Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric genetic clustering: comparison of validity indices

A variable-string-length genetic algorithm (GA) is used for developing a novel nonparametric clustering technique when the number of clusters is not fixed a-priori. Chromosomes in the same population may now have different lengths since they encode different number of clusters. The crossover operator is redefined to tackle the concept of variable string length. A cluster validity index is used as a measure of the fitness of a chromosome. The performance of several cluster validity indices, namely the Davies-Bouldin (1979) index, Dunn's (1973) index, two of its generalized versions and a recently developed index, in appropriately partitioning a data set, are compared.
Analogy Based Episode Blending in the AMBR Model 157 ANALOGY BASED EPISODE BLENDING IN THE AMBR MODEL

This article presents a series of simulation experiments investigating the interaction of memory and analogy-making in the AMBR model. This interaction leads to blending between superficially dissimilar episodes as a result of the established analogical mapping between them and of superficially and structurally dissimilar episodes as a result of a double analogy with a third episode. The conditions for the emergence of such a blending are explored on the basis of a proposed specific analogy-like retrieval mechanism.
An Empirical Study on Performance Comparison of Lucene and Relational Database

Lucene and relational database (RDB) are the two types of the most popular back-end toolkits for Web applications nowadays, which are mainly used to store and query data. However, it is often difficult for users and developers to make a decision to choose Lucene or RDB as their data repository, since they have only a conceptual understanding on difference between them. And the detailed performance comparison between Lucene and RDB is absent as yet. In this paper, we make an empirical study on performance comparison of Lucene and RDB by using experiments on a common test data set. The experimental results show that each of them has advantages and disadvantages in different application scenarios. These results can be used to help users and developers make a more advisable decision when facing different application requirements.
Surface Acoustic Wave Tactile Display using a Large Size Glass Transducer

A method to provide human tactile sensation using surface acoustic wave (SAW) was proposed. A stator transducer material (piezoelectric material) of a SAW tactile display, however, had constraint of size and shape. In this research, to solve this problem, a new method has been proposed to excite and propagate SAW on a non-piezoelectric material surface. Excitation and propagation of SAW on a non-piezoelectric material is realized by a combination of a LiNbO3 plate and a glass substrate. This paper describes design and fabrication of a large sized glass substrate transducer for the novel method to indicate tactile sensation using SAW. The design employs the novel transducer structure, which has been proposed previously. Using the transducer, two-dimensional rubbing motion is available. This paper also reports measurement result of speed of a slider under control. The result indicates successful display of roughness sensation based on the tactile display principle proposed by authors.
Portable Queries Using the Brick Schema for Building Applications: Demo Abstract

Sensorized commercial buildings are a rich target for building a new class of applications that improve operational and energy efficiency of building operations that take into account human activities. Such applications, however, rarely experience widespread adoption due to the lack of a common descriptive schema that would enable porting these applications and systems to different buildings. Our demo presents Brick [4], a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. Using a web application, we will demonstrate real buildings that have been mapped to the Brick schema, and show application queries that extracts relevant metadata from these buildings. The attendees would be able to create example buildings and write their own queries.
Local scaling of time in Hamiltonian path integration

Inspired by the usefulness of local scaling of time in the path integral formalism, we introduce a new kind of hamiltonian path integral in this paper. A special case of this new type of path integral has been earlier found useful in formulating a scheme of hamiltonian path integral quantization in arbitrary coordinates. This scheme has the unique feature that quantization in arbitrary co-ordinates requires hamiltonian path integral to be set up in terms of the classical hamiltonian only, without addition of any adhoc $ O(\hbar ^2) $terms. In this paper we further study the properties of hamiltonian path integrals in arbitrary co-ordinates with and without local scaling of time and obtain the Schrodinger equation implied by the hamiltonian path integrals. As a simple illustrative example of quantization in arbitrary coordinates and of exact path integration we apply the results obtained to the case of Coulomb problem in two dimensions.
Enterprise Mold Distinctiveness via Cloud Through Software-as-a-Service

Cloud computing is a disruptive technology with profound implications not only for Internet services but also for the field of information technology. The cloud uses resources in an optimal manner, which implies sharing of resources across a s et customer. The cloud business model is hosted for a web services on Internet, so that any type of application are interactive and uses centralized data center. As a r esult, vendors across the IT industry have announced cloud computing efforts of varying capabilities. The trend motivated the IT corporate companies there is an increasing interest in aspect of the cloud to incorporate innovative business model key process as service. Focus is made on t he new SaaS software delivery models, enterprise software and services in the cloud computing architecture. Finally, this paper is discussed on tracking and measuring the effective management of business model strategies, characteristics, implementation and pricing software applications. Keyword: Cloud computing, Grid computing, Services, SOA, SaaS, Virtualization.
Fixed-Lag Smoothing for Low-Delay Predictive Coding with Noise Shaping for Lossy Networks

