Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular phylogeny and in situ detection of the etiologic agent of necrotizing hepatopancreatitis in shrimp

Necrotizing hepatopancreatitis (NHP) is a severe disease of farm-raised Penaeus vannamei that has been associated with mortality losses ranging from 20 to 95%. NHP was first recognized in Texas in 1985 (S. K. Johnson, p. 16, in Handbook of Shrimp Diseases, 1989) and is an economically important disease that has limited the ability to culture shrimp in Texas. The putative cause of NHP is a gram-negative, pleomorphic, intracellular, rickettsia-like bacterium that remains uncultured in part because of the absence of established shrimp cell lines. The inability to culture the NHP bacterium necessitated the use of molecular methods for phylogenetic placement of the NHP bacterium. The gene encoding the 16S rRNA (16S rDNA) of this shrimp pathogen was amplified by PCR, cloned, and sequenced. Sequence analysis of the cloned 16S rDNA indicates that the NHP bacterium is a member of the alpha subclass of the Proteobacteria. Within the alpha subclass, the NHP bacterium is shown to be most closely related to bacterial endosymbionts of protozoa, Caedibacter caryophila and Holospora obtusa. Also, the NHP bacterium is distinct from but related to members of the typhus group (Rickettsia typhi and R. prowazekii) and spotted fever group (R. rickettsii) of the family Rickettsiaceae. Fluorescently labeled oligonucleotide DNA probes that bind to variable regions (V2, V6, and V8) of 16S rRNA of the NHP bacterium were used to detect the bacterium in infected shrimp by in situ hybridization. This technique provided direct visual evidence that the 16S rDNA that was amplified, cloned, and sequenced was derived from the intracellular bacterium that infects the hepatopancreas of farm-raised P. vannamei shrimp.
Time-Constrained Live VM Migration in Share-Nothing IaaS-Clouds

Both economic reasons and interoperation requirements necessitate the deployment of IaaS-clouds based on a share-nothing architecture. Here, live VM migration becomes a major impediment to achieving cloud-wide load balancing via selective and timely VM-migrations. Our approach is based on copying virtual disk images and keeping them synchronized during the VM migration operation. In this way, we ameliorate the limitations set by shared storage cloud designs as we place no constraints on the cloud's scalability and load-balancing capabilities. We propose a special-purpose file system, termed MigrateFS, that performs virtual disk replication within specified time-constraints while avoiding internal network congestion. Management of resource consumption during VM migration is supervised by a low-overhead and scalable distributed network of brokers. We show that our approach can reduce up to 24% the stress of already saturated physical network links during load balancing operations.
ReMiT: Redundancy Migration for Latency Aware Fault Tolerant Cache Design in Multicore

Power dissipation in Chip Multiprocessors (CMPs) has been addressed by Dynamic Voltage and Frequency Scaling (DVFS). But uncontrolled reduction of voltage supply results in failure of cache cells due to either external particle intervention as soft errors or wearout effects as permanent errors. Several fault protection strategies for CMP cache have been proposed in this regard. But, these techniques fail to ensure complete fault protection with minimum latency. On this outset, this work proposes a redundancy migration strategy that not only ensures complete fault protection without using any auxiliary storage but also incurs minimum latency. Complete fault protection is achieved by remapping to non-conflicting faulty as well as potential non-reusable healthy blocks. Latency is minimized by migrating the redundant blocks towards their home tiles as the healthy blocks nearer to the home tiles gradually become non-reusable. Reusability is calculated by analysing the reuse distance distribution of the cache blocks. Through cycle accurate simulation in Multi2Sim 5.0 simulation framework for CPU-GPU heterogeneous computing, the proposed work claims up to 36.23% improvement in hit ratio and 51.04% improvement in L2 latency compared to the existing techniques.
Task detection for activity-based desktop search

The desktop search tools provide powerful query capabilities and result presentation techniques. However, they do not take the user context into account. We propose to exploit collected information about user activities with desktop files and applications for activity-based desktop search. When I prepare for a project review and type in a search box the name of a colleague, I expect to find her last deliverable draft, but not her email with a paper review or our joint conference presentation. Ideally, the desktop search system should be able to infer my current task from the logs of my previous activities and present task-specific search results.
Reducing the dilution: An analysis of the information sensitiveness of capsule network with a practical improvement method

