Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate Resampling of Top- $k$  Monitoring Reports Using Unreported Residual Resource Consumption

Top-k reports are compound metrics that provide useful information when diagnosing problems in a system, e.g., to identify persistent CPU usage by a process. In large systems, these reports are collected at regular intervals and need to be resampled to reduce the bitrate of the data, to answer user queries for different sampling periods, or to save space and make it possible to keep historical data for long term performance analysis. However, resampling top-k reports, i.e., aggregating several reports collected for small time intervals into a single top-k report can introduce inaccuracies. For example, a process that consistently uses CPU over the aggregation interval but is not included in the short term top-k reports will be missing from the aggregated report. In this paper, we present an algorithm that collects top-k reports at regular intervals and can aggregate them with little or no error. This is accomplished by including residual resource consumption of unreported, but potentially significant entities in the top-k reports, and using these residual values during aggregation. We show different approaches to including residual resource consumption in individual top-k reports, analyze the error introduced, the parameters with which the algorithm’s efficiency can be tuned, and demonstrate the effectiveness of the algorithm in a real-world scenario.
Trauma and emergency general surgery patients should be extubated with an open abdomen

BACKGROUND Open abdomen (OA) and temporary abdominal closure (TAC) are common techniques to manage several surgical problems in trauma and emergency general surgery (EGS). Patients with an OA are subjected to prolonged mechanical ventilation. This can lead to increased rates of ventilator-associated pneumonia (VAP). We hypothesized that patients who were extubated with an OA would have a decrease in ventilator hours and as a result would have a lower rate of VAP without an increase in extubation failures. METHODS A retrospective review was performed of all trauma and EGS patients managed at our institution with OA and TAC from January 2014 to February 2016. Patients were divided into cohorts consisting of those who were successfully extubated with an OA and those who were not. The number of extubation events and ventilator-free hours were calculated for each patient. Adverse events such as the need for reintubation with an OA and VAP were collected. RESULTS Fifty-two patients (20 trauma, 32 EGS) were managed with an OA and TAC during the study period. Twenty-five patients (6 trauma, 19 EGS) had at least one extubation event with an OA. Median extubation events per patient was 3 (interquartile range, 1–5). The median ventilator-free hours for patients who were extubated was 101 hours (interquartile range, 39.42–260.46). Patients that were never extubated with an OA had higher rates of VAP (30.8% vs. 3.8%, p = 0.01). CONCLUSION This study provides much needed data regarding the feasibility of extubation in trauma and EGS patients managed with an OA and TAC. Benefits of early extubation may include lower VAP rates in this population. Plans for reexploration hinder the decision to extubate in these patients. LEVEL OF EVIDENCE Therapeutic study, level IV.
Targeted disruption of the endogenous zebrafish rhodopsin locus as models of rapid rod photoreceptor degeneration

