Clustering is a fundamental problem that has been extensively studied over past few decades, with most research focusing on point-based clustering such as $k$-means, $k$-median, and $k$-center. However, numerous real-world applications, such as motion analysis, computer vision, and missing data analysis, require clustering over structured data, including lines, time series and affine subspaces (flats), where traditional point-based clustering algorithms often fall short. In this paper, we study the $k$-means of lines problem, where the input is a set $L$ of lines in $\mathbb{R}^d$, and the goal is to find $k$ centers $C$ in $\mathbb{R}^d$ such that the sum of squared distances from each line in $L$ to its nearest center in $C$ is minimized. The local search algorithm is a well-established strategy for point-based $k$-means clustering, known for its efficiency and provable approximation guarantees. However, extending local search algorithm to the $k$-means of lines problem is nontrivial, as the capture relation used in point-based clustering does not generalize to the line setting. This is because that the point-to-line distance function lack the triangle inequality property that supports geometric analysis in point-based clustering. Moreover, since lines extend infinitely in space, it is difficult to identify effective swap points that can significantly reduce the clustering cost. To overcome above obstacles, we introduce a *proportional capture relation* that links optimal and current centers based the assignment proportions of lines, enabling a refined analysis that bypasses the triangle inequality barrier. We also introduce a *CrossLine* structure, which provides a principled discretization of the geometric space around line pairs, and ensures coverage of high-quality swap points essential for local search, thereby enabling effective execution of the local search process. Consequently, based on the proposed components, we develop the first single-swap local search algorithm for the $k$-means of lines problem, achieving a $(500+\varepsilon)$-approximation in polynomial time for low-dimensional Euclidean space.

The virtual memory subsystem (VMS) is a long-standing and integral part of an operating system (OS). It plays a vital role in enabling remote memory systems over fast data center networks and is promising in terms of transparency and generality. Specifically, these systems use three VMS mechanisms: demand paging, page swapping, and page prefetching. However, the VMS inherent data path is costly, which takes a huge toll on performance. Despite prior efforts to propose page swapping and prefetching algorithms to minimize the occurrences of the data path, they still fall short due to the semantic gap between the OS and applications - the VMS has limited knowledge of its running applications' memory access behaviors. In this paper, orthogonal to prior efforts, we take a fundamen-tally different approach by building an efficient framework to collect full memory access traces at the local bus, and make them available to the OS through CPU cache. Consequently, the page swapping and page prefetching can use this trace to make better decisions, thereby improving the overall performance of systems. We implement a proof-of-concept prototype on commodity x86 servers using a hardware-based memory tracking tool. To show-case our framework's benefits, we integrate it with a state-of-the-art remote memory system and the default kernel page eviction subsystem. Our evaluation shows promising improvements.

Memory disaggregation is a promising direction to mitigate memory contention in datacenters. To make memory disaggregation practical, prior efforts expose remote memory to applications transparently via virtual memory subsystem’s swapping interface. However, due to the semantic gap between OS and applications – OS cannot know the memory accessing sequences of an application but via page faults. This approach has two limitations. First, it learns little from page faults’ access history, which leads to sub-optimal prefetching predictions. Second, a page fault can still occur even if there is a prefetch-hit which leads to a large kernel overhead.To address such limitations, our key insight is to decouple the address capturing from page faults by collecting full memory access traces in the memory controller. Using this idea, we buildHoPP– a hardware-software co-designed prefetching framework.HoPPadds hardware modules to the memory controller to feed sufficient hot pages to OS in real-time, which has three benefits inHoPP’s software design: 1) it improves existing prefetching algorithms with simple revamps, also offers more insights to build better policies; 2) the prefetch algorithm can run as a separate data path alongside the normal remote data path via page faults, potentially hiding the swap latency from applications, and enabling fine-grained control over prefetching behaviors; 3) the prefetch-hit overhead can be eliminated by early page table entry (PTE) injection, i.e., inject PTE for the prefetched page as soon as it returns. We implemented a proof-of-concept prototype using commodity servers along with a hardware-based memory tracking tool calledHMTTto emulate a modified memory controller. Results show that compared to Fastswap and Leap,HoPP-optimized prefetching algorithm achieves over 90% accuracy and coverage, which leads to up to 59% completion time improvement for various datacenter applications.

Learning-based approaches have shown their great potential to adapt themselves to various environments (e.g., PCC and Sprout). Unfortunately, they do not consistently achieve superior QoS across different network conditions and configurations in mobile networks. Furthermore, although they can offer multiple application objectives by adjusting a preference weight vector, it is challenging for users to accurately express an application objective with a weight vector. In this work, we argue that, if configured correctly, the delay-based TCP scheme can outperform learned ones, and allow users to directly specify their objectives. To this end, we propose Post-QoS Analysis (PQSA), a data-driven framework that trains the key QoS-impacting parameters of the scheme to capture the statistical correlations between QoS objectives, network conditions, and configurations, thereby determining the optimal parameter-set that meets the user-defined QoS objective under different network conditions and configurations. To support this, we enhance conventional delay-based TCP design to develop a Generalized TCP-like Rate controller (GR) by exporting three key parameters. Extensive evaluations show that PQSA-optimized GR outperforms existing schemes in different scenarios consistently, and enables service providers to control the QoS flexibly.

