Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Performance of Recommender Systems: An Experimental Comparison

Much early evaluation work focused specifically on the "accuracy" of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms. Other considerations include (1) coverage, which measures the percentage of a data set that a recommender system is able to provide recommendation for, (2) confidence metrics that can help users make more effective decisions, (3) computing time, which measures how quickly an algorithm can produce good recommendations, (4) novelty/serendipity, which measure whether a recommendation is original, and (5) robustness which measure the ability of the algorithm to make good predictions in the presence of noisy or sparse data. Six collaborative recommendation methods are investigated. Results on artificial data sets (for robustness) or on the real MovieLens data set (for accuracy, novelty, and computing time) are included and analyzed, showing that kernel-based algorithms provide the best results overall.
Elasto-plastic stresses in thick walled cylinders

In the optimal design of a modern gun barrel, there are two main objectives to be achieved: increasing its strength-weight ratio and extending its fatigue life. This can be carried out by generating a residual stress field in the barrel wall, a process known as autofrettage. It is often necessary to machine the autofrettaged cylinder to its final configuration, an operation that will remove some of the desired residual stresses. In order to achieve a residual stress distribution which is as close as possible to the practical one, the following assumptions have been made in the present research on barrel analysis: A von Mises yield criterion, isotropic strain hardening in the plastic region in conjunction with the Prandtl-Reuss theory, pressure release taking into consideration the Bauschinger effect and plane stress conditions. The stresses are calculated incrementally by using the finite difference method, whereby the cylinder wall is divided into N-rings at a distance Δr apart. Machining is simulated by removing rings from both sides of the cylindrical surfaces bringing the cylinder to its final shape. After a theoretical development of the procedure and writing a suitable computer program, calculations were performed and a good correlation with the experimental results was found. The numerical results were also compared with other analytical and experimental solutions and a very good correlation in shape and magnitude has been obtained.
Analyzing microarray data using CLANS

UNLABELLED Analysis of microarray experiments is complicated by the huge amount of data involved. Searching for groups of co-expressed genes is akin to searching for protein families in a database as, in both cases, small subsets of genes with similar features are to be found within vast quantities of data. CLANS was originally developed to find protein families in large sets of amino acid sequences where the amount of data involved made phylogenetic approaches overly cumbersome. We present a number of improvements that greatly extend the previous version of CLANS and show its application to microarray data as well as its ability of incorporating additional information to facilitate interactive analysis. AVAILABILITY The program is available for download from: http://bioinfoserver.rsbs. anu.edu. au/downloads/clans/
New structure for corner compensation in anisotropic KOH etching

A new compensation layout for the protection of convex corners during anisotropic etching resulting in almost perfectly square corners is presented. The compensation mask design is tested in KOH etching and compared to other compensation designs presented earlier. Advantageous new features are that etching to half of the designed depth of the new corner compensation is geometrically defined and not dependent on KOH concentration - a unique feature - and a high space efficiency, about 20% better than earlier designs for perfect square corners.
Energy spectrum extraction and optimal imaging via dual-energy material decomposition

Inferior soft-tissue contrast resolution is a major limitation of current CT scanners. The aim of the study is to improve the contrast resolution of CT scanners using dual-energy acquisition. Based on dual-energy material decomposition, the proposed method starts with extracting the outgoing energy spectrum by polychromatic forward projecting the material-selective images. The extracted spectrum is then reweighted to boost the soft-tissue contrast. A simulated water cylinder phantom with inserts that contain a series of six solutions of varying iodine concentration (range, 0-20 mg/mL) is used to evaluate the proposed method. Results show the root mean square error (RMSE) and mean energy difference between the extracted energy spectrum and the spectrum acquired using an energy-resolved photon counting detector(PCD), are 0.044 and 0.01 keV, respectively. Compared to the method using the standard energy-integrating detectors, dose normalized contrast-to-noise ratio (CNRD) for the proposed method are improved from 1 to 2.15 and from 1 to 1.88 for the 8 mg/mL and 16 mg/mL iodine concentration inserts, respectively. The results show CT image reconstructed using the proposed method is superior to the image reconstructed using the standard method that using an energy-integrating detector.
A framework for improved training of Sigma-Pi networks

