Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Lexicographic Names Construction with CUDA

Suffix array is a simpler and compact alternative to the suffix tree, lexicographic name construction is the fundamental building block in suffix array construction process. This paper depicts the design issues of first data parallel implementation of the lexicographic name construction algorithm on a commodity multiprocessor GPU using the Compute Unified Device Architecture (CUDA) platform, both from NVIDIA Corporation. The full parallel version runs much faster than any serial implementation on CPU. The thread level parallel code block provides an efficient primitive for building a high performance suffix array construction program and many other applications.
Accelerating Network Coding on Many-core GPUs and Multi-core CPUs

Network coding has recently been widely applied in various distributed systems for throughput improvement and/or resilience to network dynamics. However, the computational overhead introduced by network coding operations is not negligible and has become the obstacle for practical deployment of network coding. In this paper, we exploit the computing power of commodity many-core Graphic Processing Units (GPUs) and multi-core CPUs to accelerate the network coding operations. We propose a set of parallel algorithms that maximize the parallelism of the encoding and decoding processes and fully utilize the power of GPUs. This paper also shares our optimization design choices and our workarounds to the challenges encountered in working with GPUs. With our implementation of the algorithms, we are able to achieve significant speedup over existing solutions on CPUs.
Lossless compression of NOAA-AVHRR satellite data

A high-performance lossless compression system for satellite NOAA data is developed. The data is called 'high resolution picture transmission' (HRPT) data, and consists of around 93 percent advanced very high resolution radiometer (AVHRR) multi-channel image data and 7 percent of miscellaneous data. In compressing the image portion, we classify each pixel into 10 different groups and apply a multi-channel prediction and a non-linear error conversion. The entropy coder is an arithmetic coder which is adaptive and regenerates the approximation of the statistical properties of the source as an initial probability table. To compress the non-image part, we used the general compressor (gzip). From experimental results, the original information is compressed down to 25 percent to approx. 40 percent.
Some Remarks on the -stability for Families of Polynomials

Using Brouwer degree, we prove a more general version of the zero exclusion principle for families of polynomials and apply it to obtain very simple proofs of extensions of recent results on the Routh-Hurwitz and Schur-Cohn stability of families of polynomials. 1. Introduction It is well know that the problem of the stability of a linear diierence or differential system with constant coeecients reduces to the question of locating the roots of its characteristic polynomial in a suitable region of the complex plane. The Routh-Hurwitz and Schur-Cohn tests, which respectively correspond to the open left half-space and the open unit ball, are well known in this respect 2, 3]. In a recent work 9], Zahreddine has considered the following problem: given a path-wise connected region in the complex plane and a set S of polynomials of the same degree, nd conditions under which all polynomials of S have their zeros inside : In the special case where S is made of all the convex combinations of two polynomials which are stable in the Routh-Hurwitz or the Schur-Cohn sense, Zahreddine has found necessary and suucient conditions for this set S to have the same stability. His approach is algebraic and based upon some properties of resultants and standard Routh-Hurwitz or Schur-Cohn stability conditions for a complex polynomial 2, 3]. The aim of this note is to show that a very simple proof of more general version of this result can be obtained by using the elementary properties of the Brouwer degree 4]. We rst use this technique to prove a more general version of the standard zero exclusion principle 1, 6] and then apply it to the proof of the Zahreddine's results.
Pilot Study on Clinical Effectiveness of Autofluorescence Imaging for Early Gastric Cancer Diagnosis by Less Experienced Endoscopists

This study aimed to assess and compare effectiveness of Autofluorescence imaging (AFI) in diagnosis of early gastric cancer (EGC) between experienced and less experienced endoscopists. Fifty selected images (20 neoplastic lesions and 30 benign lesions/areas) of both white light endoscopy (WLE) and AFI were blindly reviewed by two groups; first consisted of five experienced endoscopists and second included five less experienced endoscopists. Sensitivity, specificity, and accuracy were 70%, 78%, and 75%, respectively, for AFI and 81%, 76%, and 78%, respectively, for WLE in the experienced group. In the less experienced group, sensitivity, specificity and accuracy were 80%, 81% and 80%, respectively, for AFI and 65%, 77%, and 72%, respectively, for WLE. Interobserver variability for the less experienced group was better with AFI than WLE. AFI improved sensitivity of endoscopic diagnosis of neoplastic lesions by less experienced endoscopists, and its use could beneficially enhance the clinical effectiveness of EGC screening.
Exploration of Participation in Student Software Engineering Teams

