Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection Using Neighborhood Negative Selection

Abstract Negative Selection Algorithms (NSAs) have been widely used in anomaly detection. As the security issue becomes more complex, more and more anomaly detection schemes involve high-dimension data. NSAs however perform poorly on effectiveness and efficiency when dealing with high-dimension data. To address these issues, we propose a Neighborhood Negative Selection (NNS) algorithm in this paper. Instead of a single data point, NNS uses a neighborhood to represent a self sample (or a detector). As a result, the training efficiency is greatly improved. We further introduce a special matching mechanism to limit the negative effect of the dimensionality of a shape space and improve the detecting performance in high dimensions. The experimental results show that NNS can provide a more accurate and stable detection performance. Meanwhile, both theoretical analysis and experimental results show that NNS further improves the training efficiency.
Involvement of the Mannose Receptor in Infection of Macrophages by Influenza Virus

ABSTRACT Influenza viruses A/PR/8/34 (PR8; H1N1), A/Aichi/68 X-31 (HKx31; H3N2), and A/Beijing/89 X-109 (BJx109; H3N2) show marked differences in their ability to infect murine macrophages, including resident alveolar and peritoneal macrophages as well as the macrophage-derived cell line J774. The hierarchy in infectivity of the viruses (PR8 < HKx31 < BJx109) resembles that of their reactivity with mannose-binding lectins of the collectin family. Since the macrophage mannose receptor recognizes the same spectrum of monosaccharides as the collectins do, we investigated the possible involvement of this receptor in infection of macrophages by influenza virus. In competitive binding studies, the binding of 125I-labeled mannosylated bovine serum albumin to macrophages was inhibited by the purified hemagglutinin and neuraminidase (HANA) glycoproteins of influenza virus but not by HANA that had been treated with periodate to oxidize its oligosaccharide side chains. The inhibitory activity of HANA from the three strains of virus differed markedly and correlated with the infectivity of each virus for macrophages. Infection of macrophages, but not MDCK cells, by influenza virus was inhibited by yeast mannan. A variant line of J774 cells, J774E, which expresses elevated levels of the mannose receptor, was more readily infected than J774, and the sensitivity of J774E cells to infection was greatly reduced by culture in the presence of d-mannose, which down-modulated mannose receptor expression. Together, the data implicate the mannose receptor as a major endocytic receptor in the infectious entry of influenza virus, and perhaps other enveloped viruses, into murine macrophages.
A Web-based High-Throughput Tool for Next-Generation Sequence Annotation

The availability of a large number of genome sequences, resulting from inexpensive, high-throughput next-generation sequencing platforms, has created the need for an integrated, fully-automated, rapid, and high-throughput annotation capability that is also easy-to-use. Here, we present a web-based software application, Annotation of Genome Sequences (AGeS), which incorporates publicly-available and in-house-developed bioinformatics tools and databases, many of which are parallelized for high-throughput performance. The current version of AGeS provides annotations for bacterial genome sequences, and serves as a readily-accessible resource to Department of Defense (DoD) scientists for storing, annotating, and visualizing genomes of newly-sequenced pathogens of interest. The AGeS system is composed of two major components. The first component is a web-based application that provides a graphical user interface for managing users’ input genomes, submitting annotation jobs, and visualizing results. Sequence contigs are uploaded as a multi-FASTA input file and submitted for annotation, and the resulting annotations are visualized through GBrowse. The input genome sequences and the annotation results are stored in a secure, customized database. The second component is a high-throughput annotation pipeline for finding the genomic regions that code for proteins, RNAs, and other genomic elements through a Do-It-Yourself Annotation framework. The pipeline also functionally annotates the protein-coding regions using an in-house-developed high-throughput pipeline, the Pipeline for Protein Annotation. The annotation pipeline has been deployed on the Mana Linux cluster at the Maui High Performance Computing Center. The two components are connected together using the DoD user interface toolkit application programming interface. The AGeS system was evaluated for scaling of its parallel execution and annotation performance. AGeS scaled with super-linear speedup for up to 128 processors, after which performance degraded. A 2.2-Mbp bacterial genome sequence can be annotated in ~1 hr using 128 processors. AGeS annotations of draft and complete genomes were compared with the original annotations from three different sources, and were found to be in general agreement with them.
Modeling the slowdown of data-parallel applications in homogeneous and heterogeneous clusters of workstations

