Factorized Explainer for Graph Neural Networks

Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. To open the black-box of these deep learning models, post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trained GNN. In this paper, we show analytically that for a large class of explanation tasks, conventional approaches, which are based on the principle of graph information bottleneck (GIB), admit trivial solutions that do not align with the notion of explainability. Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions. We further introduce a novel factorized explanation model with theoretical performance guarantees. The modified GIB is used to analyze the structural properties of the proposed factorized explainer. We conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our proposed factorized explainer.Comment: AAAI 2

The Algorithmic Phase Transition of Random Graph Alignment Problem

We study the graph alignment problem over two independent Erd\H{o}s-R\'enyi graphs on $n$ vertices, with edge density $p$ falling into two regimes separated by the critical window around $p_c=\sqrt{\log n/n}$. Our result reveals an algorithmic phase transition for this random optimization problem: polynomial-time approximation schemes exist in the sparse regime, while statistical-computational gap emerges in the dense regime. Additionally, we establish a sharp transition on the performance of online algorithms for this problem when $p$ lies in the dense regime, resulting in a $\sqrt{8/9}$ multiplicative constant factor gap between achievable and optimal solutions.Comment: 50 page

Automatic Robotic Development through Collaborative Framework by Large Language Models

Despite the remarkable code generation abilities of large language models LLMs, they still face challenges in complex task handling. Robot development, a highly intricate field, inherently demands human involvement in task allocation and collaborative teamwork . To enhance robot development, we propose an innovative automated collaboration framework inspired by real-world robot developers. This framework employs multiple LLMs in distinct roles analysts, programmers, and testers. Analysts delve deep into user requirements, enabling programmers to produce precise code, while testers fine-tune the parameters based on user feedback for practical robot application. Each LLM tackles diverse, critical tasks within the development process. Clear collaboration rules emulate real world teamwork among LLMs. Analysts, programmers, and testers form a cohesive team overseeing strategy, code, and parameter adjustments . Through this framework, we achieve complex robot development without requiring specialized knowledge, relying solely on non experts participation

Hierarchical Large Language Models in Cloud Edge End Architecture for Heterogeneous Robot Cluster Control

Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi agent strategy generation and motion control, we propose an innovative architecture that employs the concept of a cloud edge end hierarchical structure. By leveraging multiple large language models with distinct areas of expertise, we can efficiently generate strategies and perform task decomposition. Introducing the cosine similarity approach,aligning task decomposition instructions with robot task sequences at the vector level, we can identify subtasks with incomplete task decomposition and iterate on them multiple times to ultimately generate executable machine task sequences.The robot is guided through these task sequences to complete tasks of higher complexity. With this architecture, we implement the process of natural language control of robots to perform complex tasks, and successfully address the challenge of multi agent execution of open tasks in open scenarios and the problem of task decomposition

Comparison of staged-stent and stent-assisted coiling technique for ruptured saccular wide-necked intracranial aneurysms: Safety and efficacy based on a propensity score-matched cohort study

BackgroundApplication of stent-assisted coiling and FD in acute phase of ruptured wide-necked aneurysms is relatively contraindicated due to the potential risk of ischemic and hemorrhagic complications. Scheduled stenting after initial coiling has emerged as an alternative paradigm for ruptured wide-necked aneurysms. The objective of this study is to evaluate the safety and efficacy of a strategy of staged stent-assisted coiling in acutely ruptured saccular wide-necked intracranial aneurysms compared with conventional early stent-assisted coiling strategy via propensity score matching in a high-volume center.MethodsA retrospective review of patients with acutely ruptured saccular wide-necked intracranial aneurysms who underwent staged stent-assisted coiling or conventional stent-assisted coiling from November 2014 to November 2019 was performed. Perioperative procedure-related complications and clinical and angiographic follow-up outcomes were compared.ResultsA total of 69 patients with staged stent-assisted coiling and 138 patients with conventional stent-assisted coiling were enrolled after 1:2 propensity score matching. The median interval time between previous coiling and later stenting was 4.0 weeks (range 3.5–7.5 weeks). No rebleeding occurred during the intervals. The rate of immediate complete occlusion was lower with initial coiling before scheduled stenting than with conventional stent-assisted coiling (21.7 vs. 60.9%), whereas comparable results were observed at follow-up (82.5 vs. 72.9%; p = 0.357). The clinical follow-up outcomes, overall procedure-related complications and procedure-related mortality between the two groups demonstrated no significant differences (P = 0.232, P = 0.089, P = 0.537, respectively). Multivariate analysis showed that modified Fisher grades (OR = 2.120, P = 0.041) were independent predictors for overall procedure-related complications and no significant predictors for hemorrhagic and ischemic complications.ConclusionsStaged stent-assisted coiling is a safe and effective treatment strategy for acutely ruptured saccular wide-necked intracranial aneurysms, with comparable complete occlusion rates, recurrence rates at follow-up and overall procedure-related complication rates compared with conventional stent-assisted coiling strategy. Staged stent-assisted coiling could be an alternative treatment option for selected ruptured intracranial aneurysms in the future

