Miracle-dz/UniFAR-FTUs huggingface dataset:
Saudi Moumouvirus, the First Group B Mimivirus Isolated from Asia

The number of novel giant viruses identified and characterized from the recently proposed order Megavirales has increased in recent years and new questions have been raised regarding viral diversity and evolution. Here, we describe the isolation and characterization of Saudi moumouvirus (SDMV), a new giant virus belonging to Mimivirus lineage B, isolated from a sewage sample taken from the King Abdulaziz University hospital in Jeddah, Saudi Arabia. SDMV presented 500 nm icosahedral particles with a 1,046,087 bp genome, which is larger than moumouvirus-like genomes which have been described in the past. The SDMV genome was predicted to encode 868 ORFs, ranging in size from 54 to 2,914 amino acids, with a mean size of 349 aa. Furthermore, this genome was predicted to encode 40 new genes (ORFans) without similarity with other sequences (ORFan L850 transcript was detected by qPCR in infected amoeba), in addition to 42 hypothetical proteins (pseudo-ORFs) with less than 100 aa, which matched other sequences in the NCBI nr database. Phylogenetic analysis showed that SDMV clustered together with mimiviruses from lineage B, including moumouvirus-like strains. It is, therefore, the third Mimivirus to be isolated in Asia and the first of group B.
Random Forest Random Field

We combine random forest (RF) and conditional random field (C RF) into a new computational framework, called random forest random field (RF). Inference of (RF) uses the Swendsen-Wang cut algorithm, characterized by Met ropolisHastings jumps. A jump from one state to another depends on th e ratio of the proposal distributions, and on the ratio of the posterior di stributions of the two states. Prior work typically resorts to a parametric estima tion of these four distributions, and then computes their ratio. Our key idea is to instead directly estimate these ratios using RF. RF collects in leaf nodes of eac h de ision tree the class histograms of training examples. We use these class hi stograms for a nonparametric estimation of the distribution ratios. We deriv the theoretical error bounds of a two-class (RF). (RF) is applied to a challenging task of multiclass object recognition and segmentation over a random field of in put image regions. In our empirical evaluation, we use only the visual informat ion provided by image regions (e.g., color, texture, spatial layout), whereas th e competing methods additionally use higher-level cues about the horizon location a nd 3D layout of surfaces in the scene. Nevertheless, (RF) outperforms the state of the art on benchmark datasets, in terms of accuracy and computation time.
Optical and Infrared Photometry of the Nearby Type Ia Supernovae 1999ee, 2000bh, 2000ca, and 2001ba

We present near-infrared photometry of the Type Ia supernova (SN) 1999ee; also, optical and infrared photometry of the Type Ia SNe 2000bh, 2000ca, and 2001ba. For SNe 1999ee and 2000bh, we present the first-ever SN photometry at 1.035 μm (the Y band). We present K-corrections that transform the infrared photometry in the observer's frame to the supernova rest frame. Using our infrared K-corrections and stretch factors derived from optical photometry, we construct JHK templates that can be used to determine the apparent magnitudes at maximum if one has some data in the window -12 to +10 days with respect to T(Bmax). Following up previous work on the uniformity of V minus IR loci of Type Ia supernovae of midrange decline rates, we present unreddened loci for slow decliners. We also discuss evidence for a continuous change of color at a given epoch as a function of decline rate.
A Wide Tuning Range CMOS Voltage Controlled Oscillator

In this paper a new wide tuning range CMOS voltage controlled oscillator has been introduced. Two techniques have been used simultaneously to increase the tuning range of a ring oscillator. By combining current control and bulk forward biasing techniques the oscillation frequency can be controlled over a large frequency range with a low phase noise of -86dBc/Hz at 1MHz offset from 5.8GHz center frequency. The TSMC 180nm CMOS process with 1.8V supply voltage has been used for the design. The proposed voltage controlled oscillator has 1.73-9.27GHz frequency range with control voltage of 0-1V and consumes 5.17mW of power at center frequency oscillation.
Brain Tumour Detection In MRI Images Using Matlab

Now a days Medical image processing is the most challenging and emerging field. The image processing is an important aspect of medical science to visualize the different anatomical structure of human body . Magnetic Resonance Imaging (MRI) is one of the significant techniques for examining human body. This paper describes how to detect and extraction of brain tumour from patient’s MRI scan images of the brain. Here by using MATLAB software and using the basic concept of image processing, detection and extraction of tumour from MRI scan images of the brain is done.
“We don't need services. We have no problems”: exploring the experiences of young people who inject drugs in accessing harm reduction services

