For providers operating large-scale global networks, the timeliness of network failure recovery significantly affects the reliability of network services. Ideally, a network monitoring system should have enough coverage to detect even minor issues, but high coverage means alert floods during severe network failures. In practice, there is a gap between the flooding raw alerts data collected by network monitoring tools and the readable information needed for failure diagnosis. Existing solutions using limited network monitoring data sources and heuristic diagnostic rules, lack comprehensive coverage and the capability to address severe failures, especially which network operators have never handled a similar one before. This paper presents SkyNet, a network analysis system to extract scope and severity information from alert floods. SkyNet ensures comprehensive coverage by integrating multiple monitoring data sources through a uniform input format, enhancing extensibility for new network monitoring tools. During alert floods, SkyNet groups alerts, assesses their severity, and filters out insignificant ones to aid network operators in mitigating network failures. To date, SkyNet has been running stably on our network for one and a half years without any false negatives and has successfully reduced the time-to-mitigation for over 80% of network failures since its deployment in production.

Multi-dimensional Root Cause Analysis (RCA) is often applied to identify abnormal traffic patterns, i.e., localizing the abnormal combination of traffic header fields. Several techniques have been proposed recently, but they were mainly designed for smaller-scaled datasets and were not feasible in the real network due to the high computational overhead. To overcome the aforementioned limitations, we propose FTM-RCA, which accelerates RCA by breaking the analysis procedure into two stages: coarse-grained rules filtering and fine-grained localization. In the first stage, an optimized frequent itemset mining (FIM) technique called CUSC is proposed, which can detect high-volume combinations faster based on the mutual exclusion of dimension values. Experiments on CUSC show that it can speed up by 44.87% and reduce memory consumption by 21.89% compared to the best previous FIM algorithms. In the second stage, a dimension-based search method is proposed to identify the root cause combinations, which consists of two key components: 1) drill-down strategy, which utilizes Contributive Power to measure the correlation between the combination and anomaly. 2) pruning strategy, which adopts the Shannon entropy to avoid generating trivial results. As a result, the overall diagnostic time of FTM-RCA is at least 25 times faster than the previous best research while improving accuracy by an average of 21.6%. Also, our practical application in real network also illustrates the applicability of FTM-RCA.

Recent advances in bioinformatics analyses have led to the development of novel tools enabling the capture and trajectory mapping of single-cell RNA sequencing (scRNAseq) data. However, there is a lack of methods to assess the contributions of biological pathways and transcription factors to an overall developmental trajectory mapped from scRNAseq data. In this manuscript, we present a simplified approach for trajectory inference of pathway significance (TIPS) that leverages existing knowledgebases of functional pathways and other gene lists to provide further mechanistic insights into a biological process. TIPS identifies key pathways which contribute to a process of interest, as well as the individual genes that best reflect these changes. TIPS also provides insight into the relative timing of pathway changes, as well as a suite of visualizations to enable simplified data interpretation of scRNAseq libraries generated using a wide range of techniques. The TIPS package can be run through either a web server or downloaded as a user-friendly GUI run in R, and may serve as a useful tool to help biologists perform deeper functional analyses and visualization of their single-cell data.

Recommendation techniques are very important in the fields of E-commerce and other web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally, a recommendation is made by adaptively weighting the features. Experimental results on public data sets show that the proposed algorithm has satisfying performance.

A self-diagnosis circuit that can be used for built-in self-repair is proposed. The circuit under diagnosis is assumed to be composed of a large number of field repairable units (FRUs), which can be replaced with spares when they are found to be defective. Since the proposed self-diagnosis circuit is implemented on the chip, responses that are scanned out of scan chains are compressed by the group compactor, the space compression circuit, and finally, the time compression circuit to reduce the volume of test response data. Both the space and time compression circuits implement a Reed-Solomon code. Unlike prior work, in the proposed technique, responses of all FRUs are observed at the same time to reduce diagnosis time. The proposed diagnosis circuit can locate up to l defective FRUs. We propose a novel space compression circuit that reduces hardware overhead by exploiting the frequency difference of the scan shift clock and the system clock and by combining scan cells into groups of size r. When the size of constituent multiple-input signature register (MISR) is m, the total number of signatures to be stored for the fault-free signature is 2 lmB bits, where 1≤ B ≤ m. The experimental results show that the proposed diagnosis circuit that can locate up to four defective FRUs in the same test session can be implemented with less than one percent of hardware overhead for a large industrial design. Hardware overhead for the diagnosis circuit is lower for large CUDs.

A self-diagnosis circuit that can be used for builtin self-repair is proposed. The circuit under diagnosis is assumed to be comprised of a large number of field repairable units (FRUs), which can be replaced with spares when they are found to be defective. Since the proposed self-diagnosis circuit is implemented on the chip, responses that are scanned out of scan chains are compressed first by the space compression circuit and then by the time compression circuit to reduce the volume of test response data. Both the space and the time compression circuit implement a Reed-Solomon code. Unlike prior work, in the proposed technique, responses of all FRUs are observed at the same time to reduce diagnosis time. The proposed diagnosis circuit can locate up to l defective FRUs. We propose a novel space-compression circuit that reduces hardware overhead by exploiting the frequency difference of the scan shift clock and the system clock. When the size of constituent multiple-input signature-register (MISR) is m, the total number of signatures to be stored for the fault-free signature is 2lmB bits, where 1 les B les m. The experimental results show that the proposed diagnosis circuit that can locate up to 4 defective FRUs in the same test session can be implemented with less than 1% of hardware overhead for a large industrial design. Hardware overhead for the diagnosis circuit is lower for large CUDs.

Stock price forecasting has aroused great concern in research of economy, machine learning and other fields. Time series analysis methods are usually utilized to deal with this task. In this paper, we propose to combine news mining and time series analysis to forecast inter-day stock prices. News reports are automatically analyzed with text mining techniques, and then the mining results are used to improve the accuracy of time series analysis algorithms. The experimental result on a half year Chinese stock market data indicates that the proposed algorithm can help to improve the performance of normal time series analysis in stock price forecasting significantly. Moreover, the proposed algorithm also performs well in stock price trend forecasting.

This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, "democratic" fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://organic.usc.edu:8376/ approximately tangx/neco/index.html .).

We propose novel error correction coding schemes called generalized integrated interleaving and sparsely integrated interleaving codes. In the context of block interleaved codewords, generalized integrated interleaving allows nonuniform redundancy to be shared among all the interleaves. This allows the redundancy to be adjusted on-the-fly to better suit the error statistics of the channel or storage device. Sparsely integrated interleaving groups data nodes in a distributed storage system into subgroups. A data node can belong to several subgroups. Small errors are corrected locally within each subgroup. A localized algebraic iterative decoding algorithm is used to decode across subgroups to correct large errors that cannot be corrected within subgroups. Very little correction capability is sacrificed to achieve fast error correction and lower communication overhead. This scheme improves data access for all the data nodes and allows easy scaling of the distributed storage network

The correction of errors in bursty channels commonly utilizes interleaved blocks of codewords. Integrated Interleaving coding schemes [1] uses nonuniform redundancy in the interleaves that can be adjusted on-the-fly according to the error statistics of the channel. This gives greater flexibility in the designing of the code and achieves higher reliability for the same code rate. We give a different and substantially tighter theoretical evaluation on the performance of the Integrated Interleaving code than [1]. Based on the tighter theoretical evaluation, we formulate a closed-form analytical expression for its performance in terms of the word-error probability. We also propose a modified decoding algorithm of the Integrated Interleaving codes which achieves a significant gain in performance. We optimize the performance of this scheme within a given class of codes.