Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and predicting the task-by-task behavior of search engine users

Web search engines answer user needs on a query-by-query fashion, namely they retrieve the set of the most relevant results to each issued query, independently. However, users often submit queries to perform multiple, related tasks. In this paper, we first discuss a methodology to discover from query logs the latent tasks performed by users. Furthermore, we introduce the Task Relation Graph (TRG) as a representation of users' search behaviors on a task-by-task perspective. The task-by-task behavior is captured by weighting the edges of TRG with a relatedness score computed between pairs of tasks, as mined from the query log. We validate our approach on a concrete application, namely a task recommender system, which suggests related tasks to users on the basis of the task predictions derived from the TRG. Finally, we show that the task recommendations generated by our solution are beyond the reach of existing query suggestion schemes, and that our method recommends tasks that user will likely perform in the near future.
Subcortical contribution to late TMS-induced I-waves in intact humans

Paired-pulse transcranial magnetic stimulation (TMS) of the human motor cortex results in consecutive facilitatory motor evoked potential (MEP) peaks in surface electromyography. It has been proposed that early and late MEP peaks involve different mechanisms of action; however, little is known about the characteristics of the later peaks. Using paired-pulse TMS over the hand motor cortex at different test (S1) and conditioning (S2) interstimulus intervals and intensities we examined early (first) and late (second and third) MEP peaks in a resting finger muscle. We demonstrate that the third peak had reduced amplitude and duration compared with the second, regardless of the S1 intensity. Higher S2 intensity increased the amplitude of the third but not the second peak, suggesting that the third peak had a higher threshold. The interval between the second and third peak was longer than between the first and second peak in all conditions even though all peaks had a similar latency dispersion. No differences were found in the amplitude, duration, and threshold of the first and second peaks. A threshold electrical S2 over the cervicomedullary junction facilitated the second and third but not the first peak similarly to TMS. Our results indicate that the third MEP peak is smaller and has higher threshold than the second peak and the similarities between the first and second peak suggest that this is less likely explained by a reduced effectiveness in recruitment. We argue that subcortical pathways might contribute to differences found between late TMS-induced peaks in intact humans.
A robust gradient clock synchronization algorithm for wireless sensor networks

A variation of the classical clock synchronization problem is to synchronize clocks with an error that is proportinal to their distance in the network. Among several applications, gradient clock synchronization in wireless sensor networks can yield precise time coordination of packet transmissions, allowing for time-based collision free transmission scheduling. In this paper, we propose a simple and robust gradient clock synchronization (RGCS) algorithm designed for wireless sensor networks. The algorithm is fully distributed and operates independently of network topology, network routing and has no parameters to tune. It also does not require strict timing or reliability in clock synchronization messages or any global time reference. In particular, RGCS is based on a running average of logical clock rates and addresses specific aspects of gradient synchronization, such as estimating the logical clock of neighbors. We evaluate the algorithm through realistic simulations in various scenarios to illustrate the benefits of our approach. We also compare our results with GTSP a prominent gradient clock synchronization algorithm. Our results indicates that RCGS can operate adequately under high loss rates (50%) and high variability in the period of synchronization messages.
TOWARDS PROGRAMMABLE NETWORKS

Intermediate nodes (e.g., routers, switches) of cur rent networks, in contrast with end nodes (e.g., PC s workstations), are vertically integrated closed sys tems. Their functions, mostly implemented by embedd ed software, are rigidly built-in by intermediate node s vendors. Vendors must follow designs dictated by slow and intractable standard committees rather tha n pursue rapid introduction of innovative costeffective technologies. There is thus a need for ne w technologies that would enable programming intermediate nodes with the same simplicity of programmi ng end-nodes. This paper describes the NetScript project, pursuing agent-based middleware for progra mming functions of intermediate network nodes. Delegated agents are used to deploy functions in in termediate nodes. The NetScript programming language provides means to script processing of packet streams; it is particularly suitable to program ro uting, packet analyzers or signalling functions. This pap er describes an architecture for programmable netwo rk nodes; middleware for agent-based programming of th ese nodes; the NetScript language to program node functions and applications of these technologies to create programmable SNMP agents, protocol analyzers, signalling and routing protocols and firewalls.
Identification of a DNA Methylome Profile of Esophageal Squamous Cell Carcinoma and Potential Plasma Epigenetic Biomarkers for Early Diagnosis