Evidence suggests that people who inject drugs often begin their drug use and injecting practices in adolescence, yet there are limited data available on the HIV epidemic and the responses for this population. The comprehensive package of interventions for the prevention, treatment and care of HIV infection among people who inject drugs first laid out in 2009 (revised in 2012) by World Health Organization, United Nations Office of Drugs and Crime and Joint United Nations Programme on HIV/AIDS, does not consider the unique needs of adolescent and young people. In order to better understand the values and preferences of young people who inject drugs in accessing harm reduction services and support, we undertook a series of community consultations with young people with experience of injecting drugs during adolescence.
An ensemble method using small training sets for imbalanced data sets: Application to drugs used for kinases

Nearly all aspects of cell life and death are controlled by the phosphorylation of proteins which are catalyzed by kinases. Malfunctioning of kinases results in cell disorders causing cancers and other diseases. The present study deals with the identification of predominant features present in the inhibitors targeting these enzymes and classification of the kinase and non-kinase inhibitors using machine learning algorithms. The present work deals with two challenges. The first challenge is the classification of unbalanced data sets. Unbalanced data sets are the data sets in which there is an imbalance in the size of data sets that constitute these sets. The second challenge is the concept complexity (closely related minority and majority data sets in the feature space). Our approach deals with the binary classification of approved human inhibitors present in the Drug bank database into kinase and non-kinase inhibitors. Clustering of the inhibitors followed by classification using an ensemble consisting of several classification models is generated. Classification is done in two levels. Weighted voting is used after each level. Finally an overall accuracy of 80% is obtained after two levels of classification. Thus we established a new a type of approach for the classification of unbalanced data sets and the data sets in which there is an overlap between instances belonging to dierent classes. Finally we established a signature specific to kinase inhibitors.
A speaker tracking system based on speaker turn detection for NIST evaluation

A speaker tracking system (STS) is built by using successively a speaker change detector and a speaker verification system. The aim of the STS is to find in a conversation between several persons (some of them having already enrolled and other being totally unknown) target speakers chosen in a set of enrolled users. In a first step, speech is segmented into homogeneous segments containing only one speaker, without any use of a priori knowledge about speakers. Then, the resulting segments are checked to belong to one of the target speakers. The system has been used in a NIST evaluation test with satisfactory results.
50-Gbit/s 4-bit multiplexer/demultiplexer chip-set using InP HEMTs

This paper reports on the 50-Gbit/s 4:1 multiplexer (MUX) and 1:4 demultiplexer (DMUX) chip-set using InP HEMTs. In order to achieve high and wide-range bit-rate operation, timing design inside the ICs was precisely executed. The packaged MUX and DMUX achieved 50 Gbit/s back-to-back error-free operation for 2/sup 31/-1 pseudo-random bit streams (PRBS). Furthermore, the MUX operated from 4 to 50 Gbit/s with >1 V/sub pp/ output amplitude, and the DMUX exhibited >180-degrees phase margin from 4 to 50 Gbit/s for 2/sup 31/-1 PRBS.
Elements of Financial Risk Management

The Second Edition of this best-selling book expands its advanced approach to financial risk models by covering market, credit, and integrated risk. With new data that cover the recent financial crisis, it combines Excel-based empirical exercises at the end of each chapter with online exercises so readers can use their own data. Its unified GARCH modeling approach, empirically sophisticated and relevant yet easy to implement, sets this book apart from others. Five new chapters and updated end-of-chapter questions and exercises, as well as Excel-solutions manual, support its step-by-step approach to choosing tools and solving problems. Examines market risk, credit risk, and operational risk Provides exceptional coverage of GARCH models Features online Excel-based empirical exercises
The Evolution of Knowledge Representation within Embrapa's Information Agency

Knowledge representation has always been a major concern when designing systems for technology transfer because of its impact on the system’s usability. The Embrapa’s Information Agency was designed as a space for exchanging knowledge among farmers, agricultural researchers and rural extension technicians. In its first version, the system represented the knowledge through ontologies realized as trees displayed in hyperbolic space. This strategy, although appropriate for the users of the system, posed some difficulties when comparing ontologies developed for sub-domains of the agricultural domain. This fact, motivated the search for a new representation that should have a good usability whereas allowing such comparisons.
Technology Transfer in Cross-national Context: Experiences of an Online Market Failure in Chinese Food Industry