In this article we consider Incremental Fisher linear discriminant (IFLD) based on data denoising. The data denoising is completed by Markov sampling such that the generated non-noise sample sequence is an uniformly ergodic Markov chain (u.e.M.c.). We first establish the generalization bounds of IFLD with u.e.M.c. samples, and prove that the IFLD algorithm with u.e.M.c. samples is consistent. We also present two new IFLD classification algorithms based on Markov sampling, IFLD based on Markov sampling (IFLD-MS) and improved IFLD based on Markov sampling (IIFLD-MS). Experimental results of benchmark repository suggest that IFLD-MS and IIFLD-MS have better performance than the classical IFLD, the incremental support vector machine (ISVM) and other IFLD algorithms.

Consumer lending service is escalating in E-Commerce platforms due to its capability in enhancing buyers' purchasing power, improving average order value, and increasing revenue of the platforms. Credit risk forecasting and credit limits setting are two fundamental problems in E-Commerce/online consumer lending services. Currently, the majority of institutes rely on two-separate-step methods to resolve. First, build a rating model to evaluate credit risk, and then design heuristic strategies to set credit limits, which requires a large amount of prior knowledge and lacks theoretical justifications. In this paper, we propose an end-to-end multi-view and multi-task learning based approach named MvMoE (Multi-view-aware Mixture-of-Experts network) to solve these two problems simultaneously. First, a multi-view network with a hierarchical attention mechanism is constructed to distill users' heterogeneous financial information into shared hidden representations. Then, we jointly train these two tasks with a view-aware multi-gate mixture-of-experts network and a subsequent progressive network to improve their performances. With the real-world dataset contained 5.44 million users, we investigate the effectiveness of MvMoE. Experimental results exhibit that the proposed model is able to improve AP over 5.60% on credit risk forecasting and MAE over 9.52% on credit limits setting compared with conventional methods. Meanwhile, MvMoE has good interpretability, which better underpins the imperative demands in financial industries.

BACKGROUND: tRNA-derived fragments have been reported to be key regulatory factors in human tumors. However, their roles in the progression of multiple myeloma remain unknown. RESULTS: This study employed RNA-sequencing to explore the expression profiles of tRFs/tiRNAs in new diagnosed MM and relapsed/refractory MM samples. The expression of selected tRFs/tiRNAs were further validated in clinical specimens and myeloma cell lines by qPCR. Bioinformatic analysis was performed to predict their roles in multiple myeloma progression.We identified 10 upregulated tRFs/tiRNAs and 16 downregulated tRFs/tiRNAs. GO enrichment and KEGG pathway analysis were performed to analyse the functions of 1 significantly up-regulated and 1 significantly down-regulated tRNA-derived fragments. tRFs/tiRNAs may be involved in MM progression and drug-resistance. CONCLUSION: tRFs/tiRNAs were dysregulated and could be potential biomarkers for relapsed/refractory MM.

Credit risk forecasting generally aims to evaluate the default probability of users in financial service. It is usually regarded as a binary classification problem, which suffers from the severe class-imbalance problem due to the extremely limited throngs and the concept drift problem brought by the delayed verification. In this paper, we investigate these problems in credit risk forecasting and propose a semi-supervised meta-learning based approach called TRUST (TRainable Undersampling with Self Training) to resolve. First, it decides whether to sample the data through meta-learning based reinforcement learning. Secondly, it learns the distribution of the data that have not yet shown financial performance via self-training and updates the model trained in the first step. Finally, the updated model is evaluated on the validation dataset, the result of which will be fed back through the evaluator. These three steps will be iterated until the model converges. With the real-world industrial dataset containing 1.75 million users, we investigate the effectiveness of our method. Experimental results exhibit that the proposed method is able to improve AP over 5.94% on credit risk forecasting task compared with the recent methods.

An efficient parallel implementation of the robot inverse dynamics based on the recursive Newton-Euler formulation is considered. The algorithm basically partitions the computations related to a manipulator link among separate processors, resulting in a parallel architecture in which the number of processors equals the number of degrees of freedom of the manipulator. This has considerably low inherent parallelism if the forward propagation of velocities and accelerations and the backward propagation of forces and torques are synchronized. Therefore, in order to maximize the parallelism, the synchronization is completely removed, leaving each processor on the most recent values of the propagated variables. Since the mathematical approach for error analysis is too complex and the simulation approach is incomplete and unreliable, the algorithm is investigated on a real, multiprocessor machine, KUMARAN. A series of experiments with the model of the PUMA 560 manipulator and randomly generated inputs has shown surprisingly small errors. Some improvements of the algorithm which do not impair the time efficiency but provide further reduction of the approximation error are presented.>