arXiv subjects

Rui Li

Publications and source records attributed to Rui Li.

At least 307 records · Page 17Linked to original sources

arXiv2024

Equilibrium Strategies of Carbon Emission Reduction in Agricultural Product Supply Chain under Carbon Sink Trading

As global climate change and environmental issues escalate, carbon reduction has emerged as a paramount global concern. Agriculture accounts for approximately 30% of global greenhouse gas emissions, making carbon reduction in this sector crucial for attaining global emission targets. Carbon sink trading serves as a supplementary mechanism to achieve carbon peaking and neutrality, helping to lower the rate ofcarbon emissions. However, practical projects and research in the field of carbon sink trading are not enough currently. This work aims to thoroughly explore the cooperative models between farmers and retailers within the context of agricultural carbon sink trading, as well as the optimal decisions on the efforts to reduce carbon emission for both parties under different cooperative models. To this end, we delve into three distinct cooperative frameworks: the decentralized, the Stackelberg, and the centralized models, each accompanied by a corresponding differentialgame model. The Hamilton-Jacobi-Bellman equation is utilized to investigate the equilibrium strategies of each participant under these three cooperative models, respectively. Furthermore, we conducte numerical simulations to analyze the carbon emission reduction efforts of farmers and retailers, the carbon emission reduction level of the agricultural supply chain, and the overall profits of the supply chain. We also compare scenarios with and without carbon sink trading to provide a comprehensive assessment. The numerical results indicate that the centralized modelexcels in all aspects, followed by the Stackelberg model, with the decentralized model showing the weakest performance. Additionally, carbon sink trading can significantly increase the profits of the participants under each cooperative model.

math.OC↗

arXiv2024

Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method

We explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK -- a software tool for quantum circuit creation and execution on Intel quantum hardware. As hardware access is limited, we use the state vector simulator provided by the SDK. The novelty of this work lies in leveraging classical techniques for the implementation of quantum algorithms. We emphasize the refinement of algorithm implementation and devise strategies to enhance quantum circuits for better control over problem variables. To this end, we adopt classical principles such as modularization, which allows for systematic and controlled execution of complex algorithms. Furthermore, we discuss how the same implementation could be expanded from state vector simulations to execution on quantum hardware with minor adjustments in these configurations.

quant-ph↗

arXiv2024

Online Optimization of DNN Inference Network Utility in Collaborative Edge Computing

Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge video analytics. Nevertheless, as the key knob to improve network utility in CEC, existing works mainly focus on the workload routing strategies among edge devices with the aim of minimizing the routing cost, remaining an open question for joint workload allocation and routing optimization problem from a system perspective. To this end, this paper presents a holistic, learned optimization for CEC towards maximizing the total network utility in an online manner, even though the utility functions of task input rates are unknown a priori. In particular, we characterize the CEC system in a flow model and formulate an online learning problem in a form of cross-layer optimization. We propose a nested-loop algorithm to solve workload allocation and distributed routing iteratively, using the tools of gradient sampling and online mirror descent. To improve the convergence rate over the nested-loop version, we further devise a single-loop algorithm. Rigorous analysis is provided to show its inherent convexity, efficient convergence, as well as algorithmic optimality. Finally, extensive numerical simulations demonstrate the superior performance of our solutions.

cs.DC↗

arXiv2024

The Future of Consumer Edge-AI Computing

In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromising user privacy or quality of experience. To address this, we introduce a novel paradigm centered around EdgeAI-Hub devices, designed to reorganise and optimise compute resources and data access at the consumer edge. To this end, we lay a holistic foundation for the transition from on-device to Edge-AI serving systems in consumer environments, detailing their components, structure, challenges and opportunities.

cs.LG↗

arXiv2024

What can we learn from the experiment of electrostatic conveyor belt for excitons?