Employers require software engineers to work in teams when developing software systems. It is therefore important for graduates to have experienced teamwork before they enter the job market. We describe an experiential learning exercise that we designed to teach the software engineering process in conjunction with teamwork skills. The underlying teaching strategy applied in the exercise maximises risks in order to provide maximal experiential learning opportunities. The students are expected to work in fairly large, yet short-lived, instructor-assigned teams to complete software engineering tasks. After undergoing the exercise our students form self-selected teams for their capstone projects. In this article, we determine and report on the influence the teaching exercise had on the formation of teams for the capstone project. By analysing data provided by the students through regular peer reviews we gain insight into the team dynamics as well as to what extent the members contributed to the team effort. We develop and present a graphical model of a capstone project team which highlights participation of individuals during the teaching exercise. The participatory history of the members is visualised using segmented concentric rings. We consider how this visualisation can aid the identification of capstone project teams that are at risk. In our experience the composition of the team and the behaviour of other members in the team may have a marked impact on the behaviour of each individual in the team. We established a team classification in order to model information about teams. We use a statistical clustering method to classify teams. For this we use team profiles that are based on the participatory levels of its members. The team types that emerge from the clustering are used to derive migration models. When we consider migration, we build spring models to visualise the teams through which individuals migrate. We colour code the teams to characterise them according to the team types that were identified during the cluster classification of the teams. Owing to the complexity of the resulting model, only migrations for capstone team members who have worked together during the exercise or for solitary capstone team members are modelled. These models support the identification of areas of interest that warrant further investigation. To conclude, we present our observations from the analysis of team compositions, team types, and team migrations and provide directions for future work and collaborations.
Semi-Supervised Regression with Co-Training

In many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. Previous research mainly focuses on semi-supervised classification. In this paper, a co-training style semi-supervised regression algorithm, i.e. COREG, is proposed. This algorithm uses two k-nearest neighbor regressors with different distance metrics, each of which labels the unlabeled data for the other regressor where the labeling confidence is estimated through consulting the influence of the labeling of unlabeled examples on the labeled ones. Experiments show that COREG can effectively exploit unlabeled data to improve regression estimates.
Learning Term Dependency Links Using Information Theoretic Inclusion Measure

An algorithm to identify and remove term redundancy is proposed for text classifiers using ranking-based feature selection. The proposed method employs a normalized mu- tual information, which is called inclusion measure, to es- timate asymmetric dependency between two terms. Based on pair-wise dependency measures, a dependency matrix is constructed. In this paper, an algorithm is proposed to learn term dependency links from term dependency matrix, and visualize the dependency between term in a graph called term dependency tree. All nodes of the tree are categorized into two groups: hubs and links. Any node whose outde- gree is less than two will join the Links group. We show that all link nodes are most likely redundant. We also in- troduce a criterion, which is called substitution cost, to de- cide whether to remove or retain a candidate, redundant term. The proposed approach is applied to four well-known benchmark data sets with a SVM and Rocchio classifier us- ing a set of highly aggressive feature selection schemes. The results show the effectiveness of the proposed method espe- cially when applied to weak classifiers.
Distribution of Cystine/Glutamate Exchange Transporter, System xc−, in the Mouse Brain

Mammalian cells express a transport system known as system xc−, which is an exchange agency specific for anionic forms of cystine and glutamate. System xc− activity is important to maintain both intracellular glutathione levels and the redox balance between cystine and cysteine in the extracellular milieu. We have shown that the cloned cDNAs encoding the transporter for system xc− consist of two components, xCT and the heavy chain of 4F2 antigen. In the present study, we have investigated the mRNA distribution for these components in the mouse brain by in situ hybridization. The xCT mRNA was expressed in the area postrema, subfornical organ, habenular nucleus, hypothalamic area, and ependymal cells of the lateral wall of the third ventricle in the adult mouse brain. A strong signal was also detected in the meninges in both adult and fetal mouse brains. The mRNA expression of the heavy chain of 4F2 antigen was detected in a more broad area, including all of the regions in which xCT mRNA was detected. These data are compatible with our biochemical evidence that xCT functions in combination with the heavy chain of 4F2 antigen to elicit system xc− activity. The expression of system xc− in meninges and some circumventricular organs may suggest that this transporter contributes to the maintenance of the redox state (i.e., cysteine/cystine ratio) in the CSF.
Visualization of higher order singularities in vector fields

Presents an algorithm for the visualization of vector field topology based on Clifford algebra. It allows the detection of higher-order singularities. This is accomplished by first analysing the possible critical points and then choosing a suitable polynomial approximation, because conventional methods based on piecewise linear or bilinear approximation do not allow higher-order critical points and destroy the topology in such cases. The algorithm is still very fast, because of using linear approximation outside the areas with several critical points.
Hand Printed Character Recognition Using Neural Networks