DNA methylation is a critical epigenetic mechanism involved in key cellular processes. Its deregulation has been linked to many human cancers including esophageal squamous cell carcinoma (ESCC). This study was designed to explore the whole methylation status of ESCC and to identify potential plasma biomarkers for early diagnosis. We used Infinium Methylation 450k array to analyze ESCC tissues (n = 4), paired normal surrounding tissues (n = 4) and normal mucosa from healthy individuals (n = 4), and combined these with gene expression data from the GEO database. One hundred and sixty eight genes had differentially methylated CpG sites in their promoter region and a gene expression pattern inverse to the direction of change in DNA methylation. These genes were involved in several cancer-related pathways. Three genes were validated in additional 42 ESCC tissues and paired normal surrounding tissues. The methylation frequency of EPB41L3, GPX3, and COL14A1 were higher in tumor tissues than in normal surrounding tissues (P<0.017). The higher methylation frequency of EPB41l3 was correlated with large tumor size (P = 0.044) and advanced pT tumor stage (P = 0.001). The higher methylation frequency of GPX3 and COL14A1 were correlated with advanced pN tumor stage (P = 0.001 and P<0.001). The methylation of EPB41L3, GPX3, and COL14A1 genes were only found in ESCC patients' plasma, but not in normal individuals upon testing 42 ESCC patients and 50 healthy individuals. Diagnostic sensitivity was increased when methylation of any of the 3 genes were counted (64.3% sensitivity and 100% specificity). These differentially methylated genes in plasma may be used as biomarkers for early diagnosis of ESCC.
Sparse Matrix Format Selection with Multiclass SVM for SpMV on GPU

Sparse Matrix-Vector Multiplication (SpMV) kernel dominates the computing cost in numerous scientific applications. Many implementations based on different sparse formats were proposed recently for this kernel on the GPU side. Since the performance of these sparse formats varies significantly according to the sparsity characteristics of the input matrix and the hardware specifications, no one of them can be considered as the best one to use for every sparse matrix. In this paper, we address the problem of selecting the best representation for a given sparse matrix on GPU by using a machine learning approach. First, we present some interesting and easy to compute features for characterizing the sparse matrices on GPU. Second, we use a multiclass Support Vector Machine (SVM) classifier to select the best format for each input matrix. We consider in this paper four popular formats (COO, CSR, ELL, and HYB), but our work can be extended to support more sparse representations. Experimental results on two different GPUs (Fermi GTX 580 and Maxwell GTX 980 Ti) show that we achieved more than 98% of the performance possible with a perfect selection.
Efficacy of tinidazole for treatment of cats experimentally infected with Tritrichomonas foetus.

OBJECTIVE To determine the efficacy of tinidazole for treatment of cats with experimentally induced Tritrichomonas foetus infection. ANIMALS 8 specific-pathogen-free kittens. PROCEDURES Tinidazole was tested for activity against a feline isolate of T foetus in vitro. Kittens were infected orogastrically with the same isolate and treated or not with tinidazole (30 mg/kg, PO, q 24 h for 14 days). Amoxicillin was administered 28 weeks after completion of tinidazole administration to induce diarrhea. Feces were repeatedly tested for T foetus by use of PCR assay and microbial culture for 33 weeks. RESULTS Tinidazole killed T foetus at concentrations >or= 10 microg/mL in vitro. In experimentally induced infection, tinidazole administered at 30 mg/kg decreased T foetus below the limit of molecular detection in 2 of 4 cats. Recrudescent shedding of T foetus, as elicited by amoxicillin-induced diarrhea, was diminished in cats that received prior treatment with tinidazole. CONCLUSIONS AND CLINICAL RELEVANCE Although tinidazole decreased the detection of T foetus and treated cats were resistant to later efforts to incite the infection, inability of tinidazole to eradicate infection in many cats poses a serious impediment to the drug's effectiveness in practice.
An Investigation into the Use of Generalized Additive Neural Networks in Credit Scoring