Motivated by the experiment of electrostatic conveyor belt for indirect excitons [A. G. Winbow, \textit{et al.}, Phys. Rev. Lett. \textbf{106}, 196806 (2011)], we study the exciton patterns for understanding the exciton dynamics. By analyzing the exciton diffusion, we find that the patterns mainly come from the photoluminescence of two kinds of excitons. The patterns near the laser spot come from the hot excitons which can be regarded as the classical particles. However, the patterns far from the laser spot come from the cooled excitons or coherent excitons. Taking into account of the finite lifetime of Bosonic excitons and of the interactions between them, we build a time-dependent nonlinear Schrödinger equation including the non-Hermitian dissipation to describe the coherent exciton dynamics. The real-time and imaginary-time evolutions are used alternately to solve the Schrödinger equation in order to simulate the exciton diffusion accompanied with the exciton cooling in the moving lattices. By calculating the escape probability, we obtain the transport distances of the coherent excitons in the conveyor which are consistent with the experimental data. The cooling speed of excitons is found to be important in the coherent exciton transport. Moreover, the plateau in the average transport distance cannot be explained by the dynamical localization-delocalization transition induced by the disorders.

cond-mat.quant-gas↗

arXiv2024

Metamorphic Relation Generation: State of the Art and Visions for Future Research

Metamorphic testing has become one mainstream technique to address the notorious oracle problem in software testing, thanks to its great successes in revealing real-life bugs in a wide variety of software systems. Metamorphic relations, the core component of metamorphic testing, have continuously attracted research interests from both academia and industry. In the last decade, a rapidly increasing number of studies have been conducted to systematically generate metamorphic relations from various sources and for different application domains. In this article, based on the systematic review on the state of the art for metamorphic relations' generation, we summarize and highlight visions for further advancing the theory and techniques for identifying and constructing metamorphic relations, and discuss potential research trends in related areas.

cs.SE↗

arXiv2024

Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions

Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propose a two-stage adaptive network (TAN) for semi-supervised cross-domain crater detection. Our network is built on the YOLOv5 detector, where a series of strategies are employed to enhance its cross-domain generalisation ability. In the first stage, we propose an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with significant scale variances. Furthermore, we propose a smoothing hard example mining (SHEM) loss function to address the issue of overfitting on hard examples. In the second stage, we propose a sort-based pseudo-labelling fine-tuning (SPF) strategy for semi-supervised learning to mitigate the distributional differences between source and target domains. For both stages, we employ weak or strong image augmentation to suit different cross-domain tasks. Experimental results on benchmark datasets demonstrate that the proposed network can enhance domain adaptation ability for crater detection under varying scenario distributions.

cs.CV↗

arXiv2024

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge

On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due to the constrained memory and compute resources, and the limited availability of labelled user data. Nonetheless, prior works neglect the data scarcity issue, require excessively long training time (e.g. a few hours), or induce substantial accuracy loss (>10%). In this paper, we propose TinyTrain, an on-device training approach that drastically reduces training time by selectively updating parts of the model and explicitly coping with data scarcity. TinyTrain introduces a task-adaptive sparse-update method that dynamically selects the layer/channel to update based on a multi-objective criterion that jointly captures user data, the memory, and the compute capabilities of the target device, leading to high accuracy on unseen tasks with reduced computation and memory footprint. TinyTrain outperforms vanilla fine-tuning of the entire network by 3.6-5.0% in accuracy, while reducing the backward-pass memory and computation cost by up to 1,098x and 7.68x, respectively. Targeting broadly used real-world edge devices, TinyTrain achieves 9.5x faster and 3.5x more energy-efficient training over status-quo approaches, and 2.23x smaller memory footprint than SOTA methods, while remaining within the 1 MB memory envelope of MCU-grade platforms.

cs.LG↗

arXiv2024

Gait-Adaptive Navigation and Human Searching in field with Cyborg Insect

This study focuses on improving the ability of cyborg insects to navigate autonomously during search and rescue missions in outdoor environments. We propose an algorithm that leverages data from an IMU to calculate orientation and position based on the insect's walking gait. These computed factors serve as essential feedback channels across 3 phases of our exploration. Our method functions without relying on external systems. The results of our trials, carried out in both indoor (4.8 x 6.6 m^2) and outdoor (3.5 x 6.0 m^2) settings, show that the cyborg insect is capable of seeking a human without knowing the human's position. This exploration strategy would help to bring terrestrial cyborg insects closer to practical application in real-life search and rescue (SAR) missions.

