Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for diverse emerging applications, as it enables greater cost-effectiveness and reduced latency. In this work, we introduceMixture-of-Edge-Experts (MoE2), a novel collaborative inference framework for edge LLMs. We formulate a joint gating and expert selection problem to optimize inference performance under energy and latency constraints. Unlike conventional MoE problems, LLM expert selection becomes significantly more challenging due to the combinatorial nature and the heterogeneity of edge LLMs across various attributes. To this end, we propose a two-level expert selection mechanism through which we uncover an optimality-preserving property of gating parameters across expert selections. This property enables the decomposition of the training and selection processes, significantly reducing complexity. Furthermore, we leverage the objective’s monotonicity and design a discrete monotonic optimization algorithm for optimal expert selection. We implement edge servers with NVIDIA Jetson AGX Orins and NVIDIA RTX 4090 GPUs, and perform extensive experiments. Our results validate the performance improvements for various LLM models and show that our MoE2 method can achieve optimal trade-offs among different delay and energy budgets, and outperforms baselines under various system resource constraints. We further demonstrate its strong robustness in dynamic, non-stationary environments and its effectiveness in achieving load balancing.

Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCPA, the ability to perform machine unlearning has become crucial for Random Forests. However, insufficient attention was paid to this topic, and existing approaches face difficulties in being applied to real-world scenarios. Addressing this gap, we propose the DynFrs framework designed to enable efficient machine unlearning in Random Forests while preserving predictive accuracy. Dynfrs leverages subsampling method Occ(q) and a lazy tag strategy Lzy, and is still adaptable to any Random Forest variant. In essence, Occ(q) ensures that each sample in the training set occurs only in a proportion of trees so that the impact of deleting samples is limited, and Lzy delays the reconstruction of a tree node until necessary, thereby avoiding unnecessary modifications on tree structures. In experiments, applying Dynfrs on Extremely Randomized Trees yields substantial improvements, achieving orders of magnitude faster unlearning performance and better predictive accuracy than existing machine unlearning methods for Random Forests.

Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for emerging applications, enabling greater cost-effectiveness and reduced latency. In this work, we introduce Mixture-of-Edge-Experts (MoE2), a collaborative inference framework for edge LLMs. We formulate the joint gating and expert selection problem to optimize inference under energy and latency constraints. Unlike conventional MoE problems, expert selection here is more challenging due to the combinatorial nature and heterogeneity of edge LLMs. To address this, we propose a two-level expert selection mechanism and uncover an optimality-preserving property of gating parameters that decouples training and selection, reducing complexity. We further leverage the objective's monotonicity and design a discrete monotonic optimization algorithm. Implemented on Jetson Orin and RTX 4090 platforms, MoE2 achieves optimal trade-offs across delay and energy budgets, outperforming baselines under various resource constraints.

Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention was paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAlT, paving the way towards more effective and insightful KG analysis techniques.

The Solar Backscatter Ultraviolet Sounder (SBUS) is one of the 11 main payload instruments onboard Feng Yun-3 (FY-3), the second generation of Chinese polar orbit meteorological satellites. This paper presents the results of SBUS instrument calibration, and data and product validation during the prelaunch and postlaunch periods. Topics include the instrument prelaunch calibration and characterization, in-orbit monitoring, validation of the ozone profiles retrieved from the FY-3 SBUS measurements, and an application of the retrievals to monitoring the 2011 Arctic ozone depletion. For the prelaunch calibration of SBUS, the estimated uncertainty of laboratory calibration is approximately 4.7%. The in-orbit solar irradiance measurements indicate that the diffuser reflectivity degraded approximately 15% for the 252-nm channel, and 3% to 5% for the other 11 channels during a 12-mo period. Using ozone vertical profiles retrieved from National Oceanic and Atmospheric Administration Solar Backscatter Ultraviolet (SBUV)/2s as a “truth,” the initial comparison of ozone profiles between FY-3 SBUS and SBUV/2s finds that the relative percent bias of the FY-3 SBUS with the SBUV/2 results is good. The averaged differences range over to ±7% for FY-3A SBUS and ±6% for FY-3B SBUS. The SBUS ozone profile retrievals reveal that the spring 2011 Arctic ozone depletion mainly resulted from a sharp ozone decrease in the upper troposphere to lower stratosphere, which accounts for 70% to 80% of the total ozone loss.