We consider linear predictive coding and noise shaping for coding and transmission of auto-regressive (AR) sources over lossy networks. We generalize an existing framework to arbitrary filter orders and propose use of fixed-lag smoothing at the decoder, in order to further reduce the impact of transmission failures. We show that fixed-lag smoothing up to a certain delay can be obtained without additional computational complexity by exploiting the state-space structure. We prove that the proposed smoothing strategy strictly improves performance under quite general conditions. Finally, we provide simulations on AR sources, and channels with correlated losses, and show that substantial improvements are possible.
β-Glucan antigenemia assay for the diagnosis of invasive fungal infections in patients with hematological malignancies: a systematic review and meta-analysis of cohort studies from the Third European Conference on Infections in Leukemia (ECIL-3).

BACKGROUND Invasive fungal infections (IFIs) are life-threatening complications in patients with hemato-oncological malignancies, and early diagnosis is crucial for outcome. The compound 1,3-β-D-glucan (BG), a cell wall component of most fungal species, can be detected in blood during IFI. Four commercial BG antigenemia assays are available (Fungitell, Fungitec-G, Wako, and Maruha). This meta-analysis from the Third European Conference on Infections in Leukemia (ECIL-3) assessed the performance of BG assays for the diagnosis of IFI in hemato-oncological patients. METHODS Studies reporting the performance of BG antigenemia assays for the diagnosis of IFI (European Organization for Research and Treatment of Cancer and Mycoses Study Group criteria) in hemato-oncological patients were identified. The analysis was focused on high-quality cohort studies with exclusion of case-control studies. Meta-analysis was performed by conventional meta-analytical pooling and bivariate analysis. RESULTS Six cohort studies were included (1771 adult patients with 414 IFIs of which 215 were proven or probable). Similar performance was observed among the different BG assays. For the cutoff recommended by the manufacturer, the diagnostic performance of the BG assay in proven or probable IFI was better with 2 consecutive positive test results (diagnostic odds ratio for 2 consecutive vs one single positive results, 111.8 [95% confidence interval {CI}, 38.6-324.1] vs 16.3 [95% CI, 6.5-40.8], respectively; heterogeneity index for 2 consecutive vs one single positive results, 0% vs 72.6%, respectively). For 2 consecutive tests, sensitivity and specificity were 49.6% (95% CI, 34.0%-65.3%) and 98.9% (95% CI, 97.4%-99.5%), respectively. Estimated positive and negative predictive values for an IFI prevalence of 10% were 83.5% and 94.6%, respectively. CONCLUSIONS Different BG assays have similar accuracy for the diagnosis of IFI in hemato-oncological patients. Two consecutive positive antigenemia assays have very high specificity, positive predictive value, and negative predictive value. Because sensitivity is low, the test needs to be combined with clinical, radiological, and microbiological findings.
Partial Least Squares Based Dimension Reduction with Gene Selection for Tumor Classification

Analyzing gene expression data from DNA microarrays by commonly used classifiers is a hard task, be-cause there are only a few observations but with thousands of measured genes in the data set. Partial least squares based dimension reduction (PLSDR) is superior to handling such high dimensional problem, but irrelevant features will introduce errors into the dimension reduction process and reduce the classification accuracy of learning machines. Here feature selection is applied to filter the data and an algorithm named PLSDRg is described by integrating PLSDR with gene selection, which can effectively improve classification accuracy of learning machines. Feature selection is performed by the indication of t-statistics scores on standardized probes. Experimental results on seven microarray data sets show that the proposed method PLSDRg is effective and reliable to improve the generalization performance of classifiers.
ICASE Report No . 92-51 ADA 257 889 ICASE STATIC ASSIGNMENT OF COMPLEX STOCHASTIC TASKS USING STOCHASTIC MAJORIZATION DTIC °

We consider the problem of statically assigning many tasks to a (smaller) system of homogeneous processors, where a task's structure is modeled as a branching process, and all tasks are assumed to have identical behavior. We show how the theory of majorization can be used to obtain a partial order among possible task assignments. Our results show that if the vector of numbers of tasks assigned to each processor under one mapping is majorized by that of another mapping, then the former mapping is better than the latter with respect to a large number of objective functions. In particular, we show how measurements of finishing time, resource utilization, and reliability are all captured by the theory. We also show how the theory may be applied to the problem of partitioning a pool of processors for distribution among parallelizable tasks. 'This research was supported by the National Aeronautics and Space Administration under NASA Contract Nos. NASI-18605 and NASI-19480 while the first author was in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA 23681-0001. Research also supported in part by NSF ASC 8819393. 2 Research supported by NSF NCR-8907909.
NEAREST NEIGHBOUR STRATEGIES FOR IMAGE UNDERSTANDING

