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Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the dynamic heterogeneous networks (DHNs) environment. Moreover, current state-of-the-art distributed anomaly detection methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for DHNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a Multi-Scale Semanticized Anomaly Detection Model (MSADM), incorporating semantic rule trees with an attention mechanism to address the multi-scale anomaly detection problem in DHNs. Secondly, a chain-of-thought-based large language model is embedded in downstream to adaptively analyze the fault detection results and produce an analysis report with detailed fault information and optimization strategies. Experimental results show that the accuracy of our proposed MSADM for heterogeneous network entity anomaly detection is as high as 91.31\%.

This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the intricate physical-chemical properties of small molecules, their drugability for pharmacology, alongside the functional attributes of enzymes and crystal materials, underscoring the relevance and applicability across biological and chemical domains.The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as powerful tools for scientific discovery and innovation when equipped with domain-specific prompts. The study also discusses limitations and future directions for domain-specific prompt engineering development., Comment: 43 pages, 17 figures

In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The advent of Machine Learning (ML) has ushered in a new era in the field of catalyst optimization, offering potential solutions to the shortcomings of traditional techniques. However, existing methods fail to effectively harness the wealth of information contained within the burgeoning body of scientific literature on catalyst synthesis. To address this gap, this study proposes an innovative Artificial Intelligence (AI) workflow that integrates Large Language Models (LLMs), Bayesian optimization, and an active learning loop to expedite and enhance catalyst optimization. Our methodology combines advanced language understanding with robust optimization strategies, effectively translating knowledge extracted from diverse literature into actionable parameters for practical experimentation and optimization. In this article, we demonstrate the application of this AI workflow in the optimization of catalyst synthesis for ammonia production. The results underscore the workflow's ability to streamline the catalyst development process, offering a swift, resource-efficient, and high-precision alternative to conventional methods., Comment: 31 pages, 7 figures

The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline $\beta$-Ga$_2$O$_3$ and $\kappa$-Ga$_2$O$_3$, and apply them to perform homogeneous nonequilibrium molecular dynamics simulations to predict their LTCs. The LTC of $\beta$-Ga$_2$O$_3$ was determined to be 10.3 $\pm$ 0.2 W/(m K), 19.9 $\pm$ 0.2 W/(m K), and 12.6 $\pm$ 0.2 W/(m K) along [100], [010], and [001], respectively, aligning with previous experimental measurements. For the first time, we predict the LTC of $\kappa$-Ga$_2$O$_3$ along [100], [010], and [001] to be 4.5 $\pm$ 0.0 W/(m K), 3.9 $\pm$ 0.0 W/(m K), and 4.0 $\pm$ 0.1 W/(m K), respectively, showing a nearly isotropic thermal transport property. The reduced LTC of $\kappa$-Ga$_2$O$_3$ versus $\beta$-Ga$_2$O$_3$ stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the $\beta$ phase exhibits a typical temperature dependence slightly stronger than $\sim T^{-1}$, whereas the $\kappa$ phase shows a weaker temperature dependence, ranging from $\sim T^{-0.5}$ to $\sim T^{-0.7}$., Comment: 8 pages, 7 figures

This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field programmable gate array (FPGA) and application specific integrated circuit (ASIC) working in heterogeneous parallelization. To be specific, a multiplication-less neural network (NN) is deployed on the non-von Neumann (NvN)-based ASIC (SilTerra 180 nm process) to evaluate atomic forces, which is the most computationally expensive part of MD. All other calculations of MD are done using FPGA (Xilinx XC7Z100). It is shown that, to achieve similar-level accuracy, the proposed NvN-based system based on low-end fabrication technologies (180 nm) is 1.6x faster and 10^2-10^3x more energy efficiency than state-of-the-art vN based MLMD using graphics processing units (GPUs) based on much more advanced technologies (12 nm), indicating superiority of the proposed NvN-based heterogeneous parallel architecture.

Memory resources in data centers generally suffer from low utilization and lack of dynamics. Memory disaggregation solves these problems by decoupling CPU and memory, which currently includes approaches based on RDMA or interconnection protocols such as Compute Express Link (CXL). However, the RDMA-based approach involves code refactoring and higher latency. The CXL-based approach supports native memory semantics and overcomes the shortcomings of RDMA, but is limited within rack level. In addition, memory pooling and sharing based on CXL products are currently in the process of early exploration and still take time to be available in the future. In this paper, we propose the CXL over Ethernet approach that the host processor can access the remote memory with memory semantics through Ethernet. Our approach can support native memory load/store access and extends the physical range to cross server and rack levels by taking advantage of CXL and RDMA technologies. We prototype our approach with one server and two FPGA boards with 100 Gbps network and measure the memory access latency. Furthermore, we optimize the memory access path by using data cache and congestion control algorithm in the critical path to further lower access latency. The evaluation results show that the average latency for the server to access remote memory is 1.97 {\mu}s, which is about 37% lower than the baseline latency in the industry. The latency can be further reduced to 415 ns with cache block and hit access on FPGA.

In this work, we present a theory for topological phases for quantum systems on tree graphs. Conventionally, topological phases of matter have been studied in regular lattices, but also in quasicrystals and amorphous settings. We consider specific generalizations of regular tree graphs, and explore their topological properties. Unlike conventional systems, infinite quantum trees are not finite-dimensional, allowing for novel phenomena. We find a proliferation of topological zero modes present throughout the entire system, indicating that the bulk also acts as a boundary. We then go on to show that only three symmetry classes host stable topological phases in contrast to the usual five symmetry classes per dimension. Finally, we introduce what we call the Su-Schrieffer-Heeger tree which is topologically non-trivial even in the absence of inner degrees of freedom and does not possess any gapped trivial phases. We realize this system in an electronic circuit and show that our theory matches with experiments., Comment: 5+5 pages, 5 figures

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an optical method has been developed for simple, high throughput, and accurate determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$ films in a broad thickness range. The method is based on quantitative analysis of layer-dependent white light reflection spectra, revealing that the reflection peak intensity can be used as a clear indicator for determining the layer number. The simple yet robust method will facilitate the fundamental study on layer-dependent optical, electrical, and thermal properties and device applications of 2D materials. The technique can also be readily combined with photoluminescence and Raman spectroscopies to study other layer-dependent physical properties of 2D materials.