Purpose Retinitis pigmentosa (RP) is a collection of genetic disorders that results in the degeneration of light-sensitive photoreceptor cells, leading to blindness. RP is associated with more than 70 loci that may display dominant or recessive modes of inheritance, but mutations in the gene encoding the visual pigment rhodopsin (RHO) are the most frequent cause. In an effort to develop precise mutations in zebrafish as novel models of photoreceptor degeneration, we describe the generation and germline transmission of a series of novel clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-induced insertion and deletion (indel) mutations in the major zebrafish rho locus, rh1–1. Methods One- or two-cell staged zebrafish embryos were microinjected with in vitro transcribed mRNA encoding Cas9 and a single guide RNA (gRNA). Mutations were detected by restriction fragment length polymorphism (RFLP) and DNA sequence analyses in injected embryos and offspring. Immunolabeling with rod- and cone-specific antibodies was used to test for histological and cellular changes. Results Using gRNAs that targeted highly conserved regions of rh1–1, a series of dominant and recessive alleles were recovered that resulted in the rapid degeneration of rod photoreceptors. No effect on cones was observed. Targeting the 5′-coding sequence of rh1–1 led to the recovery of several indels similar to disease-associated alleles. A frame shift mutation leading to a premature stop codon (T17*) resulted in rod degeneration when brought to homozygosity. Immunoblot and fluorescence labeling with a Rho-specific antibody suggest that this is indeed a null allele, illustrating that the Rho expression is essential for rod survival. Two in-frame mutations were recovered that disrupted the highly conserved N-linked glycosylation consensus sequence at N15. Larvae heterozygous for either of the alleles demonstrated rapid rod degeneration. Targeting of the 3′-coding region of rh1–1 resulted in the recovery of an allele encoding a premature stop codon (S347*) upstream of the conserved VSPA sorting sequence and a second in-frame allele that disrupted the putative phosphorylation site at S339. Both alleles resulted in rod death in a dominant inheritance pattern. Following the loss of the targeting sequence, immunolabeling for Rho was no longer restricted to the rod outer segment, but it was also localized to the plasma membrane. Conclusions The efficiency of CRISPR/Cas9 for gene targeting, coupled with the large number of mutations associated with RP, provided a backdrop for the rapid isolation of novel alleles in zebrafish that phenocopy disease. These novel lines will provide much needed in-vivo models for high throughput screens of compounds or genes that protect from photoreceptor degeneration.
Benchmarking Convolutional Neural Networks for Object Segmentation and Pose Estimation

Convolutional neural networks (CNNs), particularly those designed for object segmentation and pose estimation, are now applied to robotics applications involving mobile manipulation. For these robotic applications to be successful, robust and accurate performance from the CNNs is critical. Therefore, in order to develop an understanding of CNN performance, several CNN architectures are benchmarked on a set of metrics for object segmentation and pose estimation. This paper presents these benchmarking results, which show that metric performance is dependent on the complexity of network architectures. These findings can be used to guide and improve the development of CNNs for object segmentation and pose estimation in the future.
A Data Migration Scheme Considering Node Reliability for an Autonomous Distributed Storage System

We have proposed a distributed storage system which aims to utilize storage fragments distributed in network nodes without administrators' maintenance cost. Even the performance of storage devices are not uniform, autonomously data block migration helps to achieve enhancing the whole I/O performance of the storage system. However, we have considered all the storage devices to be reliable equally. On this assumption, migration of an important data block to unstable storage might cause degradation of the availability of the data block. In this paper, we propose a scheme to choose the destination of data block migration by evaluating the nodes' reliability along with the performance of storage devices. This scheme aims to improve system stability by preventing to locate a critical data onto an unstable storage device. The storage nodes which lack stability are also utilized for storing a data block which can be easily regenerated or duplicated. The results of the preliminary investigation show that a large number of warning logs from Machine Check Architecture may suggest a sign of unexpected system shutdown.
Association between serum level of advanced glycation end products and obstructive sleep apnea–hypopnea syndrome: a meta-analysis

Objective This meta-analysis was performed to assess the difference in the serum level of advanced glycation end products (AGEs) between patients with obstructive sleep apnea–hypopnea syndrome (OSAHS) and controls. Methods A systematic literature search was performed using PubMed, Elsevier, SCI, Wanfang, Weipu, and China National Knowledge Internet. Eligible studies that reported the serum AGE level in patients with OSAHS were identified by two reviewers. Review Manager version 5.2 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark) and R version 3.10 (www.r-project.org) were employed for data synthesis. Results Five studies involving 670 subjects were identified. The meta-analysis showed that the mean serum AGE level in the OSAHS group was 0.98 mmol/L higher than those in the control group (95% confidence interval, 0.69–1.27). Conclusions This meta-analysis showed that the serum AGE level was elevated in patients with OSAHS. This finding suggests that AGEs may play an important role in insulin resistance in OSAHS and serve as a biomarker for patients with OSAHS with a high risk of type 2 diabetes mellitus.
FlowVirt: Flow Rule Virtualization for Dynamic Scalability of Programmable Network Virtualization