Capsule network has shown various advantages over convolutional neural network (CNN). It keeps more precise spatial information than CNN and uses equivariance instead of invariance during inference and highly potential to be a new effective tool for visual tasks. However, the current capsule networks have incompatible performance with CNN when facing datasets with background and complex target objects and are lacking in universal and efficient regularization method. We analyze a main reason of the incompatible performance as the conflict between information sensitiveness of capsule network and unreasonably higher activation value distribution of capsules in primary capsule layer. Correspondingly, we propose a practical improvement method by restraining the activation value of capsules in primary capsule layer to suppress non-informative capsules and highlight discriminative capsules. In the experiments, the method has achieved better performances on various mainstream datasets. In addition, the proposed improvement methods can be seen as a suitable, simple and efficient regularization method that can be generally used in capsule network.
Microservice Architecture in Reality: An Industrial Inquiry

Background: Seeking an appropriate architecture for a software design is always a challenge in recent decades. Although microservices as a lightweight architecture style is claimed that can improve the current practices with several characteristics, many practices are based upon the different circumstances and reflect the variant effects. An empirical inquiry brings us a systematic insight into the industrial practices on microservices. Objective: This study is to investigate the gap between the ideal visions and real industrial practices on microservices and what benefits we can gain from the industrial experiences. Method: We carried out a series of industrial interviews with thirteen different types of companies. The collected data were then codified according to the defined qualitative methods. Results: We characterized the gaps between the typical characteristics accepted in the community and the industrial practices of microservices. Furthermore, the compromise between benefits and sufferings of microservices around these nine dimensions were also investigated. Conclusion: We confirmed the benefits of the microservices that can be obtained from practice as well as their possible pains that need to be addressed with extra expense from experiences. Besides, some outlined pains, e.g., organizational transformation, decomposition, distributed monitoring, and bug localization, may inspire researchers to conduct the further research.
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation

This paper describes our system for the subtask 1 of traditional Chinese Parsing of SIGHAN Bake-off 2012 evaluation. Since this research mainly focuses on speech recognition and synthesis applications, only base phrase chunking was implemented using three Conditional Random Field (CRF) modules, including word segmentation, POS tagging and base phrase chunking sub-systems. The official evaluation results show that the system achieved 0.5038 (0.7210/0.387) micro- and 0.5301 (0.7343/0.4147) macro-averaging F1 (precision/recall) rates on full sentence parsing task. However, if only the performance of base phrase chunking was considered, the Fmeasures may be around 0.70 and is somehow good enough for speech recognition and synthesis applications.
MGP: Extracting Multi-Granular Phases for Evolutional Events on Social Network Platforms

In this paper, we proposes a system for extracting multi-granular phases for event evolutions on social network platforms like Sina Weibo and Twitter. Existing studies on event extraction usually use a set of tweets to describe an event, which is not able to present the evolutional knowledge about the event. In many decision-making scenarios, it is much helpful to detect the evolutional stage of an event, as this can help people make counter-measures according to the current developing trend of the event. In this paper, we present a multi-granular approach for extracting the phases of evolutional events. We implement a web-based prototype called MGP (Multi-Granular Phase) which can extract and show the stages of events from a fine granularity such as hour to a coarse granularity like month. After a brief introduction on the architecture of MGP, we present the implemental details of MGP. Then, we present a case study to demonstrate the usability and effectiveness of MGP.
Envy, Comparison Costs, and the Economic Theory of the Firm

An economic theory of the firm must explain both when firms supplant markets and when markets supplant firms. While theories of when markets fail are well developed, the extant literature provides a less than adequate explanation of why and when hierarchies fail and of actions managers take to mitigate such failure. In this article, we seek to develop a more complete theory of the firm by theorizing about the causes and consequences of organizational failure. Our theory focuses on the concept of social comparison costs that arise through social comparison processes and envy. While transaction costs in the market provide an impetus to move activities inside the boundaries of the firm, we argue that envy and resulting social comparison costs motivate moving activities outside the boundary of the firm. More specifically, our theory provides an explanation for ‘managerial’ diseconomies of both scale and scope—arguments that are independent from traditional measurement, rent seeking, and competency arguments—that provides new insights into the theory of the firm. In our theory, hierarchies fail as they expand in scale because social comparison costs imposed on firms escalate and hinder the capacity of managers to optimally structure incentives and production. Further, hierarchy fails as a firm expands in scope for the simple reason that the costs of differentially structuring compensation within the firm to match the increasing diversity of activities also rises with increasing scope. In addition, we explore how social comparison costs influence the design of the firm through selection of production technologies and compensation structures within the firm. Copyright © 2008 John Wiley & Sons, Ltd.
A Hierarchical Clustering Based Relation Extraction Method for Domain Ontology