cs.RO↗

arXiv2024

Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering

Code Community Question Answering (CCQA) seeks to tackle programming-related issues, thereby boosting productivity in both software engineering and academic research. Recent advancements in Reinforcement Learning from Human Feedback (RLHF) have transformed the fine-tuning process of Large Language Models (LLMs) to produce responses that closely mimic human behavior. Leveraging LLMs with RLHF for practical CCQA applications has thus emerged as a promising area of study. Unlike standard code question-answering tasks, CCQA involves multiple possible answers, with varying user preferences for each response. Additionally, code communities often show a preference for new APIs. These challenges prevent LLMs from generating responses that cater to the diverse preferences of users in CCQA tasks. To address these issues, we propose a novel framework called Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering (ALMupQA) to create user-focused responses. Our approach starts with Multi-perspective Preference Ranking Alignment (MPRA), which synthesizes varied user preferences based on the characteristics of answers from code communities. We then introduce a Retrieval-augmented In-context Learning (RIL) module to mitigate the problem of outdated answers by retrieving responses to similar questions from a question bank. Due to the limited availability of high-quality, multi-answer CCQA datasets, we also developed a dataset named StaCCQA from real code communities. Extensive experiments demonstrated the effectiveness of the ALMupQA framework in terms of accuracy and user preference. Compared to the base model, ALMupQA showed nearly an 11% improvement in BLEU, with increases of 20% and 17.5% in BERTScore and CodeBERTScore, respectively.

cs.CL↗

arXiv2024

LaCon: Late-Constraint Diffusion for Steerable Guided Image Synthesis

Diffusion models have demonstrated impressive abilities in generating photo-realistic and creative images. To offer more controllability for the generation process, existing studies, termed as early-constraint methods in this paper, leverage extra conditions and incorporate them into pre-trained diffusion models. Particularly, some of them adopt condition-specific modules to handle conditions separately, where they struggle to generalize across other conditions. Although follow-up studies present unified solutions to solve the generalization problem, they also require extra resources to implement, e.g., additional inputs or parameter optimization, where more flexible and efficient solutions are expected to perform steerable guided image synthesis. In this paper, we present an alternative paradigm, namely Late-Constraint Diffusion (LaCon), to simultaneously integrate various conditions into pre-trained diffusion models. Specifically, LaCon establishes an alignment between the external condition and the internal features of diffusion models, and utilizes the alignment to incorporate the target condition, guiding the sampling process to produce tailored results. Experimental results on COCO dataset illustrate the effectiveness and superior generalization capability of LaCon under various conditions and settings. Ablation studies investigate the functionalities of different components in LaCon, and illustrate its great potential to serve as an efficient solution to offer flexible controllability for diffusion models.

cs.CV↗

arXiv2024

Challenging theories of dark energy with levitated force sensor

The nature of dark energy is one of the most outstanding problems in physical science, and various theories have been proposed. It is therefore essential to directly verify or rule out these theories experimentally. However, despite substantial efforts in astrophysical observations and laboratory experiments, previous tests have not yet acquired enough accuracy to provide decisive conclusions as to the validity of these theories. Here, using a diamagnetically levitated force sensor, we carry out a test on one of the most compelling explanations for dark energy to date, namely the Chameleon theory, an ultra-light scalar field with screening mechanisms, which couples to normal-matter fields and leaves a detectable fifth force. Our results extend previous results by nearly two orders of magnitude to the entire physical plausible parameter space of cosmologically viable chameleon models. We find no evidence for such a fifth force. Our results decisively rule out the basic chameleon model as a candidate for dark energy. Our work, thus, demonstrates the robustness of laboratory experiments in unveiling the nature of dark energy in the future. The methodology developed here can be further applied to study a broad range of fundamental physics.

