Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of software agents as proxies

Today information can be accessed from the Internet using a variety of devices and via different types of networks. With such diversity, it is impossible for a server on the Internet to contain information for all different types of clients. A possible solution to this problem is to use proxies to alter the content and to provide network enhancements to suit client's individual requirements. The current proxy-based solutions rely on static implementations which have several disadvantages. We believe that it will be possible to overcome these disadvantages by making the proxies transportable and active, i.e. the use of proxy agents. In this paper we present the architecture of such a proxy agent system, and the implementation of a prototype to evaluate its viability.
Towards Automated Structure-based NMR Assignment

We introduce the “Automated Structure-based Assignment” problem: Given a reference 3D structure, a protein sequence, and its NMR spectra, automatically interpret the NMR spectra and do backbone resonance assignment. We then propose a solution to solve this problem. The core of the solution is a novel integer linear programming model, which is a general framework for many versions of the structure-based assignment problem. As a proof of concept, our system has generated an automatic assignment on a real protein TM1112 with 91% recall and 99% precision, starting from scratch. When we restrict ourselves to the special case where perfect peak lists are given, we are able to compare our results with existing results in the field. In particular, we reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg’s method by 5 folds on average, with over a thousand fold speed up. Our system also achieves 91% assignment accuracy on real experimental data for Ubiquitin. These results have direct practical implications. For example, in the protein design process, a protein is modified slightly and its structure is again measured by NMR experiments. Our method automates this process, saving time on tedious peak-picking and resonance assignment. As another example, when there is a homologous protein with known structure, our method increases the assignment accuracy and hence enables automated NMR structure determination. The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. All correspondence should be addressed to mli@uwaterloo.ca.
An Indexing Structure for Mobile Objects Utilizing Late Update

The recent technological advances in location aware, mobile and wireless devices have facilitated tracking and monitoring of moving objects. However, these capabilities require storage structures that allow the speedy access and fast retrieval of information for these objects. Therefore, there is increasing interest in storage and indexing structures based on a variety of R-trees. These tree structures use the concept of Minimum Bounding Rectangle (MBR) to represent regions that the nodes of these trees cover, in which the parent nodes may represent MBRs with larger sizes to hold the MBRs of the child nodes. This form of presentation not only is an effective strategy to retrieve objects in an arbitrary arrangement, but also it provides opportunity to delay the update operation for the location of moving objects. In this paper therefore, we propose a spatial and temporal indexing structure which uses delay update strategy for updating the location of moving objects.
On synopses for distinct-value estimation under multiset operations

The task of estimating the number of distinct values (DVs) in a large dataset arises in a wide variety of settings in computer science and elsewhere. We provide DV estimation techniques that are designed for use within a flexible and scalable "synopsis warehouse" architecture. In this setting, incoming data is split into partitions and a synopsis is created for each partition; each synopsis can then be used to quickly estimate the number of DVs in its corresponding partition. By combining and extending a number of results in the literature, we obtain both appropriate synopses and novel DV estimators to use in conjunction with these synopses. Our synopses can be created in parallel, and can then be easily combined to yield synopses and DV estimates for arbitrary unions, intersections or differences of partitions. Our synopses can also handle deletions of individual partition elements. We use the theory of order statistics to show that our DV estimators are unbiased, and to establish moment formulas and sharp error bounds. Based on a novel limit theorem, we can exploit results due to Cohen in order to select synopsis sizes when initially designing the warehouse. Experiments and theory indicate that our synopses and estimators lead to lower computational costs and more accurate DV estimates than previous approaches.
PREDICTIVE ANALYSIS OF WIND TURBINE FAULTS: A DATA MINING APPROACH

Wind industry is expanding rapidly to meet the current energy challenges. The expansion in quantity and size of the wind turbines will also increase the operation and maintenance (O&M) cost. Monitoring the performance of wind turbines can reduce the O&M cost. Supervisory Control and Data Acquisition (SCADA) system records various wind turbine parameters that can be analyzed for performance monitoring. Data mining provides an easy yet robust approach to performance monitoring by analyzing historical data. In the research reported in this paper, data mining techniques are investigated to monitor faults related wind turbines generators and blades. Data mining algorithms namely boosting tree (BT), support vector machine (SVM), k-nearest neighbor (k-NN), and Genetic programming (GP) are employed to build prediction models.
A feedforward controller with neural-network based rate-dependent model for piezoelectric-driven mechanism