We propose a new concept called "flow rule virtualization" (FlowVirt) for programmable network virtualization (P-NV). In P-NV, network hypervisor is a key component in that it plays a role in creating and managing virtual networks. This paper first reports a critical limitation of network hypervisor - scalability problem, which results in the high consumption of the switch memory, control channel, and CPU cycles: 3.9, 4.7, and 1.7 times higher than host-based network virtualization, respectively. This scalability problem arises because all the flow rules from the virtual network controllers are directly installed into switches. To resolve the scalability problem, FlowVirt introduces a flow rule abstraction: virtual and physical flow rules. By separating virtual and physical flow rules, the abstraction virtualizes flow rules so that FlowVirt can merge virtual flow rules to a smaller number of physical flow rules to be installed in switches. The evaluation results show the enhanced scalability of FlowVirt. The number of flow rules to be installed in switches decreases by up to 10 times compared to the previous P-NV. The control channel bandwidth and CPU cycles are also reduced by up to 14 and 3 times, respectively.
The Latent Structure of Negative Symptoms in Schizophrenia

Importance Negative symptoms are associated with a range of poor clinical outcomes, and currently available treatments generally do not produce a clinically meaningful response. Limited treatment progress may be owing in part to poor clarity regarding latent structure. Prior studies have inferred latent structure using exploratory factor analysis, which has led to the conclusion that there are 2 dimensions reflecting motivation and pleasure (MAP) and diminished expressivity (EXP) factors. However, whether these conclusions are statistically justified remains unclear because exploratory factor analysis does not test latent structure. Confirmatory factor analysis (CFA) is needed to test competing models regarding the latent structure of a construct. Objective To evaluate the fit of 4 models of the latent structure of negative symptoms in schizophrenia using CFA. Design, Setting, and Participants Three cross-sectional studies were conducted on outpatients with schizophrenia who were rated on the 3 most conceptually contemporary measures: Scale for the Assessment of Negative Symptoms (SANS), Brief Negative Symptom Scale (BNSS), and Clinical Assessment Interview for Negative Symptoms (CAINS). Confirmatory factor analysis evaluated the following 4 models: (1) a 1-factor model; (2) a 2-factor model with EXP and MAP factors; (3) a 5-factor model with separate factors for the 5 domains of the National Institute of Mental Health consensus development conference (blunted affect, alogia, anhedonia, avolition, and asociality); and (4) a hierarchical model with 2 second-order factors reflecting EXP and MAP and 5 first-order factors reflecting the 5 consensus domains. Main Outcomes and Measures Outcomes included CFA model fit statistics derived from symptom severity scores on the SANS, BNSS, and CAINS. Results The study population included 860 outpatients with schizophrenia (68.0% male; mean [SD] age, 43.0 [11.4] years). Confirmatory factor analysis was conducted on each scale, including 268 patients for the SANS, 192 for the BNSS, and 400 for the CAINS. The 1- and 2-factor models provided poor fit for the SANS, BNSS, and CAINS as indicated by comparative fit indexes (CFIs) and Tucker Lewis indexes (TLIs) less than 0.950, RMSEAs that exceeded the 0.080 threshold, and WRMRs greater than 1.00. The 5-factor and hierarchical models provided excellent fit, with the 5-factor model being more parsimonious. The CFIs and TLIs met the 0.95 threshold and the 1.00 threshold for both factor models with all 3 measures. Interestingly, the RMSEAs for the 5-factor model and the hierarchical model fell under the 0.08 threshold for the BNSS and the CAINS but not the SANS. Conclusions and Relevance These findings suggest that the recent trend toward conceptualizing the latent structure of negative symptoms as 2 distinct dimensions does not adequately capture the complexity of the construct. The latent structure of negative symptoms is best conceptualized in relation to the 5 consensus domains. Implications for identifying pathophysiological mechanisms and targeted treatments are discussed.
A Message Passing Approach to Acquire mm-Wave Channel State Information Based on Out-of-Band Data