This case study considers an online market adoption as a process of technology transfer across contexts. This conception enables us to analyze the adoption difficulties from a technology inscription lens. This lens suggests that technology was developed based on a particular context which assumes how technology can be best used and how certain “practices” are inscribed. In this view, the adoption of online market not only implements the technology in the recipient organization, but also transfers the practices inscribed in the technology to the recipient. However, the inscribed practice may not necessarily be assimilated into the situated practices in recipient organization. As a result, the e-marketplace will be considered as inapplicable in the recipient context, given that cultural inertia and unfavorable adoption conditions (i.e. the poor technology infrastructure) are improved. The industry dynamics are highlighted to understand e-marketplace adoption challenges. Practical and theoretical implications of the technology inscription analysis are discussed with reference to technology alignment and global technology transfer.
Determination of Cytokine Protein Levels in Cervical Mucus Samples from Young Women by a Multiplex Immunoassay Method and Assessment of Correlates

ABSTRACT Cytokines in cervical mucus are likely to play important roles in controlling pathogens. The cervical mucosal environment is complex, however, with many endogenous and exogenous factors that may affect cytokine levels. We used a multiplex, suspension-array-based immunoassay method to measure 10 proinflammatory (interleukin-1β [IL-1β], IL-6, and IL-8) and immunoregulatory (gamma interferon [IFN-γ], IL-2, IL-4, IL-5, IL-10, IL-12, and IL-13) cytokines in cervical mucus specimens collected via ophthalmic sponge from 72 healthy, nonpregnant women and correlate their levels with biologic and behavioral covariates in a cross-sectional design. Proinflammatory and immunoregulatory cytokines were readily detected, although proinflammatory cytokines were present at markedly higher levels than were immunoregulatory cytokines. Among the covariates examined, the most striking finding was the significant (P ≤ 0.05) association between depressed levels of the cytokines IFN-γ, IL-1β, IL-6, and IL-10 and cigarette smoking. Also, nonsignificant trends toward lower cytokine levels were found in the settings of incident and persistent human papillomavirus infection. The ready detection of proinflammatory cytokines may be reflective of the female genital tract as an anatomic site that is constantly exposed to immunogenic stimulation. Cigarette smoking appears to downregulate cytokine responses in the cervical mucosa, which may help explain the implicated role of tobacco use as a cofactor for cervical cancer development.
Covert Debugging Circumventing Software Armoring Techniques

Software armoring techniques have increasingly crea ted problems for reverse engineers and software security analyst s. As protections such as packers, run-time obfuscators, virtual machine and debugger detectors become common, newer methods must be developed to cope with them. In this paper we will present our covert debugging platform named Saffron. Saffron i s based upon dynamic instrumentation techniques as well as a new ly developed page fault assisted debugger. We show that the com bination of these two techniques is effective in removing armor ing from most software armoring systems.
Benefit Provision in a Cyclic Economy

We discuss business cycle effects on the management of a trust fund set up to provide regular income on a continuing basis. Fund managers must find a balance between short-term and long-term variability of income. In our model the managers know that the expected return is mean-reverting, but they have limited capability of learning the true current state of the cycle. We consider policies that are optimal under constant relative risk aversion, and we contrast these with a parametric class of policies in which the asset mix is fixed and the estimated business cycle variable is only used in the consumption decision. In a calibration exercise, we find that optimal decisions are based on an exponentially weighted history of past asset returns with a half-time of about seven years. The tradeoff between short-term and long-term variability of income is illustrated by simulation results.
Bounding Techniques

• Some bounding techniques are investigated in this section in order to serve as a tool for the analysis that follows. We first state the union upper bound that is widely used in the literature. • Then, some better bounds which are applicable to AWGN channels are also discussed here; they are Gallager bound and sphere upper bound.
HG-ckpt: A tybrid granularity checkpoint frame

Checkpoint-restart techniques are very important fault-tolerance approaches for these systems. As we know, the overhead of checkpoint will influence the system performance seriously. Incremental checkpoint is a well researched technique to reduce the overhead of checkpoint. However, huge memory footprints of parallel applications place severe limitations on scalability of conventional incremental checkpointing techniques. Recently, the emerging Phase Change Memory technology is becoming available and can replace disks with the fast accesses and byte-address features. In this paper, we propose and implement a new incremental checkpoint frame named HG-ckpt, which uses both page-level and byte-level granularity to detect dirty data. Our experiment results show that HG-ckpt can reduce checkpoint file size up to 50% without other overhead when compared to conventional page-level incremental checkpoint on PCM.
Security of VoIP : Analysis, Testing and Mitigation of SIP-based DDoS attacks on VoIP Networks

