours to provide feedback, especially for complex designs. A recent work, LightningSim, helps to solve this problem by providing a simulation workflow one to two orders of magnitude faster than RTL simulation. However, it still exhibits inefficiencies due to several types of redundant computation, making it slow for large design simulation and design space exploration. Addressing these inefficiencies, we introduce LightningSimV2, a much faster and scalable simulation tool. LightningSimV2 features three main innovations. First, we perform compile-time static analysis, exploiting the repetitive structures in HLS designs, e.g., loops, to reduce the simulation workload. Second, we propose a novel graph-based simulation approach, with decoupled simulation graph construction step and graph traversal step, significantly reducing repeated computation. Third, benefiting from the decoupled approach, LightningSimV2 can perform incremental stall analysis extremely fast, enabling highly efficient design space exploration of large numbers of complex hardware parameters, e.g., optimal FIFO depths. Moreover, the DSE is well-suited for parallel computing, further improving the DSE efficiency. Compared with LightningSim, LightningSimV2 achieves up to 3.5x speedup in full simulation and up to 577x speed up for incremental DSE. Our code is open-source on GitHub at https://github.com/sharc-lab/LightningSim/tree/v0.2.0.
Bridging the Gap between Different Vocabularies for LLM Ensemble
Venue: arXiv cs.CL 2024
Year: 2024

Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discrepancies among various LLMs have constrained previous studies to either selecting or blending completely generated outputs. This limitation hinders the dynamic correction and enhancement of outputs during the generation process, resulting in a limited capacity for effective ensemble. To address this issue, we propose a novel method to Ensemble LLMs via Vocabulary Alignment (EVA). EVA bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step. Specifically, we first learn mappings between the vocabularies of different LLMs with the assistance of overlapping tokens. Subsequently, these mappings are employed to project output distributions of LLMs into a unified space, facilitating a fine-grained ensemble. Finally, we design a filtering strategy to exclude models that generate unfaithful tokens. Experimental results on commonsense reasoning, arithmetic reasoning, machine translation, and data-to-text generation tasks demonstrate the superiority of our approach compared with individual LLMs and previous ensemble methods conducted on complete outputs. Further analyses confirm that our approach can leverage knowledge from different language models and yield consistent improvement.
Learning Tracking Representations from Single Point Annotations
Venue: arXiv cs.CV 2024
Year: 2024

Existing deep trackers are typically trained with largescale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5x faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.
Magic Clothing: Controllable Garment-Driven Image Synthesis
Venue: arXiv cs.CV 2024
Year: 2024

We propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task. Aiming at generating customized characters wearing the target garments with diverse text prompts, the image controllability is the most critical issue, i.e., to preserve the garment details and maintain faithfulness to the text prompts. To this end, we introduce a garment extractor to capture the detailed garment features, and employ self-attention fusion to incorporate them into the pretrained LDMs, ensuring that the garment details remain unchanged on the target character. Then, we leverage the joint classifier-free guidance to balance the control of garment features and text prompts over the generated results. Meanwhile, the proposed garment extractor is a plug-in module applicable to various finetuned LDMs, and it can be combined with other extensions like ControlNet and IP-Adapter to enhance the diversity and controllability of the generated characters. Furthermore, we design Matched-Points-LPIPS (MP-LPIPS), a robust metric for evaluating the consistency of the target image to the source garment. Extensive experiments demonstrate that our Magic Clothing achieves state-of-the-art results under various conditional controls for garment-driven image synthesis. Our source code is available at https://github.com/ShineChen1024/MagicClothing.