Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of immunological adjuvants

In this study, several innate immunological adjuvants and related compounds were compared with respect to complement activation in serum and induction of cytokine release in whole blood samples using immunoassays. As found, simple lipids had no effect on the complement system or on cytokine release, whereas lipopolysaccharides induced prominent release of pro‐inflammatory cytokines (IL1β, TNF and IFNγ) without affecting the complement system, except for one, which activated the lectin pathway (LP). Moreover, saponin induced IL1β and MCP1 release and did not affect the complement system. The polysaccharide inulin exhausted the alternative pathway (AP) completely without affecting the LP and the classical pathway (CP), whereas zymosan exhausted the AP and had a major effect on the LP and CP as well. Peptidoglycans mainly affected the LP. Inulin, agarose and cellulose induced IL1β and MCP1 release, while dextran had no effect on cytokine secretion. Zymosan mainly induced IL1β release. The inorganic compound aluminium hydroxide, Al(OH)3, activated the complement system very efficiently (all three pathways) but only induced MCP1 release. Other compounds tested had minor/individual effects. Collectively, well‐known adjuvants, such as aluminum hydroxide, activated the complement system and/or induced pro‐inflammatory cytokine release. Since complement activation generates anaphylactic peptides, a simple definition of an (innate) immunological adjuvant can be inferred: it activates the (innate) immune system by complement activation and/or release of cytokines so as to attract cells of the adaptive immune system.
Efficacy of clopidogrel for stroke depends on CYP2C19 genotype and risk profile

Dual antiplatelet therapy (DAT) with clopidogrel plus aspirin has been suggested by American Heart Association/American Stroke Association guidelines for minor stroke (MS) and transient ischemic attack (TIA) patients. The purpose of this study was to find the potential subgroups that benefit from DAT. We aimed to compare the efficacy of clopidogrel–aspirin therapy with that of aspirin therapy in MS/TIA patients stratified by CYP2C19 genotype and risk profiles.
Extraction d'information rythmique à partir d'enregistrements musicaux

This dissertation presents work on the development of tools for the automated analysis of music signals, more exactly it proposes a number of techniques to identify and examine the fundamental elements of musical rhythm. There exist many applications requiring rhythmic information, for example automatic music transcription, music information retrieval, audio special effects, audio editing. The main research axis consisted in the development of techniques to estimate rhythmic parameters of music such as beat rates and beat localizations at two different metrical levels: the tatum and the tactus (or tempo). In this work, we have proposed to carry out the analysis after separating the audio signal in a deterministic part (containing the harmonic sounds) and the stochastic part (containing the residual signal after extracting the harmonic part from the original signal). We exploited the principle of the so-called Spectral Energy Flux, i.e., the rate of change of the power spectrum in audio signals as a function of time, to develop an effective algorithm to estimate the amount of musical stress at a given time. We have also presented a technique based on the dynamic programming algorithm which was specially conceived to track simultaneously the course of several rhythmic trajectories through time. The effectiveness of the whole system and of a number of its derivatives was validated using a database comprising 1435 musical pieces and covering ten musical genres. In addition, the developed algorithms were submitted to external evaluation within the framework of the international contest "Music Information Retrieval Evaluation eXchange (MIREX)" in the category of "tempo extraction", and in the edition of 2005 one of our algorithms obtained the first place among more than twelve submissions.
Complications related to peri‐operative transoesophageal echocardiography – a one‐year prospective national audit by the Association of Cardiothoracic Anaesthesia and Critical Care

Previous studies on the safety of peri‐operative transoesophageal echocardiography seem to suggest a low rate of associated morbidity and mortality. That said, there has been a paucity of prospective multicentre studies in this important area of clinical practice. We carried out a one‐year prospective study in 2017, co‐ordinated by the Association of Cardiothoracic Anaesthesia and Critical Care, to determine the rate and severity of complications associated with peri‐operative transoesophageal echocardiography in anaesthetised cardiology and cardiac surgical patients. With the help of clinicians from 28 centres across the UK and Ireland, we recorded the total number of examinations conducted in anaesthetised patients during the study period. All major complications at each centre were prospectively reported and recorded. Of the 22,314 examinations, there were 17 patients diagnosed with a major complication which caused either palatal injury or gastro‐oesophageal disruption. This corresponds to an incidence of 0.08% (95%CI 0.05–0.13%) or approximately 1:1300 examinations. There were seven deaths reported during the study period which were directly attributed to these complications, corresponding to an incidence of 0.03% (95%CI 0.01–0.07%) or approximately 1:3000. These figures are higher than previously reported and suggest a high probability of death following the development of a complication (~40%). Most complications occurred in patients without known risk factors for transoesophageal echocardiography associated gastro‐oesophageal injury. We suggest clinicians and departments review their procedural guidelines, especially in relation to probe insertion techniques, together with the information communicated to patients when the risks and benefits of such examinations are discussed.
Human Papilloma Virus-Associated Cervical Cancer and Health Disparities