gr-qc↗

arXiv2024

Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization

Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogonalization with restricted dimension and homogeneous geometry, leading to deficient modeling capability. In this work, we move beyond these approaches in terms of both dimension and geometry by introducing a powerful framework named GoldE, which features a universal orthogonal parameterization based on a generalized form of Householder reflection. Such parameterization can naturally achieve dimensional extension and geometric unification with theoretical guarantees, enabling our framework to simultaneously capture crucial logical patterns and inherent topological heterogeneity of knowledge graphs. Empirically, GoldE achieves state-of-the-art performance on three standard benchmarks. Codes are available at https://github.com/xxrep/GoldE.

cs.LG↗

arXiv2024

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in industry fail to scale with compute. Inspired by success achieved by Transformers in language and vision domains, we revisit fundamental design choices in recommendation systems. We reformulate recommendation problems as sequential transduction tasks within a generative modeling framework ("Generative Recommenders"), and propose a new architecture, HSTU, designed for high cardinality, non-stationary streaming recommendation data. HSTU outperforms baselines over synthetic and public datasets by up to 65.8% in NDCG, and is 5.3x to 15.2x faster than FlashAttention2-based Transformers on 8192 length sequences. HSTU-based Generative Recommenders, with 1.5 trillion parameters, improve metrics in online A/B tests by 12.4% and have been deployed on multiple surfaces of a large internet platform with billions of users. More importantly, the model quality of Generative Recommenders empirically scales as a power-law of training compute across three orders of magnitude, up to GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model developments, and further paves the way for the first foundational models in recommendations.

cs.LG↗

arXiv2024

The Third Monocular Depth Estimation Challenge

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

cs.CV↗

arXiv2024

Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation

Multi-modal brain images from MRI scans are widely used in clinical diagnosis to provide complementary information from different modalities. However, obtaining fully paired multi-modal images in practice is challenging due to various factors, such as time, cost, and artifacts, resulting in modality-missing brain images. To address this problem, unsupervised multi-modal brain image translation has been extensively studied. Existing methods suffer from the problem of brain tumor deformation during translation, as they fail to focus on the tumor areas when translating the whole images. In this paper, we propose an unsupervised tumor-aware distillation teacher-student network called UTAD-Net, which is capable of perceiving and translating tumor areas precisely. Specifically, our model consists of two parts: a teacher network and a student network. The teacher network learns an end-to-end mapping from source to target modality using unpaired images and corresponding tumor masks first. Then, the translation knowledge is distilled into the student network, enabling it to generate more realistic tumor areas and whole images without masks. Experiments show that our model achieves competitive performance on both quantitative and qualitative evaluations of image quality compared with state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the generated images on downstream segmentation tasks. Our code is available at https://github.com/scut-HC/UTAD-Net.

eess.IV↗

arXiv2024

A Survey on the Memory Mechanism of Large Language Model based Agents

Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this paper, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss ''what is'' and ''why do we need'' the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at \url{https://github.com/nuster1128/LLM_Agent_Memory_Survey}.

cs.AI↗

arXiv2024

Improving Chinese Character Representation with Formation Tree

Learning effective representations for Chinese characters presents unique challenges, primarily due to the vast number of characters and their continuous growth, which requires models to handle an expanding category space. Additionally, the inherent sparsity of character usage complicates the generalization of learned representations. Prior research has explored radical-based sequences to overcome these issues, achieving progress in recognizing unseen characters. However, these approaches fail to fully exploit the inherent tree structure of such sequences. To address these limitations and leverage established data properties, we propose Formation Tree-CLIP (FT-CLIP). This model utilizes formation trees to represent characters and incorporates a dedicated tree encoder, significantly improving performance in both seen and unseen character recognition tasks. We further introduce masking for to both character images and tree nodes, enabling efficient and effective training. This approach accelerates training significantly (by a factor of 2 or more) while enhancing accuracy. Extensive experiments show that processing characters through formation trees aligns better with their inherent properties than direct sequential methods, significantly enhancing the generality and usability of the representations.

cs.CV↗