Resource Allocation and Secure Wireless Communication in the Large Model-based Mobile Edge Computing System

With the rapid advancement of large models and mobile edge computing, transfer learning, particularly through fine-tuning, has become crucial for adapting models to downstream tasks. Traditionally, this requires users to share their data with model owners for fine-tuning, which is not only costly but also raises significant privacy concerns. Furthermore, fine-tuning large-scale models is computationally intensive and often impractical for many users. To tackle these challenges, we introduce a system that combines offsite-tuning with physical-layer security, which provides local data owners with a lightweight adapter and a compressed emulator. Data owners then fine-tune the adapter locally and securely send it back to the model owners through a confidential channel for integration, ensuring privacy and resource conservation. Our paper focuses on optimizing computational resource allocation among data owners and the large model owner deployed on edge, and on the compression ratio of adapters. We incorporate a secrecy uplink channel to maximize the utility that we defined while minimizing system costs like energy consumption and delay. The optimization uses the Dinkelbach algorithm, fractional programming, successive convex approximation and alternating optimization. Experiments demonstrate our algorithm's superiority over existing methods

Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation

Scribble-supervised medical image segmentation tackles the limitation of sparse masks. Conventional approaches alternate between: labeling pseudo-masks and optimizing network parameters. However, such iterative two-stage paradigm is unwieldy and could be trapped in poor local optima since the networks undesirably regress to the erroneous pseudo-masks. To address these issues, we propose a non-iterative method where a stream of varying (pacing) pseudo-masks teach a network via consistency training, named PacingPseudo. Our motivation lies first in a non-iterative process. Interestingly, it can be achieved gracefully by a siamese architecture, wherein a stream of pseudo-masks naturally assimilate a stream of predicted masks during training. Second, we make the consistency training effective with two necessary designs: (i) entropy regularization to obtain high-confidence pseudo-masks for effective teaching; and (ii) distorted augmentations to create discrepancy between the pseudo-mask and predicted-mask streams for consistency regularization. Third, we devise a new memory bank mechanism that provides an extra source of ensemble features to complement scarce labeled pixels. The efficacy of the proposed PacingPseudo is validated on three public medical image datasets, including the segmentation tasks of abdominal multi-organs, cardiac structures, and myocardium. Extensive experiments demonstrate our PacingPseudo improves the baseline by large margins and consistently outcompetes several previous methods. In some cases, our PacingPseudo achieves comparable performance with its fully-supervised counterparts, showing the feasibility of our method for the challenging scribble-supervised segmentation applications. The code and scribble annotations will be publicly available.Comment: 12 pages, 8 figure

Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

With the advent of sophisticated artificial intelligence (AI) technologies, the proliferation of deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. This paper provides an overview of the current literature. Within the frontier AI's crucial application in developing defense mechanisms for detecting deepfakes, we highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We explore the multifaceted implications of LM-based GenAI on society, politics, and individual privacy violations, underscoring the urgent need for robust defense strategies. To address these challenges, in this study, we introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives to mitigate the risks associated with AI-Generated Content (AIGC). By leveraging multi-modal analysis, digital watermarking, and machine learning-based authentication techniques, we propose a defense mechanism adaptable to AI capabilities of ever-evolving nature. Furthermore, the paper advocates for a global consensus on the ethical usage of GenAI and implementing cyber-wellness educational programs to enhance public awareness and resilience against m/disinformation. Our findings suggest that a proactive and collaborative approach involving technological innovation and regulatory oversight is essential for safeguarding netizens while interacting with cyberspace against the insidious effects of deepfakes and GenAI-enabled m/disinformation campaigns.Comment: This paper appears in IEEE International Conference on Computer and Applications (ICCA) 202