Logistic regression occupies a central position in the field of credit scoring as it is relatively well understood and an explicit formula can be derived on which credit decisions may be based. Although artificial neural networks may be more powerful than logistic regression, it is not widely used in credit scoring because it is a black box with respect to interpretation and the absence of reasons why the neural network has reached its decisions may be unacceptable. In contrast to logistic regression and neural networks, generalized additive models is a compromise between inflexible, but docile linear models and flexible, but troublesome, universal approximators. In this study the performance of a generalized additive model (implemented as a neural network and therefore called a generalized additive neural network or GANN) is compared to that of a logistic regression model on a home equity data set, where the aim is to predict whether an applicant will eventually default or be seriously delinquent on a loan. Partial residuals are used to investigate the effect of the individual inputs, adjusted for the effect of the other inputs, where the jth partial residual is the deviation between the actual values and that portion of the fitted model that does not involve variable xj. Using a GANN architecture therefore assists in alleviating the black box perception of neural networks with respect to interpretation as the effect of each input variable on the fitted model can be interpreted using a graphical method (partial residual plot). Another benefit of using a GANN is that the nonlinear effects of the inputs may be easier to learn in this constrained architecture than in the general artificial neural network or multiplayer perceptron setting.
DesignCon 2006 Practical Design Considerations for 10 to 25 Gbps Copper Backplane Serial Links

There is a general consensus that 10 Gbps copper backplane serial links will be deployed in the near future. This paper takes a look at the practical and cost effective design aspects of the 1012.5 Gbps links as well as the feasibility and requirements for the next generation of serial links operating in the 20-25 Gbps range. The design aspects considered include choice of various dielectric materials, connector technologies, via technologies, and active signal conditioning technologies. Four copper based systems are considered for simulation and are shown to be capable of operating at various data rates in the range of 10 to 20 Gbps.
Z badań nad przyjaźnią. Kompetencje społeczne a jakość relacji przyjacielskich

Friendship is a universal interpersonal relationship and is important in an individual’s social life or mental health. Previous empirical studies and theoretical considerations suggests that social competencies are correlated with friendship. The study focuses on finding and describing correlations between social competencies and the quality of friendship relations among young adults. Results of the empirical research allow to derive some important conclusions. Studies have assessed that individuals which are socially competent in a some dimension have the high quality of friendship relations.
Unsupervised topic model for broadcast program segmentation

Several unsupervised methods have been proposed to segment a continuous text stream into individual topics. A simple HMM formulation of the most successful of these methods exposes their underlying assumptions and suggests the use of a new prior for segmentation probability. Under this formulation, we explore the space of possible modeling choices on databases of English and French TV and radio programs. We show that the proposed prior improves segmentation results and can also accommodate additional knowledge sources within the HMM efficient dynamic programming.
Private Transfers, Borrowing Constraints, and Timing of Homeownership

The 1991 Italian Survey of Household Income and Wealth contains retrospective information on intergenerational transfers. This information is used to estimate the impact of transfers on the saving time required to purchase a house. It is found that transfers have a small impact on saving time and that after receiving a transfer households purchase considerably larger homes. The results have implications for the debate about the source of the relation between aggregate saving and growth.
Investigating Students' Perceptions of Collegiality in Doctoral Education

Collegiality in the doctoral environment and collegial peer communities are underresearched, and their potential to enhance doctoral education is under-utilised. Academic developers, researchers, government departments, corporate and industrial bodies, and students have reported that more could be done during doctoral education to develop students’ collegial practices. This thesis adopts a collective, student-centred approach to determine how students define and practise collegiality in the doctoral environment of one research-intensive university. Using a hybrid methodology of social practice theory and phenomenography, the research involved 43 doctoral student participants from all divisions of the university. Students took part either in focus groups, or in hierarchical card sorting activities; eleven students participated in both activities. For the card sorting activity, students worked in pairs to rank what they considered were the most important features of collegiality, then discussed their rankings. The doctoral students in this study demonstrated complex and subjective understandings of collegial practices and collegial relationships, and identified those involved in their doctoral education as colleagues. The students’ expectations of collegial relationships were shaped by codes of conduct that often go unarticulated. This study created a unique context in which doctoral students could name otherwise tacit codes of conduct that facilitate productive and respectful collegial practices. These doctoral students explained how their participation in collegial practices helped to mitigate some of the emotional work of isolation, which they accepted as part of doctoral study. While students in this study were the main architects of their collegial environment, I argue that those responsible for doctoral
To appear in 1997 Workshop on Automata Induction , Grammatical Inference , and Language Acquisition Learning to Parse Natural Language Database Queriesinto Logical

