Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches

CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse

Diversified histone modifications (HMs) are essential epigenetic features. They play important roles in fundamental biological processes including transcription, DNA repair and DNA replication. Chromatin regulators (CRs), which are indispensable in epigenetics, can mediate HMs to adjust chromatin structures and functions. With the development of ChIP-Seq technology, there is an opportunity to study CR and HM profiles at the whole-genome scale. However, no specific resource for the integration of CR ChIP-Seq data or CR-HM ChIP-Seq linkage pairs is currently available. Therefore, we constructed the CR Cistrome database, available online at http://compbio.tongji.edu.cn/cr and http://cistrome.org/cr/, to further elucidate CR functions and CR-HM linkages. Within this database, we collected all publicly available ChIP-Seq data on CRs in human and mouse and categorized the data into four cohorts: the reader, writer, eraser and remodeler cohorts, together with curated introductions and ChIP-Seq data analysis results. For the HM readers, writers and erasers, we provided further ChIP-Seq analysis data for the targeted HMs and schematized the relationships between them. We believe CR Cistrome is a valuable resource for the epigenetics community

Robust Multimodal Failure Detection for Microservice Systems

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches

A \u3cem\u3eLIN28B\u3c/em\u3e Tumor-Specific Transcript in Cancer

The diversity and complexity of the cancer transcriptome may contain transcripts unique to the tumor environment. Here, we report a LIN28B variant, LIN28B-TST, which is specifically expressed in hepatocellular carcinoma (HCC) and many other cancer types. Expression of LIN28B-TST is associated with significantly poor prognosis in HCC patients. LIN28B-TST initiates from a de novo alternative transcription initiation site that harbors a strong promoter regulated by NFYA but not c-Myc. Demethylation of the LIN28B-TST promoter might be a prerequisite for its transcription and transcriptional regulation. LIN28B-TST encodes a protein isoform with additional N-terminal amino acids and is critical for cancer cell proliferation and tumorigenesis. Our findings reveal a mechanism of LIN28B activation in cancer and the potential utility of LIN28B-TST for clinical purposes

Assess and Summarize: Improve Outage Understanding with Large Language Models

Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are affected by a cloud outage, users can experience slow response times, connection issues or total service disruption, resulting in a significant negative business impact. Outages are usually comprised of several concurring events/source causes, and therefore understanding the context of outages is a very challenging yet crucial first step toward mitigating and resolving outages. In current practice, on-call engineers with in-depth domain knowledge, have to manually assess and summarize outages when they happen, which is time-consuming and labor-intensive. In this paper, we first present a large-scale empirical study investigating the way on-call engineers currently deal with cloud outages at Microsoft, and then present and empirically validate a novel approach (dubbed Oasis) to help the engineers in this task. Oasis is able to automatically assess the impact scope of outages as well as to produce human-readable summarization. Specifically, Oasis first assesses the impact scope of an outage by aggregating relevant incidents via multiple techniques. Then, it generates a human-readable summary by leveraging fine-tuned large language models like GPT-3.x. The impact assessment component of Oasis was introduced in Microsoft over three years ago, and it is now widely adopted, while the outage summarization component has been recently introduced, and in this article we present the results of an empirical evaluation we carried out on 18 real-world cloud systems as well as a human-based evaluation with outage owners. The results show that Oasis can effectively and efficiently summarize outages, and lead Microsoft to deploy its first prototype which is currently under experimental adoption by some of the incident teams