Data-parallel applications executing in multi-user clustered environments share resources with other applications. Since this sharing of resources dramatically affects the performance of individual applications, it is critical to estimate its effect, i.e., the application slowdown, in order to predict application behavior. The authors develop a new approach for predicting the slowdown imposed on data-parallel applications executing on homogeneous and heterogeneous clusters of workstations. The model synthesizes the slowdown on each machine used by an application into a contention measure-the aggregate slowdown factor-used to adjust the execution time of the application to account for the aggregate load. The model is parameterized by the work (or data) partitioning policy employed by the targeted application, the local slowdown (due to contention from other users) present in each node of the cluster and the relative weight (capacity) associated with each node in the cluster. This model provides a basis for predicting realistic execution times for distributed data-parallel applications in production clustered environments.
Neuronal Nitric Oxide Synthase Self-inactivates by Forming a Ferrous-Nitrosyl Complex during Aerobic Catalysis (*)

Neuronal NO synthase (NOS) is a flavin-containing hemeprotein that generates NO from L-arginine, NADPH, and O. NO has recently been proposed to autoinhibit NOS. We have investigated whether a NOS heme-NO complex forms during aerobic steady-state catalysis. Visible and resonance Raman spectra recorded during steady-state NO synthesis by NOS showed that the majority of enzyme (70-90%) was present as its ferrous-nitrosyl complex. Ferrous-nitrosyl NOS formed only in the coincident presence of NADPH, L-arginine, and O. Its level remained constant during NO synthesis until the NADPH was exhausted, after which the complex decayed to regenerate ferric resting NOS. Stopped-flow measurements revealed that the buildup of the ferrous-NO complex was rapid (<2 s) and caused a 10-fold decrease in the rate of NADPH consumption by NOS. Complex formation and decay could occur several times with no adverse affect on its subsequent formation or on NOS catalytic activity. Neither enzyme dilution nor NO scavengers (superoxide and oxyhemoglobin) diminished formation of ferrous-nitrosyl NOS or prevented the catalytic inhibition attributed to its formation. The ferrous-nitrosyl complex also formed in unfractionated cell cytosol containing neuronal NOS upon initiating NO synthesis. We conclude that a majority of neuronal NOS is converted quickly to a catalytically inactive ferrous-nitrosyl complex during NO synthesis independent of the external NO concentration. Thus, NO binding to the NOS heme may be a fundamental feature of catalysis and functions to down-regulate NO synthesis by neuronal NOS.
A Framework for a Collaborative DDoS Defense

Increasing use of the Internet for critical services makes flooding distributed denial-of-service (DDoS) a top security threat. A distributed nature of DDoS suggests that a distributed mechanism is necessary for a successful defense. Three main DDoS defense functionalities - attack detection, rate limiting and traffic differentiation - are most effective when performed at the victim-end, core and source-end respectively. Many existing systems are successful in one aspect of defense, but none offers a comprehensive solution and none has seen a wide deployment. We propose to harvest the strengths of existing defenses by organizing them into a collaborative overlay, called DefCOM, and augmenting them with communication and collaboration functionalities. Nodes collaborate during the attack to spread alerts and protect legitimate traffic, while rate limiting the attack. DefCOM can accommodate existing defenses, provide synergistic response to attacks and naturally lead to an Internet-wide response to DDoS threat
Synchronization with Auditory Models of Varying Complexity