In this paper an attempt is made to recognize hand-printed characters by using features extracted using the proposed sector approach. In this approach, the normalized and thinned character image is divided into sectors with each sector covering a fixed angle. The features totaling 32 include vector distances, angles, occupancy and end-points. For recognition, both neural networks and fuzzy logic techniques are adopted. The proposed approach is implemented and tested on hand-printed isolated character database consisting of English characters, digits and some of the keyboard special characters.
Implicit Theories About Willpower in Resisting Temptations and Emotion Control

Previous research suggests that people’s implicit theories about willpower affect continuous self-control performance in the domain of strenuous mental activities. The present research expands these findings to two further domains of self-control: resisting temptations and emotion control. In Study 1, participants were either led to resist a temptation or not. Participants who believed that willpower gets depleted by resistance to temptations (limited-resource theory) performed significantly worse in a subsequent Stroop task compared to participants who believed that resisting temptations activates their willpower (nonlimited-resource theory). In Study 2, participants controlled their emotions during a funny video or were allowed to express them. Participants who believed that controlling emotions depletes willpower performed worse in a subsequent persistence task than those who believed that controlling emotions activates willpower. Results suggest that implicit theories about willpower are domain specific and sensitive to the domain of the initial self-control task rather than that of the subsequent self-control task.
AC actuation of fixed-fixed beam MEMS switches

In this paper, an actuation scheme of RF microelectromechanical systems (MEMS) switches based on a combined DC and AC actuation voltage (AC actuation) is proposed and numerically simulated. Compared to the conventional electrostatic actuation, AC actuation requires a much smaller actuation voltage, which is more suitable for low-voltage application, including wireless phones and sensors. The frequency of the AC component (actuation frequency) is very close to the mechanical resonant frequency of the MEMS structure beam. A parametric study on the effects of switch geometry, residual stress, and quality factor on the switch actuation voltage and switching time is performed in this work and specific design guidelines are provided for practical designs
Social Networking Sites: A New Platform for Botnets A Short Case Study to prove that“How Today's Social Networking is a New Platform for Cyber Criminals”

--With increasing global reliance on the Internet as a medium to transmit information comes an increased risk of cyber-attack. Social networking sites are the most popular tools in internet for social interaction between the people. Online social networks (OSNs) have rapidly become a prominent and widely used service, offering a wealth of personal and sensitive information with significant security and privacy implications. They provide a convenient and accessible platform for people who want to exchange thoughts or words via the internet. Since friends on social networking sites have confirmed one another and trust one another, these people will be more unaware of the potential dangers that can come from messages/e-mails/links. When such dangers are neglected by people, internet security related cases will undoubtedly rise. As the technology is growing, hackers are also using the latest technology for malicious purposes i.e. Technology changed from traditional virus to Botnet and Root kits. Very recently, Botmaster have begun to exploit Social network websites (such as facebook.com; twitter.com etc.) as their C&C (command and control) infrastructures, which turns out to be quite stealthy because it is hard to distinguish the C&C activities from the normal Social networking traffic. Recently Fast-flux network (FFN) based Botnets (Social Bot) hacked four lacsfacebook accounts alone in Bangalore in November 2011 and 60 lacslinkedIn password in June2012. So this paper performs an empirical analysis of the cyber criminals (Botnet) on OSNs and shows that how today social networking sites (SNS) has been situated a new platform for Botnet to perform an illegal activity; to steal the valuable data for illicit financial gain behalf of user. So this paper explains the existence of various Botnet crimes on OSNs. Essentially, through analyzing social relationships affected by Bot, this paper present some mitigation; defense approach and prevent approach from Bot attack.
K-hop statistics in wireless sensor networks

In WSNs (Wireless Sensor Networks), a source node sends its packets via multi-hop relays to a destination node if the source node cannot communicate with the destination node directly. A destination's hop-count with respect to a source is the least number of multi-hop relays required for the source to reach it. Given a WSN whose nodes are distributed randomly according to a homogeneous Poisson point process, we propose a method termed CSP (Convolution of Successive Progress) to compute the conditional probability that a destination node has hop-count h with respect to a source node given that the the distance between the source and the destination is d. We also extend the CSP method into three-dimensional networks. Simulation studies show that the proposed method is able to achieve significant error reduction in computing the conditional probability compared with existing methods.
Image-Language Association: are we looking at the right features?