Cervical cancer develops through persistent infection with high-risk human papilloma virus (hrHPV) and is a leading cause of death among women worldwide and in the United States. Periodic surveillance through hrHPV and Pap smear-based testing has remarkably reduced cervical cancer incidence worldwide and in the USA. However, considerable discordance in the occurrence and outcome of cervical cancer in various populations exists. Lack of adequate health insurance appears to act as a major socioeconomic burden for obtaining cervical cancer preventive screening in a timely manner, which results in disparate cervical cancer incidence. On the other hand, cervical cancer is aggressive and often detected in advanced stages, including African American and Hispanic/Latina women. In this context, our knowledge of the underlying molecular mechanism and genetic basis behind the disparate cervical cancer outcome is limited. In this review, we shed light on our current understanding and knowledge of racially disparate outcomes in cervical cancer.
Hyper-G now Hyperwave : the next generation Web solution

Written by the team that created Hyper-G and its commercial version, HyperWave, this book is the definitive reference for the next generation hypermedia system for the Internet. If you have a large, complex and highly dynamic information space, and/or if you have, or are seeking to create, a high quality multi-author environment, read this book. The CD-ROM includes an evaluation copy of the HyperWave server, the native UNIX and MS-Windows clients as well as the HyperWave administration tools. Hyper-G was conceived and developed at the IICM of Graz University of Technology, in Austria, by a team headed by Hermann Maurer and Frank Kappe, both prolific and widely published authors. Visit the HyperWave pages and view the first five chapters of HyperWave: The Next Generation Web Solution .
Understanding Parallel I/O Performance Trends Under Various HPC Configurations

In high-performance computing (HPC) environments, an appropriate amount of hardware resources must be used for the best parallel I/O performance. For this reason, HPC users are provided with tunable parameters to change the HPC configurations, which control the amounts of resources. However, some users are not well aware of a relationship between the parallel I/O performance and the HPC configuration, and they thus fail to utilize these parameters. Even if users who know the relationship, they have to run an application under every parameter combination to find the setting for the best performance, because each application shows different performance trends under different configurations. The paper shows the result of analyzing the I/O performance trends for HPC users to find the best configurations with minimal efforts. We divide the parallel I/O characteristic into independent and collective I/Os and measure the I/O throughput under various configurations by using synthetic workload, IOR benchmark. Through the analysis, we have figured out that the parallel I/O performance is determined by the trade-off between the gain from the parallelism of increased OSTs and the loss from the contention for shared resources. Also, this performance trend differs depending on the I/O characteristic. Our evaluation shows that HPC applications also have similar performance trends as our analysis.
Semantic Cache Reasoners

Semantic caching (Ren, Q et al., 2003),(Dar et al., 1996) is said to be a technique for storing data and their corresponding semantic descriptions. Concept of semantic cache itself is quite simple but the reasoning required to evaluate any query over a semantic cache can be very complex (Godfrey P. and Gryz J., 1997). The reasoning over stored semantics is a determination process to know how query and cache formulas are related semantically. This reasoning is termed as semantic cache query processing (Ren, Q et al., 2003),(Dar et al., 1996). In this chapter we demonstrate several semantic cache query processing techniques for relational queries, web queries, xml queries, answering queries form materialized views and logic based subsumption analysis queries.
Fast and Accurate Entity Linking via Graph Embedding

Entity Linking, the task of mapping ambiguous Named Entities to unique identifiers in a knowledge base, is a cornerstone of multiple Information Retrieval and Text Analysis systems. So far, no single entity linking algorithm has been able to offer the accuracy and scalability required to deal with the ever-increasing amount of data in the web and become a de-facto standard. In this paper, we propose a framework for entity linking that leverages graph embeddings to perform collective disambiguation. This framework is modular as it supports pluggable algorithms for embedding generation and candidate ranking. With our framework, we implement and evaluate a reference pipeline that uses DBpedia as knowledge base and leverages specific algorithms for fast candidate search and high-performance state-space search optimization. Compared to existing solutions, our approach offers state-of-the-art accuracy on a variety of datasets without any supervised training and provides real-time execution even when processing documents with dozens of Named Entities. Lastly, the flexibility of our framework allows adapting to a multitude of scenarios by balancing accuracy and execution time.
Designing and implementing different use cases of aspect-oriented programming with AspectJ for developing mobile applications