This experiment is an attempt to determine the accuracy with which Ss can synchronize with repetitive models of auditory stimuli. Ss reproduced [he models simultaneously with their presentation, copying both the onset and duration of the tones and the silence durations between them. Although three Es (Dunlap, 1910; Woodrow, 1932; Bartlert & Barrlett, 1959) investigated accuracy of synchronization, none of them used complex models. They studied the effects of differences in silence durations where duration, frequency, and intensity of tone were held constant. Since Dunlap contended with a faulcy kymograph, his data are not treated. Woodrow's six Ss synchronized with eight series of clicks; the silence durations ranged from . 25 through 4.0 sec. The lowest mean standard error in relative units ( the proportion of error X the silence duration) was ,039 when the rate was . SO sec. between clicks (75 clicks per min.). The error index increased regularly on both sides of this rate, reaching . 065 at the slowest rate and . 10 at the fastest (see Fig. 1). Bartlett and Bartlett's three Ss synchronized at will with a single click in a series. The six rates tested covered the same range as Woodrow's. The lowest mean standard error (relative units) occurred with the 2.0-sec. duracion
Effects of Integrating Technology on the Fitness Levels of Elementary Students

The purpose of this research was to determine the effects of using technology in physical education classes on the cardiovascular endurance of fourth grade students. Three classes of fourth grade students were randomly assigned to Heart Rate Monitor (HRM), Pedometer, or Control groups and participated in the same physical education activities for 24 weeks. Comparisons on the ½ mile fitness run of the 3 groups were not significantly different from each other (p = .3580) at the beginning of the study. There were no significant correlations (p > 0.05) between activity levels and mile run times after the 24 weeks. A two-way (Treatment x Gender) ANOVA was used to analyse mile run times after the 24 weeks. The main effect of gender (p 0.05) and the Treatment x Gender interaction (p > 0.05) were not.
Multiple UAV coalition formation

Unmanned aerial vehicles (UAVs) have the potential to carry munitions in support of battlefield operations, however they have limited sensor range and can carry only small quantities of resources. Often, to fully prosecute a target, a variety of assets may be required, and it may be necessary to deliver these assets simultaneously. Therefore, a team of UAVs that satisfies the target resource requirement needs to be assigned to a single target, and this team is called a coalition. Other desired requirements for the coalition are (i) minimize the target prosecution delay and (ii) minimize the size of the coalition. In this paper, we propose a two-stage optimal coalition formation algorithm that assigns appropriate numbers of UAVs satisfying the desired requirements. We developed a Dubins curves based simultaneous strike scheme. Simulation results are presented to show that the two-stage coalition formation algorithm has low computational overhead and can be applied in real-time.
ConnectionistModeling of Language : Examplesand Implications

Researchers interestedin humancognitiveprocesses have longusedcomputersimulationsto try to identify theprinciplesof cognition.Thestrategy hasbeento build computationalmodelsthatembodyputative principlesandthen to examinehow well suchmodelscapturehumanperformancein cognitive tasks.Until the1980’s,thiseffort was undertakenwithin thecontext of the“computermetaphor” of mind. Researchers built computationalmodelsbased on the conceptualizationthat the humanmind operated asthoughit werea conventionaldigital computer . However, with the advent of so-calledconnectionist,neural network, or parallel distributed processingmodels(Anderson,Silverstein,Ritz,& Jones,1977;Hinton& Anderson,1981;McClelland& Rumelhart,1981;McClelland, Rumelhart,& the PDP ResearchGroup, 1986; Rumelhart, McClelland, & the PDP ResearchGroup, 1986b), researchers beganexploringtheimplicationsof principles thataremorebroadlyconsistentwith thestyleof computationemployedby thebrain.
Generic factors influencing optimal LAN size for commonly used operating systems maximized for network performance