For most natural language processing tasks, a parser that maps sentences into a semantic representation is signiicantly more useful than a grammar or au-tomata that simply recognizes syntactically well-formed strings. This paper reviews our work on using inductive logic programming methods to learn de-terministic shift-reduce parsers that translate natural language into a semantic representation. We focus on the task of mapping database queries directly into ex-ecutable logical form. An overview of the system is presented followed by recent experimental results on corpora of Spanish geography queries and English job-search queries.
Study on Recognition Characteristics of Acoustic Emission Based on Fractal Dimension

It is difficult to recognise acoustic emission (AE) signal because of serious pollution by noise. Fractal dimension is a new method to describe the characteristics of AE signal. According to the complex computation and low precision of box counting dimension, correlation dimension and Katz dimension, an algorithm of logarithmic fractal dimension based on waveform length was proposed in this paper the deduction process was introduced. The experiment datas was rub impact AE signals sampled from rotating test stand. Gaussian white noise and non-stationary noise were added to simulate the field AE signal which polluted seriously by noise. Then, three algorithms based on box counting dimension, Katz dimension and logarithmic fractal dimension were compared in AE signal recognition. The results show that the algorithm of logarithmic dimension distinguishes rub impact AE signals from strong noise is more effectively, and has lower computation and higher precision than others. It provides a new approach to identify characteristics of AE signal and detect rub impact fault of rotating machinery.
The product of the Saccharomyces cerevisiae cell cycle gene DBF2 has homology with protein kinases and is periodically expressed in the cell cycle

Several Saccharomyces cerevisiae dbf mutants defective in DNA synthesis have been described previously. In this paper, one of them, dbf2, is characterized in detail. The DBF2 gene has been cloned and mapped, and its nucleotide sequence has been determined. This process has identified an open reading frame capable of encoding a protein of molecular weight 64,883 (561 amino acids). The deduced amino acid sequence contains all 11 conserved domains found in various protein kinases. DBF2 was periodically expressed in the cell cycle at a time that clearly differed from the time of expression of either the histone H2A or DNA polymerase I gene. Its first function was completed very near to initiation of DNA synthesis. However, DNA synthesis in the mutant was only delayed at 37 degrees C, and the cells blocked in nuclear division. Consistent with this finding, the execution point occurred about 1 h after DNA synthesis, and the nuclear morphology of the mutant at the restrictive temperature was that of cells blocked in late nuclear division. DBF2 is therefore likely to encode a protein kinase that may function in initiation of DNA synthesis and also in late nuclear division.
Research on knowledge community of educator of primary and secondary school based on network

This paper advances the three values of knowledge community of educator of primary and secondary school on promoting interns' professional development, realizing benign interaction between research on colleges and primary and secondary schools and transferring teachers' covert knowledge to dominant one, expounds staff structure of knowledge community constructing model, structure of platform and illustrates the efficient institution of pro moting rational communication.
Using a Competitive Clustering Algorithm to Comprehend Web Applications

We propose an approach based on winner takes all, a competitive clustering algorithm, to support the comprehension of static and dynamic Web applications. The process first computes the distances between the Web pages and then identifies similar pages through the winner takes all clustering algorithm. Two different instances of the process are presented to identify similar pages at structural and content level, respectively. The first instance encodes the page structure into a string and then uses the Levenshtein algorithm to achieve the distances between pairs of pages. On the other hand, to group similar pages at content level we use the latent semantic indexing to produce the page representations as vectors in the concept space. The Euclidean distance is then computed between the vectors to achieve the distances between the pages to be given as input to the adopted clustering algorithm. A prototype to automate the identification of group of similar pages has been implemented. The approach and the prototype have been assessed in a case study
Social Structure and Personality Enhanced Group Recommendation

The social aspects of the group members are usually omitted in today’s group recommenders. In this paper we propose novel approach for the intergroup processes modeling, while the friendship type, user’s personality and the group context is considered in order to reflect the group member influence. Moreover, the bi-directional emotional contagion is reflected by the spreading activation over the influence graph. In this manner we are able to compute adjusted ratings for recommended items which reflect and simulates the real users’ preferences.