Millimeter-wave (mm-wave) communication is a practicable scheme for big data communication, such as next-generation cellular communication. However, because mm-wave frequencies have an extremely large path loss, in order to mitigate the mm-wave path loss, a large number of antennas are packed for beamforming. Conventional multiple-input multiple-output (MIMO) beamforming uses digital processing, which leads to needing extremely giant energy. Hybrid analog/digital beamforming could serve as an awesome technique to reduce the cost. It is essential for achieving high beamfroming gain that accurate mm-wave channel information should be attained at the transmitter. This paper concentrates on the channel state information (CSI) acquirement problem in mm-wave communication systems with massive MIMO arrays. Because of the CSI acquirement, a method of significant overhead, we consider an accurate channel estimation scheme with low overhead. Not only do we in this paper propose using support information extracted at Sub-6GHz to aid the mm-wave CSI acquirement but also introduce a strategy used in the mm-wave channel estimation. We, in particular, formulate mm-wave CSI acquirement as a compressive sensing problem and obtain the CSI by using Bernoulli–Gaussian generalized approximate message passing (BG-GAMP) algorithm. We also extend the BG-GAMP algorithm with support distribution information from sub-6-GHz channel. Furthermore, based on the $K$ nearest neighbor idea, we redesign the BG-GAMP algorithm depending on sub-6-GHz support distribution information. Details of BG-GAMP with the nearest neighbor learning algorithm that is built on support distribution information from sub-6-GHz channel would also be revealed. Simulation results show the out-of-band information aided mm-wave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy improved as well.
Deep Learning in Information Security

Machine learning has a long tradition of helping to solve complex information security problems that are difficult to solve manually. Machine learning techniques learn models from data representations to solve a task. These data representations are hand-crafted by domain experts. Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers. Consequently, representations that are used to solve a task are learned from the data instead of being manually designed. In this survey, we study the use of DL techniques within the domain of information security. We systematically reviewed 77 papers and presented them from a data-centric perspective. This data-centric perspective reflects one of the most crucial advantages of DL techniques -- domain independence. If DL-methods succeed to solve problems on a data type in one domain, they most likely will also succeed on similar data from another domain. Other advantages of DL methods are unrivaled scalability and efficiency, both regarding the number of examples that can be analyzed as well as with respect of dimensionality of the input data. DL methods generally are capable of achieving high-performance and generalize well. However, information security is a domain with unique requirements and challenges. Based on an analysis of our reviewed papers, we point out shortcomings of DL-methods to those requirements and discuss further research opportunities.
A retrospective review of correlative radiological assessment and surgical exploration for hilar cholangiocarcinoma

Backgrounds/Aims Hilar cholangiocarcinomas (HCCAs) are tumors that involve the biliary confluence; at present, radical surgery offers the only chance of long-term survival, but this can be challenging given the complexity of the hilar anatomy. Blumgart and Jarnagin described a preoperative staging system that incorporates the effect of local tumor extent and its impact on adjacent structures and that has been demonstrated to correlate better with actual surgical resectability. The primary aim of this study is to describe the correlation between preoperative Blumgart-Jarnagin staging and its correlation with surgical resectability. Methods Patients who underwent surgical resection for hilar cholangiocarcinoma at Singapore General Hospital between January 1, 2002, and January 1, 2013, were identified from a prospectively maintained institutional database. All patients were staged based on the criteria described by Blumgart and Jarnagin. Correlation with surgical resectability was then determined. Results A total of 19 patients were identified. Overall resectability was 57.8% (n=11). Patients with Blumgart-Jarnagin stage T1 had the highest rates of resectability at 80%; patients with stage T2 and T3 disease had resectability rates of 25% and 40% respectively. Median overall survival was 13.6 months. Conclusions The Blumgart-Jarnagin staging system is useful for predicting tumor resectability in HCCA.
A New SLA-Aware Load Balancing Method in the Cloud Using an Improved Parallel Task Scheduling Algorithm