At present, the focus of ontology learning is on the extraction of concepts and relations. The relation extraction is divided into hierarchical relation extraction and non-hierarchical relation extraction, and hierarchical relation extraction is the basis of non-hierarchical relation extraction. In this paper, the method of mixed hierarchical clustering is used to extract and classify domain concepts from textual corpora in different fields of Uyghur language. Due to the limitations of the text content selected in this paper and the difficulty of merging nodes by hierarchical clustering, although the results are not ideal, they basically conform to the ontology hierarchy form. The experimental results show that the method is feasible, and the factors such as increasing the vector dimension and the text content, can further improve the accuracy of the domain concept and relation.
State of Charge Estimation of Supercapacitor Using Artificial Neural Network for Onboard Railway Applications

This paper proposes a novel method based on artificial neural network (ANN) for Estimation of state-of-charge (SOC) of supercapacitors (SC) energy storage device used in railway system for braking energy recovery. Indeed, the SOC of SCs is related to the knowledge of SCs internal voltage which is not accessible for measurement. By Using the available measurement signals, SC current and voltage, as inputs, the developed model is able to estimate the internal voltage of SC energy storage installed on board of the train. The numerical simulation processed in MATLAB/SIMULINK demonstrates the effectiveness of the ANN method to provide a good performance.
New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%.
Viral glycoproteins destined for apical or basolateral plasma membrane domains traverse the same Golgi apparatus during their intracellular transport in doubly infected Madin-Darby canine kidney cells

Madin-Darby canine kidney (MDCK) cells can sustain double infection with pairs of viruses of opposite budding polarity (simian virus 5 [SV5] and vesicular stomatitis virus [VSV] or influenza and VSV), and we observed that in such cells the envelope glycoproteins of the two viruses are synthesized simultaneously and assembled into virions at their characteristic sites. Influenza and SV5 budded exclusively from the apical plasma membrane of the cells, while VSV emerged only from the basolateral surfaces. Immunoelectron microscopic examination of doubly infected MDCK cells showed that the influenza hemagglutinin (HA) and the VSV G glycoproteins traverse the same Golgi apparatus and even the same Golgi cisternae. This indicates that the pathways of the two proteins towards the plasma membrane do not diverge before passage through the Golgi apparatus and therefore that critical sorting steps must take place during or after passage of the glycoproteins through this organelle. After its passage through the Golgi, the HA accumulated primarily at the apical membrane, where influenza virion assembly occurred. A small fraction of HA did, however, appear on the lateral surface and was incorporated into the envelope of budding VSV virions. Although predominantly found on the basolateral surface, significant amounts of G protein were observed on the apical plasma membrane well before disruption of the tight junctions was detectable. Nevertheless, assembly of VSV virions was restricted to the basolateral domain and in doubly infected cells the G protein was only infrequently incorporated into the envelope of budding influenza virions. These observations indicate that the site of VSV budding is not determined exclusively by the presence of G polypeptides. Therefore, it is likely that, at least for VSV, other cellular or viral components are responsible for the selection of the appropriate budding domain.
Pseudomonas aeruginosa Cif Protein Enhances the Ubiquitination and Proteasomal Degradation of the Transporter Associated with Antigen Processing (TAP) and Reduces Major Histocompatibility Complex (MHC) Class I Antigen Presentation*

Background: P. aeruginosa Cif degrades the ABC transporters CFTR and P-glycoprotein. Results: Cif increases the ubiquitination and degradation of TAP1 and decreases MHC class I antigen presentation in airway epithelial cells. Conclusion: Cif is the first bacterial factor identified that inhibits TAP function and MHC class I antigen presentation. Significance: These observations suggest a mechanism whereby Pseudomonas infection increases the severity and duration of respiratory viral infections. Cif (PA2934), a bacterial virulence factor secreted in outer membrane vesicles by Pseudomonas aeruginosa, increases the ubiquitination and lysosomal degradation of some, but not all, plasma membrane ATP-binding cassette transporters (ABC), including the cystic fibrosis transmembrane conductance regulator and P-glycoprotein. The goal of this study was to determine whether Cif enhances the ubiquitination and degradation of the transporter associated with antigen processing (TAP1 and TAP2), members of the ABC transporter family that play an essential role in antigen presentation and intracellular pathogen clearance. Cif selectively increased the amount of ubiquitinated TAP1 and increased its degradation in the proteasome of human airway epithelial cells. This effect of Cif was mediated by reducing USP10 deubiquitinating activity, resulting in increased polyubiquitination and proteasomal degradation of TAP1. The reduction in TAP1 abundance decreased peptide antigen translocation into the endoplasmic reticulum, an effect that resulted in reduced antigen available to MHC class I molecules for presentation at the plasma membrane of airway epithelial cells and recognition by CD8+ T cells. Cif is the first bacterial factor identified that inhibits TAP function and MHC class I antigen presentation.
Link Prediction in Online Social Networks Based on the Unsupervised Marginalized Denoising Model

