Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it difficult to guarantee logical coherence and rigor. To address these limitations, we propose Thinker, a hierarchical thinking model for deep search through multi-turn interaction, making the reasoning process supervisable and verifiable. It decomposes complex problems into independently solvable sub-problems, each dually represented in both natural language and an equivalent logical function to support knowledge base and web searches. Concurrently, dependencies between sub-problems are passed as parameters via these logical functions, enhancing the logical coherence of the problem-solving process. To avoid unnecessary external searches, we perform knowledge boundary determination to check if a sub-problem is within the LLM's intrinsic knowledge, allowing it to answer directly. Experimental results indicate that with as few as several hundred training samples, the performance of Thinker is competitive with established baselines. Furthermore, when scaled to the full training set, Thinker significantly outperforms these methods across various datasets and model sizes.

Traditional clustering algorithms, such as K-Means, output a clustering that is disjoint and exhaustive, i.e., every single data point is assigned to exactly one cluster. However, in many real-world datasets, clusters can overlap and there are often outliers that do not belong to any cluster. While this is a well-recognized problem, most existing algorithms address either overlap or outlier detection and do not tackle the problem in a unified way. In this paper, we propose an intuitive objective function, which we call the NEO-K-Means (Non-Exhaustive, Overlapping K-Means) objective, that captures the issues of overlap and non-exhaustiveness in a unified manner. Our objective function can be viewed as a reformulation of the traditional K-Means objective, with easy-to-understand parameters that capture the degrees of overlap and non-exhaustiveness. By considering an extension to weighted kernel K-Means, we show that we can also apply our NEO-K-Means idea to overlapping community detection, which is an important task in network analysis. To optimize the NEO-K-Means objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using low-rank semidefinite programming techniques. Our experimental results show that the new objective and algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness; for the case of graphs, we show that our method outperforms state-of-the-art overlapping community detection algorithms.

Road detection in aerial images is a crucial technique for visual navigation and scene understanding in relation to unmanned aerial vehicles (UAVs). A shape-aware road detection method for aerial images is proposed in this paper. It first employs the stroke width transform (SWT) and a geodesic distance based superpixel clustering to generate proposal regions. Then, a shape classification is responsible for selecting all potential road segments from the proposal regions which appear to be long and with consistent width. All road segments selected are clustered into several groups based on width and color features. A global graph based labeling model is then applied based on each group to remove potential background clutters, as well as to generate the final output. Experiments on two public datasets demonstrate that the proposed method can handle more diverse and challenging road scenes and needs less pre-training, leading to better performance compared to conventional methods.

Clustering is one of the most fundamental and important tasks in data mining. Traditional clustering algorithms, such as K-means, assign every data point to exactly one cluster. However, in real-world datasets, the clusters may overlap with each other. Furthermore, often, there are outliers that should not belong to any cluster. We recently proposed the NEO-K-Means (Non-Exhaustive, Overlapping K-Means) objective as a way to address both issues in an integrated fashion. Optimizing this discrete objective is NP-hard, and even though there is a convex relaxation of the objective, straightforward convex optimization approaches are too expensive for large datasets. A practical alternative is to use a low-rank factorization of the solution matrix in the convex formulation. The resulting optimization problem is non-convex, and we can locally optimize the objective function using an augmented Lagrangian method. In this paper, we consider two fast multiplier methods to accelerate the convergence of an augmented Lagrangian scheme: a proximal method of multipliers and an alternating direction method of multipliers (ADMM). For the proximal augmented Lagrangian or proximal method of multipliers, we show a convergence result for the non-convex case with bound-constrained subproblems. These methods are up to 13 times faster—with no change in quality—compared with a standard augmented Lagrangian method on problems with over 10,000 variables and bring runtimes down from over an hour to around 5 minutes.

Clustering is one of the most fundamental tasks in data mining. To analyze complex real-world data emerging in many data-centric applications, the problem of non-exhaustive, overlapping clustering has been studied where the goal is to find overlapping clusters and also detect outliers simultaneously. We propose a novel convex semidefinite program (SDP) as a relaxation of the non-exhaustive, overlapping clustering problem. Although the SDP formulation enjoys attractive theoretical properties with respect to global optimization, it is computationally intractable for large problem sizes. As an alternative, we optimize a low-rank factorization of the solution. The resulting problem is non-convex, but has a smaller number of solution variables. We construct an optimization solver using an augmented Lagrangian methodology that enables us to deal with problems with tens of thousands of data points. The new solver provides more accurate and reliable answers than other approaches. By exploiting the connection between graph clustering objective functions and a kernel k-means objective, our new low-rank solver can also compute overlapping communities of social networks with state-of-the-art accuracy.

Data protection using backup is one of the most critical IT management operations to ensure business continuity, which is also constantly evolving due to the emerging challenges in the “Big Data Era.” In this paper, we introduce our ongoing research effort in designing intelligent enterprise backup management solutions by obtaining actionable insights from voluminous backup job metadata across data centers. Our proof-of-concept scalable data analytics framework integrated with enterprise backup architecture is introduced, and use case studies of leveraging big data analytics on enterprise backup framework are discussed.