Voice over IP (VoIP) is gaining more popularity in today's communications. The Session Initiation Protocol (SIP) is becoming one of the dominant VoIP signalling protocol[1, 2], however it is vulnerable to many kinds of attacks. Among these attacks, flood-based denial of service attacks have been identified as the major threat to SIP. Even though a great deal of research has been carried out to mitigate denial of service attacks, only a small proportion has been specific to SIP. This project examines the way denial of service attacks affect the performance of a SIP-based system and two evolutionary solutions to this problem that build on each other are proposed with experimental results to demonstrate the effectiveness of each solution. In stage one, this project proposes the Security-Enhanced SIP System (SESS), which contains a security-enhanced firewall, which evolved from the work of stage one and a security-enhanced SIP proxy server. This approach helps to improve the Quality-of-Service (QoS) of legitimate users during the SIP flooding attack, while maintaining a 100 percent success rate in blocking attack traffic. However, this system only mitigates SIP INVITE and REGISTER floods. In stage two, this project further advances SESS, and proposes an Improved Security-Enhanced SIP System (ISESS). ISESS advances the solution by blocking other SIP request floods, for example CANCEL, OK and BYE flood. JAIN Service Logic Execution Environment (JAIN SLEE) is a java-based application server specifically designed for event-driven applications. JAIN SLEE is used to implement enhancements of the SIP proxy server, as it is becoming a popular choice in implementing communication applications. The experimental results show that during a SIP flood, ISESS cannot only drop all attack packets but also the call setup delay of legitimate users can be improved substantially compared to and unsecured VoIP system.
Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.
Retrieving Vertical Ionospheric Electron Density Profile with an Improved Abel Inversion Algorithm

Vertical electron density profile is an important source of information on the Earth’s ionosphere. It is well known that such vertical electron density profile can be retrieved using LEO (Low Earth Orbit) satellite RO (radio occultation) data. With more and more LEO satellite data available (such as CHAMP, SAC_C, GRACE, COSMIC), the retrieved vertical electron density can be more accurate if some proper models are used. Nowadays, there are two major vertical electron density retrieve methods with the LEO data: tomographic reconstruction and the Abel inversion. Compared with the tomographic reconstruction technology, the Abel inversion method is achieved by small amount of RO data and a simple algorithm. But the drawback of the Abel inversion is that the retrieved vertical electron density profile only shows the vertical electron density average variation over a large area rather than accurate altitude variation in a latitude and longitude position. This problem is caused by the assumption of electron density local spherical symmetry and even distribution. When a RO passes a few thousands kilometers, it is not reasonable to assume the electron density along the RO ray path varies only respect to altitude without considering both latitude and longitude factors. To improve the accuracy of the Abel inversion model, the one dimensional (1D) shape function is introduced. It is defined as the function of altitude, and its value with respect to a point is equal to the vertical TEC (Total Electron Content) at the point divided by the electron density at the point. This definition makes the solved electron density not only depend on altitude (represented by the shape function) but also be related to latitude and longitude (represented by vertical TEC --the function of latitude and longitude). Although the 1D shape function increases the model accuracy, it is still not realistic to assume that the shape function relies only on altitude. In this paper, the two dimensional (2D) shape function is used to further improve the accuracy of the 1D shape function model. The 2D shape function is defined as a function of altitude and the zenith angle with regard to the local center or a GPS (Global Positioning System) receiver. The value of the 2D shape function is first solved by combining ground based GPS data and RO data. Then based on the solved 2D shape function, the vertical electron density profiles in the local area are generated. This paper will describe the details of the proposed method and also discuss the numerical results generated from the proposed 2D model, the 1D shape function model and the IRI model
Visual masking phenomena with high dynamic range content

High Dynamic Range (HDR) technology (capture and display), can offer high levels of immersion through a dynamic range that meets and exceeds that of the Human Visual System (HVS). This increase in immersion comes at the cost of higher bitrate requirements, which necessitate the development of efficient HDR-relevant coding solutions. Efficient perception-based compression of HDR imagery requires models that capture accurately the various masking effects experienced by the HVS under HDR conditions, so that bits are not wasted coding redundant imperceptible information. In this paper we present two psychovisual experiments that we carried out with the aid of a high dynamic range display, in order to determine potential differences between Standard Dynamic Range (SDR) and HDR edge masking (EM) and luminance masking (LM) effects. The EM experimental results indicate that the visibility threshold is higher for the case of HDR content than SDR, especially on the dark background side of an edge. The LM experimental results suggest that the HDR visibility threshold is higher compared to SDR for both dark and bright luminance backgrounds.