Piezoelectric actuator is considered as the main device for the high precision positioning system due to its rapid response, high stiffness and ultra-high resolution. However, the intrinsic hysteresis behavior of piezoelectric actuator can seriously degrade the trajectory-tracking precision. Moreover, the model weights of the Prandtl-Ishlinskii (PI)'s play operator versus velocity are nonlinear which is hard to model when the bandwidth is relatively wide if the rate-dependent model is utilized to address the hysteresis. This will become more serious if the trajectory is non-periodic. Therefore, a neural-network (NN) based rate-dependent PI model is utilized to address the nonlinearity between model weights and corresponding rates since NN can be trained to learn the phenomena without any requirement of complex and difficult mathematical analysis. This method is verified by experiments and it is shown that this approach can effectively address the hysteresis nonlinearity under non-periodic input when the bandwidth is between 0–70 Hz.
Real-time semi-global matching using segmentation and plane fitting for improved accuracy on the GPU

The suitability of stereo algorithms for intelligent vehicle applications is conditioned by their ability to compute dense, accurate disparity maps in real time. In this paper a realtime stereo reconstruction system designed for automotive applications is presented. The system is based on the Semi-Global Matching algorithm, widely known for its high quality and potential for real-time implementation. The accuracy of this algorithm is increased by a new segmentation and plane fitting based approach which is used for refining the estimated disparities. The main contribution is scalable solution for obtaining a significant enhancement of the matching quality in textureless or otherwise difficult areas that also maintains the algorithm's real-time processing capabilities. In this sense, a pixel-centered approach is proposed and compared with two existing plane fitting strategies. The different approaches are also analyzed from the perspective of matching quality and parallelization cost on GPU architectures. Results on the Middlebury benchmark and real-world images confirm the strength of the new method. The complete stereo reconstruction system was implemented and evaluated on a current generation GPU.
HOMEOSTASIS OF ANTIBODY FORMATION IN THE ADULT RAT

Passive immunization of rats with homologous anti-sheep erythrocyte serum markedly inhibited the primary antibody response to various doses of sheep erythrocytes. Inhibition was "specific" and apparently produced by either "19S" or "7S" antibody to the antigen. Passive immunization inhibited splenic hyperplasia associated with the primary antibody response. Passive immunization 24 hours after active immunization effectively inhibited the primary antibody response. The markedly suppressive effect of specific antibody on the primary antibody response contrasted sharply with the absence of this effect on the secondary response. Antigen-antibody complexes formed in vitro elicited no measurable primary antibody response but did elicit a high secondary response. Exposure of normal spleen cells to the antibody in vivo or in vitro suppressed their response to the antigen in x-irradiated recipients. In contrast, cells from previously immunized animals transferred to x-irradiated animals produced antibody in the presence of passively given antibody. Thus, "potential antibody-forming cells" from normal animals were unresponsive to the antigen in the presence of specific antibody, while "antibody-forming cells" from previously immunized animals responded to the antigen in the presence of antibody. Presumably, antibody actively produced in small quantities by a few antibody-forming cells might inhibit antibody formation by potential antibody-forming cells. Confirmation of this suggestion was obtained by showing that some animals initially injected with small doses of antigen failed to produce measurable antibody to subsequent injections of larger doses of the antigen. Low doses of antigen capable of inducing unresponsiveness produced no measurable circulating antibody, but these doses did produce increased numbers of plaque-forming (antibody-releasing) cells in spleens of rats. Thus, the formation of specific antibody may provide a homeostatic or "feed-back" mechanism which controls or limits production of specific antibody to the portion of the antibody-forming system previously stimulated by the antigen. This mechanism may account in part for immunological unresponsiveness produced in certain other related experimental systems.
Discovering Phone Patterns in Spoken Utterances by Non-Negative Matrix Factorization

We present a technique to automatically discover the (word-sized) phone patterns that are present in speech utterances. These patterns are learnt from a set of phone lattices generated from the utterances. Just like children acquiring language, our system does not have prior information on what the meaningful patterns are. By applying the non-negative matrix factorization algorithm to a fixed-length high-dimensional vector representation of the speech utterances, a decomposition in terms of additive units is obtained. We illustrate that these units correspond to words in case of a small vocabulary task. Our result also raises questions about whether explicit segmentation and clustering are needed in an unsupervised learning context.
Improving music genre classification using collaborative tagging data