Cloud computing as a novel and entirely internet-based computing platform is emerging and its tenacious challenges become more vivid. A parallel genetic algorithm-based method for scheduling tasks with priorities is provided in this paper. The goal is to efficiently utilize resources and reduce resource wastage in cloud environments. This is achieved by improving the load balancing rate while better resources are selected to fulfill arrival tasks in a shorter time with lower task failure rate. To evaluate the proposed method, it is simulated using Matlab and compared with two existing methods, a hybrid Ant colony-honey method and a Round-Robin (RR) based load balancing method. The results show that the proposed method has 9% - 31% lower energy usage, 14% - 37% lower migration rate and 13%- 17% better Service Level Agreement (SLA) in comparison with the Hybrid and RR method.
Efficient Key-Value Stores with Ranged Log-Structured Merge Trees

The log-structured merge (LSM) tree is designed to provide efficient indexing for data that is frequently updated by using the log-structured approach. It defers merge operations for reordering data, propagating the index changes from a memory-resident component through one or more disk components. Thus, LSM-based storage engines can achieve good write performance. However, processing merge operations incurs high write amplification and memory consumption, ultimately having an adverse effect on system performance. In this paper, we propose the Ranged Log-Structured Merge (RLSM) tree to mitigate the problems of the LSM tree. To reduce the write amplification and memory overhead, RLSM simplifies the logical layout of storage and keeps data as an unsorted order. In addition, we prevent read performance from declining by partitioning data on the disk into multiple files with non-overlapping ranges. We implement our schemes on HBase, one of the most popular key-value storage engines, and evaluate our system by using YCSB benchmark. Our experimental results show that RLSM consequently reduces write amplification by a factor of 3, and memory consumption by up to 24%.
BLM’s balancing act and the involvement of FANCJ in DNA repair

ABSTRACT Timely recruitment of DNA damage response proteins to sites of genomic structural lesions is very important for signaling mechanisms to activate appropriate cell cycle checkpoints but also repair the altered DNA sequence to suppress mutagenesis. The eukaryotic cell is characterized by a complex cadre of players and pathways to ensure genomic stability in the face of replication stress or outright genomic insult by endogenous metabolites or environmental agents. Among the key performers are molecular motor DNA unwinding enzymes known as helicases that sense genomic perturbations and separate structured DNA strands so that replacement of a damaged base or sugar-phosphate backbone lesion can occur efficiently. Mutations in the BLM gene encoding the DNA helicase BLM leads to a rare chromosomal instability disorder known as Bloom’s syndrome. In a recent paper by the Sengupta lab, BLM’s role in the correction of double-strand breaks (DSB), a particularly dangerous form of DNA damage, was investigated. Adding to the complexity, BLM appears to be a key ringmaster of DSB repair as it acts both positively and negatively to regulate correction pathways of high or low fidelity. The FANCJ DNA helicase, mutated in another chromosomal instability disorder known as Fanconi Anemia, is an important player that likely coordinates with BLM in the balancing act. Further studies to dissect the roles of DNA helicases like FANCJ and BLM in DSB repair are warranted.
Sensor Data Based System-Level Anomaly Prediction for Smart Manufacturing

With the popularity of Supervisory Information System (SIS), Supervisory Control and Data Acquisition (SCADA) system and Internet of Things (IoT) sensors, we can easily obtain abundant sensor data in manufacturing. We could save manufacturing maintenance costs and prevent further damages if we can accurately predict system anomalies from the sensor data. Yet learning from individual sensors often cannot directly determine whether the system will have anomaly because each sensor only measures a partial state of a big system. By detecting events across sensors collectively and their temporal dependencies, this paper proposes a new system-level anomaly prediction framework by mining anomaly dependency graph from sensor data. The advantages of the approach include explainability, collective prediction and temporal sensitivity. We applied our approach with a real-world power plant dataset to evaluate its feasibility.