This paper proposes and demonstrates a framework for Sigma-Pi networks such that the combinatorial increase in product terms is avoided. This is achieved by only implementing a subset of the possible product terms (sub-net Sigma-Pi). Application of a dynamic weight pruning algorithm enables redundant weights to be removed and replaced during the learning process, hence permitting access to a larger weight space than employed at network initialization. More than one learning rate is applied to ensure that the inclusion of higher order descriptors does not result in over description of the training set (memorization). The application of such a framework is tested using a problem requiring significant generalization ability. Performance of the resulting sub-net Sigma-Pi network is compared to that returned by optimal multi-layer perceptrons and general Sigma-Pi solutions.
Low-Area Wallace Multiplier

Multiplication is one of the most commonly used operations in the arithmetic. Multipliers based on Wallace reduction tree provide an area-efficient strategy for high speed multiplication. A number of modifications are proposed in the literature to optimize the area of the Wallace multiplier. This paper proposed a reduced-area Wallace multiplier without compromising on the speed of the original Wallace multiplier. Designs are synthesized using Synopsys Design Compiler in 90 nm process technology. Synthesis results show that the proposed multiplier has the lowest area as compared to other tree-based multipliers. The speed of the proposed and reference multipliers is almost the same.
Optimal Camera Trajectory with Image-Based Control

Image-based servo is a local control solution. Thanks to the feedback loop closed in the image space, local convergence and stability in the presence of modeling errors and noise perturbations are ensured when the error is small. The principal deficiency of this approach is that the induced (3D) trajectories are not optimal and sometimes, especially when the displacement to realize is large, these trajectories are not physically valid leading to the failure of the servoing process. In this paper we address the problem of finding realistic image-space trajectories that correspond to optimal 3D trajectories. The camera calibration and the model of the observed scene are assumed unknown. First, a smooth closed-form collineation path between given start and end points is obtained. This path is generated in order to correspond to an optimal camera path. The trajectories of the image features are then derived and efficiently tracked using a purely image-based control. A second path planning scheme, based on the potential field method is briefly presented. It allows us to introduce constraints in the desired trajectory to be realized. Such constraints are, for instance, to ensure that the object of interest remains in the camera field of view and to avoid the robot joints limits. Experimental results obtained on a six-degrees-of-freedom eye-in-hand robotic system are presented and confirm the validity of the proposed approach.
Isolation of monoclonal antibodies specific for human c-myc proto-oncogene product

Six monoclonal antibodies have been isolated from mice immunized with synthetic peptide immunogens whose sequences are derived from that of the human c-myc gene product. Five of these antibodies precipitate p62c-myc from human cells, and three of these five also recognize the mouse c-myc gene product. None of the antibodies sees the chicken p110gag-myc protein. All six antibodies recognize immunoblotted p62c-myc. These reagents also provide the basis for an immunoblotting assay by which to quantitate p62c-myc in cells.
P53-dependent induction of serine proteases in irradiated mouse colon

Tumor suppressor p53 induces apoptosis through the transactivation of target genes. Previous work has shown that p53-dependent gene expression changes in response to ionizing radiation are tissue specific. To determine critical p53 target genes in the colon, we irradiated wild-type and p53-null mice and examined the global p53 gene expression patterns in response to ionizing radiation. Microarray analysis using the Affymetrix MOE430A genechip showed that many of the genes that increased in a p53-dependent manner are serine proteases (e.g. elastase, trypsin) and other proteases (e.g. carboxypeptidases). Reverse transcription polymerase chain reaction (RT-PCR), immunohistochemistry, and Western blots were used to validate microarray results on trypsin 4, carboxypeptidase A1, and elastase-2, three genes that have potential p53-binding elements. Further study of tissue specific mediators of p53-dependent responses such as serine proteases will greatly increase understanding of in vivo p53-dependent pathways within the colon triggered by ionizing radiation.
Developing non-parametric density estimation on genetic evolution computing as a cloud based sensor fusion method: Taking psychiatric major depressive disorder detection as an application example

Biomedical cloud computing offers on-demand healthcare services. A sensor fusion method is developed based on non-parametric density estimation on genetic evolution computing. Our method provides a potential solution for decision making on flicking features when not all measurements of sensors appear at the input end. The method was applied on major depressive disorder detection as an application example and it was successfully for MDD classification regardless different combinations of sensor monitoring.
Study on SOC Forecast Model in Regions of Hilly Purple Soil by Water Erosion