Real-Time Heart Rate Detection Method Based on 77 GHz FMCW Radar

This paper proposes a real-time heart rate detection method based on 77 GHz FMCW radar. Firstly, the method establishes a new motion model according to respiratory and heartbeat rules, and extracts the motion signals of the chest and the abdomen; then, the random body motion (RBM) signal is eliminated by a combination of polynomial fitting and recursive least squares (RLS) adaptive filtering; lastly, multi-detection-point adaptive harmonics cancellation (AHC) is used to eliminate respiratory harmonics. In addition, the method introduces a spectrum analysis algorithm based on linear predictive coding (LPC). The experimental results show that the method can effectively eliminate the RBM signal and respiratory harmonics, and that the average real-time heart rate detection error rate is 2.925%

Overview of Integrated Electric Motor Drives: Opportunities and Challenges

Integrated Motor Drives (IMDs) have recently received extensive attention. In electric vehicles (EVs), electric propulsion aircraft, and ship propulsion systems, integrated motors have the great potential to replace traditional motors with the distinct merits of compact size, high power density, high efficiency, and high-cost effectiveness. This paper investigates and reviews integrated motor drives’ development and critical technologies. It not only reveals the research progress of the motor structure, converter, volume optimization, heat dissipation design, and weakening electromagnetic interference of integrated motor drives but also explores in detail the applications of wide-bandgap semiconductors and the integration of LCL filters. In addition, this paper also puts forward the concept of integrated motor drive integration level and establishes a corresponding quantitative method to evaluate IMDs integration level. In the future, integrated wireless motor drives will have a broad scope of research and application. IMDs systems will play an important role in applications requiring high power density, providing solutions to motor system size and heat dissipation problems. This overview will help clarify the opportunities, challenges, and future development of IMDs

Depletion of G9A attenuates imiquimod-induced psoriatic dermatitis via targeting EDAR-NF-κB signaling in keratinocyte

Abstract Psoriasis is a common and recurrent inflammatory skin disease characterized by inflammatory cells infiltration of the dermis and excessive proliferation, reduced apoptosis, and abnormal keratosis of the epidermis. In this study, we found that G9A, an important methyltransferase that mainly mediates the mono-methylation (me1) and di-methylation (me2) of histone 3 lysine 9 (H3K9), is highly expressed in lesions of patients with psoriasis and imiquimod (IMQ)-induced psoriasis-like mouse model. Previous studies have shown that G9A is involved in the pathogenesis of various tumors by regulating apoptosis, proliferation, differentiation, and invasion. However, the role of G9A in skin inflammatory diseases such as psoriasis remains unclear. Our data so far suggest that topical administration of G9A inhibitor BIX01294 as well as keratinocyte-specific deletion of G9A greatly alleviated IMQ-induced psoriatic alterations in mice for the first time. Mechanistically, the loss function of G9A causes the downregulation of Ectodysplasin A receptor (EDAR), consequently inhibiting the activation of NF-κB pathway, resulting in impaired proliferation and increased apoptosis of keratinocytes, therefore ameliorating the psoriatic dermatitis induced by IMQ. In total, we show that inhibition of G9A improves psoriatic-like dermatitis mainly by regulating cell proliferation and apoptosis rather than inflammatory processes, and that this molecule may be considered as a potential therapeutic target for keratinocyte hyperproliferative diseases such as psoriasis

Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot’s proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution

Preparation of Palladium/Silver-Coated Polyimide Nanotubes: Flexible, Electrically Conductive Fibers

A simple and practical method for coating palladium/silver nanoparticles on polyimide (PI) nanotubes is developed. The key steps involved in the process are silver ion exchange/reduction and displacement reactions between silver and palladium ions. With the addition of silver, the conductivity of the PI nanotubes is greatly enhanced. Further, the polyimide nanotubes with a dense, homogeneous coating of palladium nanoparticles remain flexible after heat treatment and show the possibility for use as highly efficient catalysts. The approach developed here is applicable for coating various noble metals on a wide range of polymer matrices, and can be used for obtaining polyimide nanotubes with metal loaded on both the inner and outer surface