Nearest Neighbour algorithms for pattern recognition have been widely studied. It is now well-established that they offer a quick and reliable method of data classification. In this paper we further develop the basic definition of the standard k-nearest neighbour algorithm to include the ability to resolve conflicts when the highest number of nearest neighbours are found for more than one training class (kNN model). We also propose aNN model of nearest neighbour algorithm that is based on finding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of five natural classes: grass, trees, sky, river reflecting sky and river reflecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies when compared with neural networks. 1 © British Crown Copyright 1999/DERA Published with the permission of the controller of Britannic Majesty's Stationary Office; S. Singh, J.F. Haddon and M. Markou. Nearest Neighbour Strategies for Image Understanding, Proc. Workshop on Advanced Concepts for Intelligent Vision, Systems (ACIVS'99), Baden-Baden, (2-7 August, 1999).
The Combined Application of the Caco-2 Cell Bioassay Coupled with In Vivo (Gallus gallus) Feeding Trial Represents an Effective Approach to Predicting Fe Bioavailability in Humans

Research methods that predict Fe bioavailability for humans can be extremely useful in evaluating food fortification strategies, developing Fe‐biofortified enhanced staple food crops and assessing the Fe bioavailability of meal plans that include such crops. In this presentation, research from four recent poultry (Gallus gallus) feeding trials coupled with in vitro analyses of Fe‐biofortified crops will be compared to the parallel human efficacy studies which used the same varieties and harvests of the Fe‐biofortified crops. Similar to the human studies, these trials were aimed to assess the potential effects of regular consumption of these enhanced staple crops on maintenance or improvement of iron status. The results demonstrate a strong agreement between the in vitro/in vivo screening approach and the parallel human studies. These observations therefore indicate that the in vitro/Caco‐2 cell and Gallus gallus models can be integral tools to develop varieties of staple food crops and predict their effect on iron status in humans. The cost‐effectiveness of this approach also means that it can be used to monitor the nutritional stability of the Fe‐biofortified crop once a variety has released and integrated into the food system. These screening tools therefore represent a significant advancement to the field for crop development and can be applied to ensure the sustainability of the biofortification approach.
Specification of control goals for mechanical systems as function minimization problem

A new relation between Lyapunov theory and control of mechanical models is presented. A goal function is constructed on the base of a mechanical system geometry independently of a control model. Control tasks are then related to a function minimization problem over the system trajectory. It is shown that such minimization problems are strictly related to the Lyapunov condition.
Parallel Inductive Logic for Data

Data mining is the process of automatic extraction of novel, useful and understandable patterns in very large databases. High-performance, scalable, and parallel computing algorithms are crucial in data mining as datasets grow in size and complexity. Inductive logic is a research area in the intersection of machine learning and logic programming, which has been recently applied to data mining. Inductive logic studies learning from examples, within the framework provided by clausal logic. It provides a uniform and expressive means of representation: examples, background knowledge, and induced theories are all expressed in rst-order logic. Such an expressive representation is computationally expensive, so it is natural to consider improving the performance of inductive logic data mining using parallelism. We present a parallelization technique for inductive logic, and implement a parallel version of a core inductive logic programming system, Progol. Performance results on several datasets and platforms are reported.
Predicting and Manipulating Cardiac Drug Inactivation by the Human Gut Bacterium Eggerthella lenta

Digoxin Dangers A proportion of patients treated with digoxin, a cardiac glycoside used to treat heart function abnormalities, generate the inactive metabolite, dihydrodigoxin, resulting in poor efficacy. Haiser et al. (p. 295) examined a potential culprit responsible for this transformation—the actinobacterium, Eggerthella lenta—to probe the microbiota-digoxin interaction. Microbe growth was promoted by arginine, and differential expression analysis revealed a two-gene cardiac glycoside reductase (cgr) operon that was induced by digoxin in low arginine conditions. Not all strains of E. lenta could reduce digoxin and, when fecal samples from healthy people were tested, a spectrum of digoxin inactivation was detected. When the digoxin-reducing strain of E. lenta was given to germ-free mice that were fed a high-protein (that is, high-arginine) diet, digoxin levels stayed high in serum, and drug inactivation was suppressed. The heart drug digoxin can be inactivated by a strain of gut microbe bearing a cardiac glycoside reductase operon. Despite numerous examples of the effects of the human gastrointestinal microbiome on drug efficacy and toxicity, there is often an incomplete understanding of the underlying mechanisms. Here, we dissect the inactivation of the cardiac drug digoxin by the gut Actinobacterium Eggerthella lenta. Transcriptional profiling, comparative genomics, and culture-based assays revealed a cytochrome-encoding operon up-regulated by digoxin, inhibited by arginine, absent in nonmetabolizing E. lenta strains, and predictive of digoxin inactivation by the human gut microbiome. Pharmacokinetic studies using gnotobiotic mice revealed that dietary protein reduces the in vivo microbial metabolism of digoxin, with significant changes to drug concentration in the serum and urine. These results emphasize the importance of viewing pharmacology from the perspective of both our human and microbial genomes.
A Mass Tracking Formulation for Bubbles in Incompressible Flow