The soil productive forces are mainly affected by soil erosion as soil nutrients and organic carbon are taken away. On the premise of analysis of the last 17 years’ data of rainfalls and vegetation coverage in Xiejiawan small watershed, the grey model was established to forecast and evaluate the losses of organic carbon caused by water erosion in the hilly area of purple soils. Moreover, according to comparison of four-dimensional, five-dimensional and six-dimensional model groups, we found that, six-dimensional GM (1,1) model was the best one, posterior-variance-test accuracy of which amounted to the first class, and maximum simulated relative error ,average simulated relative error and predicted relative error of which were respectively 43.26 % ,16.85% and -17.09%, besides, its simulation and prediction accuracy was also high. Furthermore, the accuracy of simulated forecast value was all satisfactory. So the SOC loss by water erosion in purple areas of Xiejiawan small watershed was simulated and predicted well by grey model, also the unique evolution patterns of changing organic carbon in purple soil by water erosion could be well described and explained by the grey model.
Mining e-mail content for author identification forensics

We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different e-mail topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.
Discovery of Cancellation Regions within Process Mining Techniques

Process mining is a relatively new field of computer science which deals with process discovery and analysis based on event logs. In this work we consider the problem of discovering workflow nets with cancellation regions from event logs. Cancellations occur in the majority of real-life event logs. In spite of huge amount of process mining techniques little has been done on cancellation regions discovery. We show that the state-based region algorithm gives labeled Petri nets with overcomplicated control flow structure for logs with cancellations. We propose a novel method to discover cancellation regions from the transition systems built on event logs and show the way to construct equivalent workflow net with reset arcs to simplify the control flow structure.
The macrophage scavenger receptor at 30 years of age: current knowledge and future challenges Work in the authors' laboratories is funded by the British Heart Foundation. Published, JLR Papers in Press, December 11, 2008.

It is now thirty years since the original observation that macrophages take up and degrade modified forms of low density lipoprotein (LDL). Molecular cloning has identified multiple scavenger receptors that can endocytose modified LDL and binding studies continue to identify a wide range of scavenger receptor ligands displayed by bacterial pathogens, modified self proteins and apoptotic cells. Scavenger receptors drive macrophage foam cell development in vitro but their exact role in the development of atherosclerosis remains difficult to assess critically. This could be due in part to functional redundancy or it could perhaps reflect our incomplete appreciation of all the potential roles of this family of receptors in host defense and tissue homeostasis. In this brief overview of the extensive literature in this area we emphasize the continuing duality of scavenger receptors as sensors of both nonself (pathogens) and modified self (modified forms of LDL and apoptotic cells). We highlight some under-appreciated roles of scavenger receptors in macrophage biology (e.g., regulation of macrophage cytokine responses) and we speculate on potential approaches to target the activity of this diverse family of receptors for therapeutic benefit in cardiovascular disease.
Extracting significant places from mobile user GPS trajectories: a bearing change based approach

Moving object data, in particular of mobile users, is becoming widely available. A GPS trajectory of a moving object is a time-stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for a range of applications. Existing studies have extracted knowledge from trajectory patterns for both single and multiple GPS trajectories. However, few works have taken into account the unreliability of GPS measurements for mobile devices or focused on the extraction of fine-grained events from a user's GPS trajectory, such as waiting in traffic, at an intersection, or at a bus stop. In this paper, we develop and experimentally evaluate a novel algorithm that analyses a mobile user's bearing change distribution, together with speed and acceleration, to extract significant places of events from their GPS trajectory.
A Careful Look at Binding Site Reorganization in the even-skipped Enhancers of Drosophila and Sepsids

lines wherever BLAST finds a short stretch of sequence similarity (forward- and reverse-strand hits are distinguished by the direction of the line). The name ‘‘homology plot’’ is a misnomer, as most of the hits they display have BLAST scores too low to confirm the evolutionary relationship implied by the term homology—so we will use the more appropriate term ‘‘twodimensional similarity plot. ’’ Nonetheless, these plots, and the more commonly used dot plots [3], are an excellent tool for identifying potential regions of homology between distantly related species. Indeed, our initial discovery of the sepsid-Drosophila–conserved noncoding blocks was based on dot plots we generated to compare our newly identified enhancers to their D. melanogaster orthologs. These dot plots, as well as two-dimensional similarity plots for each enhancer, are shown in Figure S1. Crocker and Erives cite the presence of multiple hits along a single diagonal in similarity plots of the D. melanogaster and Themira putris stripe 2 enhancers to argue that there is conservation of the global organization of this enhancer. However, they overemphasize the significance of this observation by displaying only a portion of the enhancer, placing reverse-strand hits in a separate panel, and augmenting ondiagonal hits with blue lines that are longer than the hits themselves. A more straightforward way to visualize these data is to plot each enhancer in parallel, with regions of similarity connected by bars whose width represents the size of the matched region and whose color
The Electroencephalogram Does Not Predict Depth of Isoflurane Anesthesia