Summary Information technology infrastructure is a critical element in modern day communication. Businesses, organizations, individuals, governments and other social services all rely on global IT infrastructure for reliable and timely communication. At the heart of this infrastructure are numerous hardware and software components, and the performance of each individual element contributes to the overall success of the installation. One such software component that is vital for communication is an operating system (software that makes a computer and network work). With enhancements in technology, these operating systems are becoming more and more sophisticated in their functionality, and at least three mainstream vendor products are available in the market. It is common for software vendors to claim that their product out-perform competitors offerings, in functionality and performance. This paper reviews the literature for a current project that identifies generic factors that influence performance of a LAN. The focus is on both the performance and the metrics of commonly used operating systems implemented to create IT infrastructure. It identifies performance analysis, Internet protocols and wireless as major themes in literature. A number of gaps in the literature, related to network performance analysis, are identified in Section 4 of the paper.
3D active net for volume extrac-tion

3D Active Net, which is a 3D extension of Snakes, is an energy-minimizing surface model which can extract a volume of interest from 3D volume data. It is deformable and evolves in 3D space to be attracted to salient features, according to its internal and image energy. The net can be tted to the contour of a target object by the de nition of the image energy suitable for the contour property. It is an alternative way to the extraction of a desired 3D object by manual segmentation or by using a slice by slice approach. We present testing results of the extraction of a muscle from the Visible Human Data by two methods: manual segmentation and the application of 3D Active Net. We apply principal component analysis, which utilizes the color information of the 3D volume data to emphasize an ill-de ned contour of the muscle, and then apply 3D Active Net. We recognize that the extracted object has a smooth and natural contour in contrast with a comparable manual segmentation, proving an advantage of our approach.
Nonsubsampled Contourlet Transform for Texture Classifications using Support Vector Machines

In this paper, a new texture classification method using the nonsubsampled contourlet transform (NSCT) and support vector machines (SVMs) is proposed. The NSCT provides a shift-invariant, multiscale, and multidirectional image representation that has proven to be very efficient in image analysis applications. Firstly, features are extracted from NSCT coefficients of source images. In addition, SVMs, which have been demonstrated excellent performance as classifiers in a variety of pattern recognition problems, are used as classifiers for texture classification. The algorithm is tested on texture images from Brodatz album. Experimental results demonstrate that the proposed method produces more accurate classification results than other methods.
Line-of-Sight Pursuit in Strictly Sweepable Polygons

We study a turn-based game in a simply connected polygonal environment $Q$ between a pursuer $P$ and an adversarial evader $E$. Both players can move in a straight line to any point within unit distance during their turn. The pursuer $P$ wins by capturing the evader, meaning that their distance satisfies $d(P, E) \leq 1$, while the evader wins by eluding capture forever. Both players have a map of the environment, but they have different sensing capabilities. The evader $E$ always knows the location of $P$. Meanwhile, $P$ only has line-of-sight visibility: $P$ observes the evader's position only when the line segment connecting them lies entirely within the polygon. Therefore $P$ must search for $E$ when the evader is hidden from view. We provide a winning strategy for $P$ in the family of strictly sweepable polygons, meaning that a straight line $L$ can be moved continuously over $Q$ so that (1) $L \cap Q$ is always convex and (2) every point on the boundary $\partial Q$ is swept exactly once. This strict sweeping requires that $L$ moves along $Q$ via a sequence of translations and rotations. We develop our main result by first considering pursuit in the subfamilies of monotone polygons (where $L$ moves by translation) and scallop polygons (where $L$ moves by a single rotation). Our algorithm uses rook strategy during its active pursuit phase, rather than the well-known lion strategy. The rook strategy is crucial for obtaining a capture time that is linear in the area of $Q$. For monotone and scallop polygons, our algorithm has a capture time of $O(n(Q) + \mbox{area}(Q))$, where $n(Q)$ is the number of polygon vertices. The capture time bound for strictly sweepable polygons is $O( n(Q) \cdot \mbox{area}(Q) )$.
Pamidronate treatment of severe osteogenesis imperfecta in children under 3 years of age.