Peculiar self-nucleation behavior of a polybutene-1/ethylene random copolymer

Unformatted post-print version of the accepted articleThe self-nucleation behavior of a polybutene-1/ethylene random copolymer, P(B1-ran-E), which undergoes a complex crystal-crystal transition behavior, has been studied in detail. Similar to PE random copolymers, this material shows a strong melt memory effect even above equilibrium melting point of PB-1 homopolymer. Different polymorphic forms can be obtained when P(B1-ran-E) is cooled from different self-nucleation Domains. The trigonal form I' could only be nucleated in the presence of remaining form I crystals via self-seeding, while the melt memory in Domain IIa could only act as self-nuclei for kinetically favored form II. Furthermore, observations from optical microscopy illustrated that melt memory is able to enhance nucleation density but it does not affect the spherulitic growth rate.Financial supports from the National Science Foundation of China (Grant No. U1510207) and the Key Program for Coal-based Science and Technology of Shanxi Province (MH-2014-08) are gratefully acknowledged. We would like to acknowledge the financial support from the BIODEST project, this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 778092. AJM would also like to acknowledge funding from the Basque Government through grant IT1309-19

Promotion of Self-Nucleation with Latent Form i Nuclei in Polybutene-1 and Its Copolymer

The formation of form I nuclei of polybutene-1 (PB-1) and its copolymer with polyethylene (PB1-ran-PE) has been studied by means of modified self-nucleation protocols. Even when the self-nucleation temperature was high enough and all form II crystals melt, recrystallization can be accelerated if the melt-crystallized sample was annealed at low temperatures (below 60 \ub0C for PB-1 and 75 \ub0C for PB1-ran-PE) for just 3 min. These results suggest the formation of latent form I nuclei within form II crystals. This hypothesis is consistent with the observed growth of a small amount of form I crystals during heating, after previous annealing at temperature lower than 20 \ub0C. In addition, a peculiar phenomenon was found in PB1-ran-PE, as both form II and form I\u2032 can be induced by the presence of latent form I nuclei, due to cross-nucleation and self-nucleation effects, respectively. The final ratio of the two kinds of crystal forms is a result of the competition between the two nucleation rates, which strongly depend on crystallization temperature. In this work, we have shown that careful design of novel self-nucleation protocols can yield evidence of the early stages of form II to form I transition, even when the degree of transformed crystals is below the limit of detection of conventional techniques sensitive to crystalline order (DSC, WAXD, and FTIR)

Polymer Physics behind the Gel-Spinning of UHMWPE Fibers

Gel-spinning of ultra-high molecular weight polyethylene (UHMWPE) fibers has attracted great interest in academia and industry since its birth and commercialization in the 1980s, due to unique properties such as high modulus, low density, and excellent chemical resistance. However, the high viscosity and long relaxation time greatly complicate processing. In industry, solvents, like decalin and paraffin oil, usually disentangle the physical networks and promote final drawability. From extruding the polymer solution to post-solid-stretching, many polymer physics problems that accompany high-modulus fiber gel-spinning should be understood and addressed. In this review, by detailed discussions about the effect of entanglements and intracrystalline chain dynamics on the mechanical properties of UHMWPE, theoretical descriptions of the structure formation of disentangled UHMWPE crystals, and the origin of high modulus and strength of final fibers are provided. Several physical intrinsic key factors are also discussed, revealing why UHMWPE is an ideal material for producing high-performance fibers.Z.W. acknowledges the financial support from the National Natural Science Foundation of China (Grant No. 22303052). X.J. and C.Z. are grateful for the support from the National Natural Science Foundation of China (No. 52373041, 51973118, 22175121), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08C642). Shenzhen Science and Technology Program (JCYJ20220818095810022). Z.W. thanks for the fruitful discussion with Prof. Piet Lemstra (Jiangnan University). L.S. and A.J.M. would like to acknowledge the support of the Basque Government through grant IT1503-22