As a fundamental and critical component of music information retrieval (MIR) systems, music genre classification has attracted considerable research attention. Automatically classifying music by genre is, however, a challenging problem due to the fact that music is an evolving art. While most of the existing work categorizes music using features extracted from music audio signals, in this paper, we propose to exploit the semantic information embedded in tags supplied by users of social networking websites. Particularly, we consider the tag information by creating a graph of tracks so that tracks are neighbors if they are similar in terms of their associated tags. Two classification methods based on the track graph are developed. The first one employs a classification scheme which simultaneously considers the audio content and neighborhood of tracks. In contrast, the second one is a two-level classifier which initializes genre label for unknown tracks using their audio content, and then iteratively updates the genres considering the influence from their neighbors. A set of optimizing strategies are designed for the purpose of further enhancing the quality of the two-level classifier. Extensive experiments are conducted on real-world data collected from Last.fm. Promising experimental results demonstrate the benefit of using tags for accurate music genre classification.
Diagnóstico do Glaucoma em Imagens de Retinografia usando Variantes de Padrões Locais Binários

O glaucoma é uma doença óptica que degrada o nervo óptico até a perda total do campo visual. Os sintomas só aparecem quando a doença está em estágio avançado e é irreversível, sendo assim é necessário o diagnóstico precoce de tal patologia. O principal objetivo deste trabalho é apresentar um método computacional utilizando descritores de textura para detectar o glaucoma automaticamente em retinografias (imagem de fundo de olho). Para descrever a textura foram utilizados os descritores LBP, LQP, CS-LBP e CLBP. Para a classificação das imagens foi utilizada a SVM. O método proposto está organizado em quatro etapas: (1) pré-processamento, (2) decomposição espacial, (3) extração de características e (4) reconhecimento de padrões. O método apresentou resultados promissores de 90,70% de acurácia.
Prognostic Significance of c-kit Mutation in Localized Gastrointestinal Stromal Tumors

Purpose: Constitutive mutational activation of c-kit has been found to be associated with the pathogenesis of gastrointestinal stromal tumors (GISTs). The prognostic significance of c-kit mutations, however, is still controversial. Experimental Design: We examined 86 patients curatively resected for localized GIST. Genomic DNA was extracted from paraffin-embedded tumor tissues. Exons 9, 11, 13, and 17 of the c-kit gene were amplified by PCR and sequenced. Results: Mutations in exon 11 were detected in 61 tumors, and mutations in exon 9 were observed in three tumors, whereas no mutations were detected in exons 13 or 17. The overall c-kit mutation frequency was 74%. Amino acid alterations in the 61 tumors with exon 11 mutations were deletion in 33 tumors, substitution in 20, both deletion and substitution in 4, insertion in 1, and duplication in 3. Histologically, tumors with c-kit mutations showed higher mitotic counts and higher cellularity. The 5-year relapse-free survival (RFS) in patients having GISTs with c-kit mutations was 21%, compared with 60% in those without c-kit mutations. Significantly higher RFS rates were observed in patients with tumors having mitotic counts < 5 mitoses/50 high power field, spindle-cell histology, tumor size < 5 cm, or gastric GISTs. Multivariate analyses indicated association of poorer RFS with a higher mitotic count [≥5 of 50 high power fields; odds ratio (OR) = 3.0], presence of c-kit mutations (OR = 5.6), and a larger tumor size (≥5 cm; OR = 4.2). Conclusions: The presence of c-kit mutation, along with high mitotic count and larger tumor size, was an independent factor for poor prognosis in patients with localized GISTs.
Exploring deep microbial life in coal-bearing sediment down to ~2.5 km below the ocean floor

A deep sleep in coal beds Deep below the ocean floor, microorganisms from forest soils continue to thrive. Inagaki et al. analyzed the microbial communities in several drill cores off the coast of Japan, some sampling more than 2 km below the seafloor (see the Perspective by Huber). Although cell counts decreased with depth, deep coal beds harbored active communities of methanogenic bacteria. These communities were more similar to those found in forest soils than in other deep marine sediments. Science, this issue p. 420; see also p. 376 Coal beds more than 2 kilometers below the seafloor host methanogenic bacteria related to those found in forest soils. [Also see Perspective by Huber] Microbial life inhabits deeply buried marine sediments, but the extent of this vast ecosystem remains poorly constrained. Here we provide evidence for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan. Microbial methanogenesis was indicated by the isotopic compositions of methane and carbon dioxide, biomarkers, cultivation data, and gas compositions. Concentrations of indigenous microbial cells below 1.5 km ranged from <10 to ~104 cells cm−3. Peak concentrations occurred in lignite layers, where communities differed markedly from shallower subseafloor communities and instead resembled organotrophic communities in forest soils. This suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.
Cellular Defense and Sensory Cell Survival Require Distinct Functions of ebi in Drosophila