The separation of concerns as a conceptual paradigm, aims to manage the complexity of the software systems by dividing them into different concerns and aspects. The benefits of this paradigm such as adaptability, reuse and maintenance, have been key drivers of its adoption and usability. These quality attributes have been discussed in aspect-oriented programming (AOP) which is a complement to traditional programming methods, whether object-oriented programming or procedural programming. The main concept of AOP is to gather the treatments related to a specific concern (problem) in a centralized unit called aspect. In this paper, two case studies of AOP are conducted with AspectJ: i) through three different implementations, addressing three distinct issues: Logging, Adding features and Using the Observer design pattern, and ii) through the design of aspect mobile application that is easy to use and quickly accessible and especially through Android devices. Also, the different technologies of AspectJ are used to design our application and illustrate the modularity and the composition of components (database manager, development software, and plugins). For each case study, we present the advantages and disadvantages to better understand and decompose the applications using this programming paradigm. Potential implementation examples are provided to support the explanation of the different situations and justify the use of the AOP to solve these issues. OSGi framework can also be used in the future work to offer dynamic manner of mobile applications.
Spatial envelope and background knowledge for scene classification problem

Scene classification problem is one of the major fields of research in artificial vision. The ability to assign the correct label to a scene can provide a significant advantage to automatic processes in order to achieve their task. This paper explores the possibility to classify a scene using objects as attributes and a discrete spatial envelope theory. The challenge is to be able to distinguish among all the existing objects the most discriminative ones in the scene using a proposed background knowledge and sorting functions. The classification process is then guided by a proposed discrete spatial envelope theory in order to provide an accurate and coherent category of scene. The proposed approach offers very satisfying results going up to 69.92% of well classified scenes on the very challenging SUN397 dataset. Compared to some existing state of the art methods, the proposed approach distinguishes itself by proposing a higher rate of classification.
A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics

Streaming analytics require real-time aggregation and processing of geographically distributed data streams continuously over time. The typical analytics infrastructure for processing such streams follow a hub-and-spoke model, comprising multiple edges connected to a center by a wide-area network (WAN). The aggregation of such streams often require that the results be available at the center within a certain acceptable delay bound. Further, the WAN bandwidth available between the edges and the center is often scarce or expensive, requiring that the traffic between the edges and the center be minimized. We propose a novel Time-to-Live (TTL-)based mechanism for real-time aggregation that provably optimizes both delay and traffic, providing a theoretical basis for understanding the delay-traffic tradeoff that is fundamental to streaming analytics. Our TTL-based optimization model provides analytical answers to how much aggregation should be performed at the edge versus the center, how much delay can be incurred at the edges, and how the edge-to-center bandwidth must be apportioned across applications with different delay requirements. To evaluate our approach, we implement our TTL-based aggregation mechanism in Apache Flink, a popular stream analytics framework. We deploy our Flink implementation in a hub-and-spoke architecture on geo-distributed Amazon EC2 data centers and a WAN-emulated local testbed, and run aggregation tasks for realistic workloads derived from extensive Akamai and Twitter traces. The delay-traffic tradeoff achieved by our Flink implementation agrees closely with theoretical predictions of our model. We show that by deriving the optimal TTLs using our model, our system can achieve a "sweet spot" where both delay and traffic are minimized, in comparison to traditional aggregation schemes such as batching and streaming.
Taking a long look at QUIC

Google's Quick UDP Internet Connections (QUIC) protocol, which implements TCP-like properties at the application layer atop a UDP transport, is now used by the vast majority of Chrome clients accessing Google properties but has no formal state machine specification, limited analysis, and ad-hoc evaluations based on snapshots of the protocol implementation in a small number of environ-merits. Further frustrating attempts to evaluate QUIC is the fact that the protocol is under rapid development, with extensive rewriting of the protocol occurring over the scale of months, making individual studies of the protocol obsolete before publication. Given this unique scenario, there is a need for alternative techniques for understanding and evaluating QUIC when compared with previous transport-layer protocols. First, we develop an approach that allows us to conduct analysis across multiple versions of QUIC to understand how code changes impact protocol effectiveness. Next, we instrument the source code to infer QUIC's state machine from execution traces. With this model, we run QUIC in a large number of environments that include desktop and mobile, wired and wireless environments and use the state machine to understand differences in transport-and application-layer performance across multiple versions of QUIC and in different environments. QUIC generally outperforms TCP, but we also identified performance issues related to window sizes, re-ordered packets, and multiplexing large number of small objects; further, we identify that QUIC's performance diminishes on mobile devices and over cellular networks.
Keddah: Network Evaluation Powered by Simulating Distributed Application Traffic