The ever growing popularity and availability of multimedia information has rendered automatic image-language association essential in a number of multimedia integration applications. Bridging the gap between the two media requires an appropriate feature-set for describing their common reference; one that will be both distinctive of the entities referred too and feasible to extract automatically from visual media. In this paper, we suggest an alternative –to current approaches- feature set, which has been used in OntoVis, a domain model for a prototype that describes three-dimensional (3D) indoor scenes. We argue that it is worth employing this feature-set in a larger scale for image-language association and investigating the feasibility of doing so and of detecting such features automatically even beyond 3D visual data, in 2D images.
Setting Monetary Policy in East Asia: Goals, Developments and Institutions

This paper presents a comprehensive and analytical review of monetary policy-making in the East Asian economies since the 1997 financial crisis. Focusing on the three key issues of objectives, instruments and institutional developments, it surveys the recent choice of monetary policy objectives in East Asia. The paper observes that while most central banks in East Asia are striving for price stability, the openness of their economies compels them to focus on exchange rate. Their strategies, however, vary from using effective exchange rate as an operating target, to adoption of inflation targeting and fixed exchange rate with selective capital controls. On central bank independence, the paper concludes that there is very little support for several propositions linking central bank independence with inflation; and that more attention should be given to the balance-sheet independence of central banks.
Biometrics Authentication System on Open Network and Security Analysis

Biometrics-based authentication systems are being increasingly used to control physical access to high-security facilities. In the case of biometrics authentication through an open network, there are many vulnerabilities which need to be addressed. After analyzing the possible vulnerabilities of a biometrics-based network authentication system, this paper presents a biometrics-based network authentication model combined with public key encryption technology to guarantee the authenticity of biometric data at transmission. The proposed model can provide secure authentication service for network-based application only if it follows the authentication protocol of the model.
Some Donaldson invariants of CP^2

For an integer n ≥ 2, let q4n−3 be the coefficient of the Donaldson polynomial of degree 4n − 3 of P = CP. An interpretation of q4n−3 in an algebro-geometric context is the following. Let Mn denote the Gieseker-Maruyama moduli space of semistable coherent sheaves on P with rank 2 and Chern classes c1 = 0 and c2 = n. For such a sheaf F , the Grauert-Mülich theorem implies that the restriction of F to a general line L ⊆ P splits as FL ≃ OL ⊕ OL, and that the exceptional lines form a curve J(F ) of degree n in the dual projective plane P . The association F 7→ J(F ) is induced from a morphism of algebraic varieties, called the Barth map, fn : Mn → Pn. Here Pn = P n(n+3)/2 is the linear system parameterizing all curves of degree n in P . Let H ∈ Pic(Pn) be the hyperplane class and let α = f ∗ nH . The interpretation of the Donaldson invariant is:
Discrete policy changes and empirical models of the federal funds rate

Empirical models of the federal funds rate almost uniformly use the quarterly or monthly average of the daily rates. One empirical question about the federal funds rate concerns the extent to which monetary policymakers smooth this interest rate. Under the hypothesis of rate smoothing, policymakers set the interest rate this period equal to a weighted average of the rate inherited from the previous quarter and the rate implied by current economic conditions, such as the Taylor rule rate. Perhaps surprisingly, however, little attention has been given to measuring the interest rate inherited from the previous quarter. Previous tests for interest rate smoothing have assumed that the quarterly or monthly average from the previous period is the inherited rate. The authors of this study, in contrast, suggest that the end-of-quarter level of the target federal funds rate is the inherited rate, and empirical tests support this proposition. The authors show that this alternative view of the rate inherited from the past affects empirical results concerning interest rate smoothing, even in relatively rich models that include regime switching.
Prediction of Specific 70-mer Oligonucleotide with Neural Network Model

Motivation: Mammalian genomes are highly complex. To identify the specific 70-mer oligonucleotides of each gene in a mammalian gene database is a computation intensive task. With the advent of parallel genetic analysis methods such as microarrays and the availability of more and more whole genome sequences of organisms, an algorithm allowing speedy prediction of the specific oligonucleotides with a neural network will be a very useful tool. Result: We present a computational model for specific 70-mer oligonucleotides prediction based on a Multi-Layer Backpropagation neural network using a broad range of input parameters. The algorithm was applied to the assemblies of gene sequences and was highly efficient for the TIGR Rat and S.cerevisiae TC database. The confidence can achieve 94.47% with coverage 22.52% in RGI test performance and it can achieve 96.00% with coverage 79.04% in SCGI test performance. Even the confidence of cross-species neural network can achieve 95.67% with coverage 93.49%. On the other hand, the trained neural network process 1,000 70-mert oligos that just costs 1.74 seconds. Contact: PC Chang <pcchang@thmu.edu.tw> JJW Chen <jwchen@dragon.nchu. edu.tw>