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

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)

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

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

The origin of memory effects in the crystallization of polyamides: Role of hydrogen bonding

The effect of hydrogen bonding stability on the memory effects in the crystallization of long chain polyamides have been investigated by the self-nucleation calorimetric technique. Self-nucleation is characterized by three domains in decreasing temperature order: complete melting or Domain I, exclusive self-nucleation or Domain II and, self-nucleation and annealing or Domain III. The memory effect is observed in the high temperature range of Domain II (when all crystals are molten, or in Domain IIa). In the low temperature range of Domain II, crystal remnants act as self-seeds (i.e., Domain IIb). The hydrogen bonds between amide groups were detected with FTIR, and a ratio of the content of hydrogen bonded vs. free amide groups could be calculated. The energy needed to break the hydrogen bonds decreases as the self-nucleation temperature (Ts) increases. This means that hydrogen bonds become weaker (and their amount decrease), while the crystalline memory disappears upon crossing from Domain IIa to Domain I. Comparing the widths of Domain IIa in different polyamides, we found for the first time a clear correlation with the relative content of amide groups with respect to methylene groups in the repeat units. In conclusion, we have demonstrated that memory in polyamides is a strong function of hydrogen bonding between chain segments.This work was financially supported by the National Natural Science Foundation of China (No. 21574140) and the National Key R&D Program of China (No. 2017YFB0307600). The SSRF beamlines BL16B1 are acknowledged for kindly providing the beam time and assistance. We thank Dr. François Bouéfrom CEA UMR12 Lab Léon Brillouin-Orphée Neutron Reactor for the good discussion and help on this work. We also acknowledge funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778092

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

Interfacial nucleation in iPP/PB-1 blends promotes the formation of polybutene-1 trigonal crystals

The formation of trigonal Form I\ub4 crystals of polybutene-1 (PB-1) directly from melt has drawn much attention in past decades. In this study, we investigate the fractionated crystallization behavior of PB-1 within droplets formed by blending PB-1 with an excess of isotactic polypropylene (iPP) employing DSC, SEM, in situ synchrotron WAXD and FT-IR. When PB-1 is dispersed into a large number of small size droplets, the heterogeneous nucleation of Form II crystals can be inhibited because the number of droplets is larger than that of active nucleation sites for Form II (i.e., active heterogeneities originally present in bulk PB-1). The nucleation of the finely dispersed PB-1 droplets does not occur homogenously, but at the interface with the iPP matrix, which induces the crystallization of the droplets into Form I\ub4. The crystallization rate of Form I\ub4 at different temperatures was determined by Fourier transform infrared spectroscopy. It was found that trigonal Form I\ub4 crystallizes faster when the content of PB-1 in the blend is lower, and the specific interfacial surface area is larger. The opposite effect has been observed for the kinetics of the metastable Form II formation. It is therefore suggested that Form I\ub4 crystallization is driven by the nucleation of PB-1 at the crystalline iPP surface, which competes with the crystallization of Form II induced by nucleating heterogeneities present in PB-1 droplets

Explaining Time Series via Contrastive and Locally Sparse Perturbations

Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns. Although previous saliency-based methods addressed the challenges, their perturbation may not alleviate the distribution shift issue, which is inevitable especially in heterogeneous samples. We present ContraLSP, a locally sparse model that introduces counterfactual samples to build uninformative perturbations but keeps distribution using contrastive learning. Furthermore, we incorporate sample-specific sparse gates to generate more binary-skewed and smooth masks, which easily integrate temporal trends and select the salient features parsimoniously. Empirical studies on both synthetic and real-world datasets show that ContraLSP outperforms state-of-the-art models, demonstrating a substantial improvement in explanation quality for time series data. The source code is available at \url{https://github.com/zichuan-liu/ContraLSP}.Comment: Accepted by International Conference on Learning Representations (ICLR 2024

Deep learning in channel estimation and signal detection in OFDM systems

This dissertation presents the results of channel estimation and signal detection using deep learning in Orthogonal Frequency Division Multiplexing (OFDM) system. In this dissertation, deep learning is used to deal with wireless OFDM channel. In the existing method, the channel state information is estimated first, and then the estimated channel state information is used to detect / recover the OFDM receiver of the transmission symbol. The method based on deep learning proposed in this dissertation implicitly estimates the channel state information and directly recovers the transmission symbols. In order to solve the channel distortion, the deep learning model first uses the data generated by the simu- lation based on channel statistics for offline training, and then directly restores the data transmitted online. From the simulation results, the method based on deep learning is more robust than the traditional method. In conclusion, deep learning is a useful method in signal detection and channel estimation in complex channel with distortion.Master of Science (Communications Engineering