Severe osteogenesis imperfecta (OI) is a hereditary disorder characterized by increased bone fragility and progressive bone deformity. Cyclical pamidronate infusions improve clinical outcome in children older than 3 yr of age with severe OI. Because earlier treatment may have potential to prevent deformities and improve functional prognosis in young children, we studied nine severely affected OI patients under 2 yr of age (2.3-20.7 months at entry) for a period of 12 months. Pamidronate was administered i.v. in cycles of 3 consecutive days. Patients received four to eight cycles during the treatment period, with cumulative doses averaging 12.4 mg/kg. Clinical changes were evaluated regularly during treatment, and radiological changes were assessed after 6-12 months of treatment. The control group consisted of six age-matched, severely affected OI patients, who had not received pamidronate treatment. During treatment bone mineral density (BMD) increased between 86-227%. The deviation from normal, as indicated by the z-score, diminished from -6.5 +/- 2.1 to -3.0 +/- 2.1 (P < 0.001). In the control group the BMD z-score worsened significantly. Vertebral coronal area increased in all treated patients (11.4 +/- 3.4 to 14.9 +/- 1.8 cm2; P < 0.001), but decreased in the untreated group (P < 0.05). In the treated patients, fracture rate was lower than in control patients (2.6 +/- 2.5 vs. 6.3 +/- 1.6 fractures/year; P < 0.01). No adverse side-effects were noted, apart from the well known acute phase reaction during the first infusion cycle. Pamidronate treatment in severely affected OI patients under 3 yr of age is safe, increases BMD, and decreases fracture rate.
Imaging the serotonin 1 A receptor using [ 11 C ] WAY 100635 in healthy controls and major depression

As a neurotransmitter, serotonin (5-HT) is widely used throughout the brain and known to play a role in many processes including emotion and brain development. Of the 15 subtypes of 5-HT receptors, the 1A receptor (5-HT1A) has been implicated in depression and suicide. Using the [carbonyl-C]WAY100635 ([C]WAY) ligand and positron emission tomography, we have studied the 5-HT1A receptor, first in a group of healthy controls, then in two separate groups of subjects with major depressive disorder (MDD) (antidepressant exposed and not recently medicated), and, lastly, in a group of subjects remitted from MDD. All MDD subjects were medication-free at the time of scan. We found higher 5-HT1A binding potential (BPF) in MDD subjects not recently exposed to an antidepressant compared with controls and recently medicated MDD subjects; and higher BPF in subjects with the C(-1019)G promoter polymorphism. We replicated these findings in a novel cohort and reconciled our discrepant findings with other groups using alternate quantification techniques. We also reported higher BPF in subjects remitted from a major depressive episode than in controls. From this work, we proposed a temporal model in which 5-HT1A BPF may be a trait abnormality of MDD. To further explore the genetic components of MDD and utility of 5-HT1A imaging as a potential tool for biomarker or treatment response prediction, these findings should be replicated in a larger cohort using the [C]CUMI-101 agonist tracer.
The Effect of Inattentive Behaviours in the Classroom on Students' Progress in Literacy and Numeracy

An enduring concern of teachers, parents and mental health professionals is the extent to which externalizing behaviour problems in the classroom presently called disruptive behavior disorders in DSM-III-R (APA, 1987) (ie., attention deficit/overactivity and conduct disorders), adversely affect students' opportunities for learning and educational development. Students whose behaviours are regarded as inattentive, disruptive or maladjusted have been shown to be at risk of poor educational progress. In addition to the consequences for an individual, such behaviour problems in the classroom diminish educational opportunities for other students and contribute to teacher stress. A further concern is that externalizing behaviour disorders ". are quite refractory to typical interventions and, like severe underachievement, comprise a major psychological, economic and social problem" (Hinshaw, 1992, p. 894). Using illustrative data from the first two stages of a current three-year longitudinal study among 13,000 students (aged 5-16 years) drawn from 90 government, Catholic and independent primary and secondary schools, this paper provides estimates of the effects of externalizing, inattentive behaviours in the classroom on primary students' progress in literacy and numeracy.