As online social networking platforms change the ways and means of people communicating, accurate link prediction among a massive pool of users has become a difficult problem. The problem arises in many applications, such as friend recommendation, news feedback, and product recommendation. In this paper, we propose a novel algorithm to solve this problem. The existing online social network link prediction algorithms have some deficiencies in link prediction accuracy because they cannot make full use of information or capture all the features. From an unconventional perspective, this paper formulates the link prediction problem as a matrix denoising problem. We first propose an unsupervised marginalized denoising model (USMDM) and explain in detail its effectiveness. The core of the USMDM lies with a mapping function that is capable of identifying patterns in a vast amount of user information and also understands the topological structure of social networks. The mapping function projects the observed matrix onto a target matrix. To improve efficiency and prevent overfitting, a low-rank matrix is used to replace the original matrix in the learning process. Using the weak law of large number, the function can be learned on limited datasets. To illustrate the effectiveness of the proposed algorithm, experiments are conducted on four real social networks, and the results demonstrate the effectiveness of the model.
Ibex - An Intelligent Storage Engine with Support for Advanced SQL Off-loading

Modern data appliances face severe bandwidth bottlenecks when moving vast amounts of data from storage to the query processing nodes. A possible solution to mitigate these bottlenecks is query oﬀ-loading to an intelligent storage engine , where partial or whole queries are pushed down to the storage engine. In this paper, we present Ibex , a prototype of an intelligent storage engine that supports oﬀ-loading of complex query operators. Besides increasing performance, Ibex also reduces energy consumption, as it uses an FPGA rather than conventional CPUs to implement the oﬀ-load engine. Ibex is a hybrid engine, with dedicated hardware that evaluates SQL expressions at line-rate and a software fallback for tasks that the hardware engine cannot handle. Ibex supports GROUP BY aggregation, as well as projection - and selection - based ﬁltering. GROUP BY aggregation has a higher impact on performance but is also a more challenging operator to implement on an FPGA.
Are We There Yet? - Estimating Search Progress

Heuristic search is a general problem solving technique. While most evaluations of heuristic search focus on the speed of search, there are relatively few techniques for predicting when search will end. This paper provides a study of progress estimating techniques for optimal, suboptimal, and bounded suboptimal heuristic search algorithms. We examine two previously proposed techniques, search velocity and search vacillation, as well as two new approaches, path-based estimation and distribution-based estimation. We find that both new approaches are better at estimating the remaining amount of search effort than previous work in all three varieties of search, occasionally erring by less than 5%.
Characterising Third Party Cookie Usage in the EU after GDPR

The recently introduced General Data Protection Regulation (GDPR) requires that when obtaining information online that could be used to identify individuals, their consents must be obtained. Among other things, this affects many common forms of cookies, and users in the EU have been presented with notices asking their approvals for data collection. This paper examines the prevalence of third party cookies before and after GDPR by using two datasets: accesses to top 500 websites according to Alexa.com, and weekly data of cookies placed in users' browsers by websites accessed by 16 UK and China users across one year. We find that on average the number of third parties dropped by more than 10% after GDPR, but when we examine real users' browsing histories over a year, we find that there is no material reduction in long-term numbers of third party cookies, suggesting that users are not making use of the choices offered by GDPR for increased privacy. Also, among websites which offer users a choice in whether and how they are tracked, accepting the default choices typically ends up storing more cookies on average than on websites which provide a notice of cookies stored but without giving users a choice of which cookies, or those that do not provide a cookie notice at all. We also find that top non-EU websites have fewer cookie notices, suggesting higher levels of tracking when visiting international sites. Our findings have deep implications both for understanding compliance with GDPR as well as understanding the evolution of tracking on the web.
HydraFS: A High-Throughput File System for the HYDRAstor Content-Addressable Storage System

A content-addressable storage (CAS) system is a valuable tool for building storage solutions, providing efficiency by automatically detecting and eliminating duplicate blocks; it can also be capable of high throughput, at least for streaming access. However, the absence of a standardized API is a barrier to the use of CAS for existing applications. Additionally, applications would have to deal with the unique characteristics of CAS, such as immutability of blocks and high latency of operations. An attractive alternative is to build a file system on top of CAS, since applications can use its interface without modification. Mapping a file system onto a CAS system efficiently, so as to obtain high duplicate elimination and high throughput, requires a very different design than for a traditional disk subsystem. In this paper, we present the design, implementation, and evaluation of HydraFS, a file system built on top of HYDRAstor, a scalable, distributed, content-addressable block storage system. HydraFS provides high-performance reads and writes for streaming access, achieving 82-100% of the HYDRAstor throughput, while maintaining high duplicate elimination.