As a distributed system, Hadoop heavily relies on the network to complete data-processing jobs. While the traffic generated by Hadoop jobs is critical for job execution performance, the actual behaviour of Hadoop network traffic is still poorly understood. This lack of understanding greatly complicates research relying on Hadoop workloads. In this article, we explore Hadoop traffic through empirical traces. We analyse the generated traffic of multiple types of MapReduce jobs, with varying input sizes, and cluster configuration parameters. We present Keddah, a toolchain for capturing, modelling, and reproducing Hadoop traffic, for use with network simulators to better capture the behaviour of Hadoop. By imitating the Hadoop traffic generation process and considering the YARN resource allocation, Keddah can be used to create Hadoop traffic workloads, enabling reproducible Hadoop research in more realistic scenarios.
A learning rule for fuzzy associative memories

In this paper, a learning rule for multiple pattern pairs in fuzzy associative memories (FAMs) with max-min composition units is presented. Under a certain condition, the proposed rule can efficiently encode multiple fuzzy pattern pairs in a single FAM and perfect association of these pairs can be achieved. The correctness of the proposed rule is proved and illustrative examples are given. <<ETX>>
Normalization of XML Schema Definitions

An XML schema definition can be organized according to one of the following design styles: "Russian Doll", "Salami Slice", "Venetian Blind", or "Garden of Eden"; otherwise, it is said to be in "Bologna" style, which actually means free format. Converting a complex XML schema from a style to another without automated support is not a trivial task. The "Garden of Eden" style can be considered as a normalized format since the definitions of all components (i.e., elements, attributes and types) are global and, thus, can be re-used in the definition of other XML schema constructs (e.g., when expanding the schema or importing it into another schema or using it to define an XML namespace). In this work, we present a Normalize procedure, which can be used to convert any given XML schema to the "Garden of Eden" style, by automatically transforming and rearranging all declarations and definitions it contains.
Environmental Audio Scene and Sound Event Recognition for Autonomous Surveillance

Monitoring of human and social activities is becoming increasingly pervasive in our living environment for public security and safety applications. The recognition of suspicious events is important in both indoor and outdoor environments, such as child-care centers, smart-homes, old-age homes, residential areas, office environments, elevators, and smart cities. Environmental audio scene and sound event recognition are the fundamental tasks involved in many audio surveillance applications. Although numerous approaches have been proposed, robust environmental audio surveillance remains a huge challenge due to various reasons, such as various types of overlapping audio sounds, background noises, and lack of universal and multi-modal datasets. The goal of this article is to review various features of representing audio scenes and sound events and provide appropriate machine learning algorithms for audio surveillance tasks. Benchmark datasets are categorized based on the real-world scenarios of audio surveillance applications. To have a quantitative understanding, some of the state-of-the-art approaches are evaluated based on two benchmark datasets for audio scenes and sound event recognition tasks. Finally, we outline the possible future directions for improving the recognition of environmental audio scenes and sound events.
Android Malicious Application Detection Method Based on Multi-class Characteristics

With the development of malicious applications on the Android platform, detection methods for malicious applications have gradually become a hot research content. The existing analysis methods have some shortcomings: on the one hand, the same algorithm cannot fully detect different features; on the other hand, more and more malicious code uses dynamic loading mechanism. In view of the above limitations, this paper proposes a method to detect different features by using a variety of classification algorithms. First, two types of permissions to be applied for detection and function call are extracted. Mutual information processing is used to reduce data that has little impact on classification. Then, different classification algorithms are used for calculation to select the optimal algorithm for the two characteristic attributes. Finally, according to the optimal algorithm to determine whether the application to be detected belongs to malicious applications Experimental results show that the proposed method has a high recognition rate and can effectively identify malicious applications.
Alvus: A Reconfigurable 2-D Wireless Charging System

Wireless charging pads such as Qi are rapidly gaining ground, but their limited power supply range still requires precise placement on a specific point. 2-D wireless power transfer (WPT) sheets consisting of coil arrays are one well-known counterpart to extend this range. However, these approaches require custom-made designs by expert engineers; what we need is a WPT system that can be reconfigured by simply placing ready-made modules on the intended surface (e.g., table, floor, shelf board, etc). In this paper, we present "Alvus", a reconfigurable 2-D WPT system which enables such simple construction of WPT surfaces. Our system is based on multihop WPT that composes "virtual power cords" and consists of three types of ready-made resonator modules: (i) transmitter, which outputs energy, (ii) relays, which pass energy down to the next module, and (iii) receivers, which receive energy and charge the loads. We show that power can be transferred efficiently (over 25%) within a range of 19.6 m2 using a single transmitter. We implemented an end-to-end WPT system and demonstrated that Alvus is capable of intuitive construction/reconfiguration of WPT surfaces, as well as automatically deciding the power routes based on the sensed information (e.g., receiver location, module placement, obstructive objects).