This paper presents a structured decoding approach to support reliable and efficient container orchestration using large language models (LLMs) in conjunction with the Model Context Protocol (MCP), a standard interface for LLMs to interact with Docker and Kubernetes. We address key challenges in using LLMs for container orchestration: high token overhead from outputs and the risk of generating invalid or unsafe commands. Empirical results demonstrate up to a 76.2% latency reduction.

Large language models (LLMs) have shown immense potential for applications in information retrieval and knowledge management, but their computational and memory demands pose challenges for resource-constrained devices. In response, this work introduces an FPGA-based accelerator designed to improve LLM inference performance on embedded devices. We leverage quantization techniques, asynchronous computation, and a fully-pipelined accelerator to enhance efficiency. Our empirical evaluations, conducted using the TinyLlama 1.1B model on a Xilinx ZCU102 platform, demonstrate a 14.3-15.8x speedup and a 6.1x energy efficiency improvement over running exclusively on the ZCU102 processing system (PS).

The eXtreme Multi-label text Classification (XMC) refers to training a classifier that assigns a text sample with relevant labels from an extremely large-scale label set (e.g., millions of labels). We propose MatchXML, an efficient textlabel matching framework for XMC. We observe that the label embeddings generated from the sparse Term Frequency-Inverse Document Frequency (TF–IDF) features have several limitations. We thus propose label2vec to effectively train the semantic dense label embeddings by the Skip-gram model. The dense label embeddings are then used to build a Hierarchical Label Tree by clustering. In fine-tuning the pre-trained encoder Transformer, we formulate the multi-label text classification as a text-label matching problem in a bipartite graph. We then extract the dense text representations from the fine-tuned Transformer. Besides the fine-tuned dense text embeddings, we also extract the static dense sentence embeddings from a pre-trained Sentence Transformer. Finally, a linear ranker is trained by utilizing the sparse TF–IDF features, the fine-tuned dense text representations, and static dense sentence features. Experimental results demonstrate that MatchXML achieves the state-of-the-art accuracies on five out of six datasets. As for the training speed, MatchXML outperforms the competing methods on all the six datasets. Our source code is publicly available athttps://github.com/huiyegit/MatchXML.

Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks. Among the variants of GNNs, Graph Attention Networks (GATs) improve the performance of many graph learning tasks through a dense attention mechanism. However, real-world graphs are often very large and noisy, and GATs are prone to overfitting if not regularized properly. In this paper, we propose Sparse Graph Attention Networks (SGATs) that learn sparse attention coefficients under an L0-norm regularization, and the learned sparse attentions are then used for all GNN layers, resulting in an edge-sparsified graph. By doing so, we can identify noisy/task-irrelevant edges, and thus perform feature aggregation on most informative neighbors. Extensive experiments on synthetic and real-world (assortative and disassortative) graph learning benchmarks demonstrate the superior performance of SGATs. Furthermore, the removed edges can be interpreted intuitively and quantitatively. To the best of our knowledge, this is the first graph learning algorithm that shows significant redundancies in graphs and edge-sparsified graphs can achieve similar (on assortative graphs) or sometimes higher (on disassortative graphs) predictive performances than original graphs. Our code is available at https://github.com/Yangyeeee/SGAT.

We use a well-known deep neural network framework, called Mask R-CNN, for identification of solar filaments in full-disk H-$\alpha$ images from Big Bear Solar Observatory (BBSO). The image data, collected from BBSO's archive, are integrated with the spatiotemporal metadata of filaments retrieved from the Heliophysics Events Knowledgebase (HEK) system. This integrated data is then treated as the ground-truth in the training process of the model. The available spatial metadata are the output of a currently running filament-detection module developed and maintained by the Feature Finding Team; an international consortium selected by NASA. Despite the known challenges in the identification and characterization of filaments by the existing module, which in turn are inherited into any other module that intends to learn from such outputs, Mask R-CNN shows promising results. Trained and validated on two years worth of BBSO data, this model is then tested on the three following years. Our case-by-case and overall analyses show that Mask R-CNN can clearly compete with the existing module and in some cases even perform better. Several cases of false positives and false negatives, that are correctly segmented by this model are also shown. The overall advantages of using the proposed model are two-fold: First, deep neural networks' performance generally improves as more annotated data, or better annotations are provided. Second, such a model can be scaled up to detect other solar events, as well as a single multi-purpose module. The results presented in this study introduce a proof of concept in benefits of employing deep neural networks for detection of solar events, and in particular, filaments.

We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the Cumulate Relevance Model, a user behavior model recently proposed as a way to extract relevance signal from click-through logs. We propose a novel way of directly learning the ranking function, effectively by-passing the need to have explicit editorial relevance label for each query-document pair. This approach potentially adjusts more closely the ranking function to a variety of user behaviors both at the individual and at the aggregate levels. We investigate two ways of using behavioral model; First, we consider the parametric approach where we learn the estimates of document relevance and use them as targets for the machine learned ranking schemes. In the second, functional approach, we learn a function that maximizes the behavioral model likelihood, effectively by-passing the need to estimate a substitute for document labels. Experiments using user session data collected from a commercial vertical search engine demonstrate the potential of our approach. While in terms of DCG, the editorial model out-perform the behavioral one, online experiments show that the behavioral model is on par --if not superior-- to the editorial model. To our knowledge, this is the first report in the Literature of a competitive behavioral model in a commercial setting

In many Web search engines, a ranking function is selected for deployment mainly by comparing the relevance measurements over candidates. Due to the dynamical nature of the Web, the ranking features and the query and URL distribution on which the ranking functions are built, may change dramatically over time. The actual relevance of the function may degrade, and thus the previous function selection conclusions become invalid. In this work we suggest to select Web ranking functions according to both their relevance and robustness to the changes that may lead to relevance degradation over time. We argue that the ranking robustness can be effectively measured by taking into account the ranking score distribution across search results. We then propose two alternatives to the NDCG metric that both incorporate ranking robustness into ranking function evaluation and selection. A machine learning approach is developed to learn the parameters that control the metric sensitivity to score turbulence, from human-judged preference data.

It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked in the result set during a user search session. We formulate the problem as a global ranking problem, emphasizing the importance of the sequential nature of user clicks, with the goal to predict the relevance labels of all the documents in a search session. This is distinct from conventional learning to rank methods that usually design a ranking model defined on a single document; in contrast, in our model the relational information among the documents as manifested by an aggregation of user clicks is exploited to rank all the documents jointly. In particular, we adapt several sequential supervised learning algorithms, including the conditional random field (CRF), the sliding window method and the recurrent sliding window method, to the global ranking problem. Experiments on the click data collected from a commercial search engine demonstrate that our methods can outperform the baseline models for search results re-ranking.

In commercial search engines, a ranking function is selected for deployment mainly by comparing the relevance measurements over candidates. In this paper we suggest to select Web ranking functions according to both their relevance and robustness to the changes that may lead to relevance degradation over time. We argue that the ranking robustness can be effectively measured by taking into account the ranking score distribution across Web pages. We then improve NDCG with two new metrics and show their superiority in terms of stability to ranking score turbulence and stability in function selection.