The innate immune response and stress-induced apoptosis are well-established signaling pathways related to cellular defense. NF-κB and AP-1 are redox-sensitive transcription factors that play important roles in those pathways. Here we show that Ebi, a Drosophila homolog of the mammalian co-repressor molecule transducin β-like 1 (TBL1), variously regulates the expression of specific genes that are targets of redox-sensitive transcription factors. In response to different stimuli, Ebi activated gene expression to support the acute immune response in fat bodies, whereas Ebi repressed genes that are involved in apoptosis in photoreceptor cells. Thus, Ebi seems to act as a regulatory switch for genes that are activated or repressed in response to different external stimuli. Our results offer clear in vivo evidence that the Ebi-containing co-repressor complex acts in a distinct manner to regulate transcription that is required for modulating the output of various processes during Drosophila development.
Automated unsupervised authorship analysis using evidence accumulation clustering

Abstract Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents.
Metallicity and Nuclear Star Formation in Nearby Galaxy Pairs: Evidence for Tidally Induced Gas Flows

We derive the first luminosity-metallicity (L-Z) relation for a large objectively selected sample of local galaxy pairs, and we compare the L-Z relation of the pairs with the relation for the Nearby Field Galaxy Survey. Galaxy pair members with small projected separations (s < 20 kpc h-1) have systematically lower metallicities (∼0.2 dex on average) than either the field galaxies or more widely separated pairs at the same luminosity. There is a strong correlation between metallicity and central burst strength in the galaxy pairs. All five galaxies in the pairs sample with strong central bursts have close companions and metallicities lower than the comparable field galaxies. Our results provide strong observational evidence for a merger scenario in which galaxy interactions cause gas flows toward the central regions, carrying less enriched gas from the outskirts of the galaxy into the central regions. The less enriched gas dilutes the preexisting nuclear gas to produce a lower metallicity than would be obtained prior to the interaction. These gas flows trigger central bursts of star formation, causing strong central burst strengths and possibly aiding the formation of blue bulges. We show that the timescale and central gas dilution required by this scenario are consistent with predictions from hydrodynamic merger models.
Rejection algorithm for mis-segmented characters in multilingual document recognition

In OCR systems the character segmentation algorithmmay generate mis-segmented blocks. Feedbackinformation from character classifier is indispensable toachieve higher character segmentation accuracy. In thispaper a novel rejection algorithm is proposed to identifythese mis-segmented characters more accurately. First,based on confidence evaluation of distance-basedclassifiers, the usual generalized confidence mappingfunction is modified to fit this specific purpose. Second, anovel adaptive thresholding rejection rule is proposed,which is more accurate and flexible. Experiments onChinese, Japanese and Korean document recognitionshowed that new rejection algorithm evidently improvedthe system performance, especially for low-qualityprinted document recognition.
Polynomial Path Orders

This paper is concerned with the complexity analysis of constructor term rewrite systems and its ramification in implicit computational complexity. We introduce a path order with multiset status, the polynomial path order POP ∗ , that is applicable in two related, but distinct contexts. On the one hand POP ∗ induces polynomial innermost runtime complexity and hence may serve as a syntactic, and fully automatable, method to analyse the innermost runtime complexity of term rewrite systems. On the other hand POP ∗ provides an order-theoretic characterisation of the polytime computable functions: the polytime computable functions are exactly the functions computable by an orthogonal constructor TRS compatible with POP ∗ .
IDENTIFICATION OF DYNAMICALLY

Todays model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to use linear quadratic optimal control theory. However, it is diicult to identify the mathematical model of a dynamically positioned (DP) ship since the ship is not persistently excited under DP. In addition the ship parameter estimation problem is nonlinear and multivariable with only position and thruster state measurements available for parameter estimation. The process and measurement noise most also be modelled in order to avoid parameter drift due to environmental disturbances and sensor failure. This article discusses an oo-line parallel extended Kalman lter (EKF) algorithm utilizing two measurement series in parallel to estimate the parameters in the DP ship model. Full-scale experiments with a supply vessel are used to demonstrate the convergence and robustness of the proposed parameter estimator.
62-GHz 24-mW static SiGe frequency divider

We present a static frequency divider with a divide ratio of 16. The circuit is realized in current-mode logic (CML) and operates up to a maximum frequency of 62 GHz. The first master-slave flip-flop uses shunt peaking and consumes only 4 mW from a 2 V supply. The total supply current including all four divider stages and buffers is 12 mA. The circuit is manufactured in a 200-GHz f/sub T/ SiGe bipolar process.