How entanglements determine the morphology of semicrystalline polymers

Crystallization of polymers from entangled melts generally leads to the formation of semicrystalline materials with a nanoscopic morphology consisting of stacks of alternating crystalline and amorphous layers. The factors controlling the thickness of the crystalline layers are well studied; however, there is no quantitative understanding of the thickness of the amorphous layers. We elucidate the effect of entanglements on the semicrystalline morphology by the use of a series of model blends of high-molecular-weight polymers with unentangled oligomers leading to a reduced entanglement density in the melt as characterized by rheological measurements. Small-angle X-ray scattering experiments after isothermal crystallization reveal a reduced thickness of the amorphous layers, while the crystal thickness remains largely unaffected. We introduce a simple, yet quantitative model without adjustable parameters, according to which the measured thickness of the amorphous layers adjusts itself in such a way that the entanglement concentration reaches a specific maximum value. Furthermore, our model suggests an explanation for the large supercooling that is typically required for crystallization of polymers if entanglements cannot be dissolved during crystallization

Crystallization, Orientation, and Solid−Solid Crystal Transition of Polybutene‑1 Confined within Nanoporous Alumina

Unformatted post-print version of the accepted articleThe effect of confinement on the crystallization, crystal orientation, and polymorphic crystal transition of bulk and infiltrated polybutene-1 (PB-1) within nanoporous alumina templates (AAO) were studied. After cooling from the melt, PB-1 within AAO templates crystallized into the tetragonal Form II directly. The nucleation process inside the AAO pores was probably homogeneous when pore sizes were below 200 nm. The crystal orientation of Form II was investigated by grazing angle X-ray scattering. Form II to I transition was investigated as a function of time and modeled with the Avrami equation. The rate of Form II to I transition for infiltrated PB-1 within 400 nm AAO was unexpectedly higher than that of the bulk. The stress generated due to the mismatch of the thermal expansion coefficients between PB-1 and AAO greatly enhanced the nucleation of Form I within the Form II matrix. A slower Form II to I transition was observed when the pore diameter of AAO decreased. The transition degree decreased with decreasing pore diameter and was completely inhibited for PB-1 infiltrated within the 30 nm AAO template. A stable Form II interfacial layer with a thickness of ~ 12 nm was postulated to account for this phenomenon.This work is supported by the National Key R&D Program of China (2017YFE0117800) and the National Natural Science Foundation of China (21873109 and 21922308). We acknowledge sponsorship from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778092. G. L. is grateful to the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y201908)

RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models

Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. However, current methods are still reliant on manual workflow settings and do not unleash LLMs' decision-making and environment interaction capabilities. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools. Our framework combines a variety of enhancements, including a unique Self-Consistency for action trajectories, and a suite of methods for context management, stabilization, and importing domain knowledge. Our experiments show RCAgent's evident and consistent superiority over ReAct across all aspects of RCA -- predicting root causes, solutions, evidence, and responsibilities -- and tasks covered or uncovered by current rules, as validated by both automated metrics and human evaluations. Furthermore, RCAgent has already been integrated into the diagnosis and issue discovery workflow of the Real-time Compute Platform for Apache Flink of Alibaba Cloud

Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) have been widely used due to their advanced reasoning skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual understanding and reasoning capabilities. We investigate whether state-of-the-art MLLMs can handle embodied decision-making in an end-to-end manner and whether collaborations between LLMs and MLLMs can enhance decision-making. To address these questions, we introduce a new benchmark called PCA-EVAL, which evaluates embodied decision-making from the perspectives of Perception, Cognition, and Action. Additionally, we propose HOLMES, a multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs to gather multimodal information for informed decision-making. We compare end-to-end embodied decision-making and HOLMES on our benchmark and find that the GPT4-Vision model demonstrates strong end-to-end embodied decision-making abilities, outperforming GPT4-HOLMES in terms of average decision accuracy (+3%). However, this performance is exclusive to the latest GPT4-Vision model, surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate that powerful MLLMs like GPT4-Vision hold promise for decision-making in embodied agents, offering new avenues for MLLM research. Code and data are open at https://github.com/pkunlp-icler/PCA-EVAL/.Comment: FMDM@NeurIPS2023, Code and data: https://github.com/pkunlp-icler/PCA-EVAL