Abstract : We devise a novel method for treating bubbles in incompressible flow that relies on the conservative advection of bubble mass and an associated equation of state in order to determine pressure boundary conditions inside each bubble. We show that executing this algorithm in a traditional manner leads to stability issues similar to those seen for partitioned methods for solid-fluid coupling. Therefore, we reformulate the problem monolithically. This is accomplished by first proposing a new fully monolithic approach to coupling incompressible flow to fully nonlinear compressible flow including the effects of shocks and rarefactions, and then subsequently making a number of simplifying assumptions on the air flow removing not only the nonlinearities but also the spatial variations of both the density and the pressure. The resulting algorithm is quite robust, has been shown to converge to known solutions for test problems, and has been shown to be quite effective on more realistic problems including those with multiple bubbles, merging and pinching, etc. Notably, this approach departs from a standard two-phase incompressible flow model where the air flow preserves its volume despite potentially large forces and pressure differentials in the surrounding incompressible fluid that should change its volume. Our bubbles readily change volume according to an isothermal equation of state.
Application of modified ant colony optimization for computer aided bleeding detection system

Wireless capsule endoscopy (WCE) plays a significant role in the non-invasive small intestine screening for obscure gastrointestinal bleeding detection. However, the task of reviewing 60,000 frames to detect the bleeding encumbers the clinician, leading to visual fatigue and false diagnosis. In this paper, we propose a color feature based bleeding detection system with feature selection using a modified ant colony optimization (MACO) algorithm. We have utilized the feature selection capability of MACO algorithm to find the optimum feature subset over the color space of RGB and HSV, which provided a classifier that outperforms the classifier formed from RGB and HSV features individually. Comprehensive experimental results reveal that the proposed MACO algorithm can detect the optimal feature subset with performance comparable to exhaustive search in case of individual classifier from RGB and HSV requiring 2% of the computational time compared to exhaustive search. The comparative study of feature selection showed that MACO can provide the most relevant features and improve the performance in terms of accuracy, sensitivity and computational time.
Classification of video genre using audio

In this paper we propose an approach to high-level clas-siﬁcation of video into genre: sport, cartoon, news, commercial and music. An important issue for automatic high-level classiﬁ-cation systems is the amount of time needed to classify a video. Here we investigate classiﬁcation performance as a function of the test sequence length. In addition we present performance against different orders and combinations of static and dynamic mel-frequency cepstral co-efﬁcients (MFCC). We ﬁnd that static and delta MFCCs perform well for this classiﬁcation task. A test sequence length of approximately 25 seconds for the 5 class problem gives approximately 80% correct identiﬁcation.
The Path Forward: Specialized Computing in the Datacenter

Popular belief holds that the cores on chip will grow at an exponential rate, following Moore’s Law, with a commensurate increase in performance. However, by exploring the design space of multicore chips across technologies under a large array of design parameters, we observe that physical constraints in power and off-chip bandwidth prohibit such performance increase. This leads us to conclude that server chips will not scale beyond a few tens of cores, potentially leaving the die real-estate underutilized in future technology generations. We observe that heterogeneous multicores can leverage the die area to overcome the initial power barrier, delivering significantly higher performance for the same offchip bandwidth and power envelopes. Thus, specialized computing, especially when coupled with emerging memory technologies, promises significant increases in performance and energy-efficiency compared to general-purpose computing in the datacenter.
New Intelligent Power Modules ( IPMs ) for Motor Drive Applications

Two new families of intelligent power devices have been developed to address industry requirements for high reliability, low cost, motor controls with increased performance and smaller size. These new devices are the latest product of the continuos optimization of advanced technology that has characterized the evolution of Intelligent Power Modules (IPM's). This article will explain the design philosophy and internal architecture of these new devices.