BackgroundThe power spectrum of the electroencephalogram (EEG) may be analyzed to provide quantitative measures of EEG activity (e.g., spectral edge, which defines the highest EEG frequency at which significant activity is found). The current study tested the hypothesis that spectral edge and similar measures distinguish different functional depths of anesthesia in humans. MethodsThree groups were studied. Group 1 consisted of 34 surgical patients (ASA physical status 1 or 2) who received 0.6, 1.0 and 1.4 MAC isoflurane anesthesia. A subgroup (group 2) of group 1 was tested during 1.0 MAC isoflurane anesthesia at surgical incision. Group 3 consisted of 16 volunteers who listened to an audiotape while receiving 0.15, 0.3, and 0.45 MAC isoflurane or 0.3, 0.45, and 0.6 MAC nitrous oxide in oxygen. The audiotape contained information designed to test implicit and explicit memory formation. We tested the ability of six EEG parameters (spectral-edge, 95th percentile power frequency, median power, and zero crossing frequencies and total power in the α- [8–13 Hz] and δ- [<4 Hz] power ranges) to predict movement after surgical incision, purposeful response to command, or memory of information presented during anesthetic administration. ResultsIsoflurane decreased EEG activity in group 1 in a dose-related fashion. The 55% of group 2 who made purposeful movements in response to incision did not differ in their EEG from nonresponders (e.g., spectral edge 19.8 ± 3.1 vs. 19.3 ± 2.6 Hz, mean ± SD). In group 3, memory of the information presented did not correlate with values of any EEG parameter. Response to verbal command was associated with lower anesthetic concentrations and with smaller α- and δ-band power (298 ± 66 vs. 401 ± 80 watts; and 75 ± 20 vs. 121 ± 49 watts, mean ± SD), but there was no difference in values for other parameters. ConclusionsWe conclude that our EEG measures do not predict depth of anesthesia as defined by the response to surgical incision, the response to verbal command or the development of memory.
Predicting Future Reading Problems Based on Pre-reading Auditory Measures: A Longitudinal Study of Children with a Familial Risk of Dyslexia

Purpose: This longitudinal study examines measures of temporal auditory processing in pre-reading children with a family risk of dyslexia. Specifically, it attempts to ascertain whether pre-reading auditory processing, speech perception, and phonological awareness (PA) reliably predict later literacy achievement. Additionally, this study retrospectively examines the presence of pre-reading auditory processing, speech perception, and PA impairments in children later found to be literacy impaired. Method: Forty-four pre-reading children with and without a family risk of dyslexia were assessed at three time points (kindergarten, first, and second grade). Auditory processing measures of rise time (RT) discrimination and frequency modulation (FM) along with speech perception, PA, and various literacy tasks were assessed. Results: Kindergarten RT uniquely contributed to growth in literacy in grades one and two, even after controlling for letter knowledge and PA. Highly significant concurrent and predictive correlations were observed with kindergarten RT significantly predicting first grade PA. Retrospective analysis demonstrated atypical performance in RT and PA at all three time points in children who later developed literacy impairments. Conclusions: Although significant, kindergarten auditory processing contributions to later literacy growth lack the power to be considered as a single-cause predictor; thus results support temporal processing deficits' contribution within a multiple deficit model of dyslexia.
Utilizing the influencing factors of group learning for simulating a model of a group learning process

Group learning is an essential component for organizing cooperative learning or collaborative learning, and it is important for implementing CSCL as well. In this paper, we introduce an empirical approach for simulating a group learning model based on its influencing factors which have been extracted from our previous study. The architecture and a generic model of a group learning process are presented, and a model of a group learning process is devised for a blended e-learning environment.
Computing Semantic Similarity of Documents Based on Semantic Tensors

Exploiting semantic content of texts due to its wide range of applications such as finding related documents to a query, document classification and computing semantic similarity of documents has always been an important and challenging issue in Natural Language Processing. In this paper, using Wikipedia corpus and organizing it by three-dimensional tensor structure, a novel corpus-based approach for computing semantic similarity of texts is proposed. For this purpose, first the semantic vector of available words in documents are obtained from the vector space derived from available words in Wikipedia articles, then the semantic vector of documents is formed according to their words vector. Consequently, semantic similarity of a pair of documents is computed by comparing their corresponding semantic vectors. Moreover, due to existence of high dimensional vectors, the vector space of Wikipedia corpus will cause curse of dimensionality. On the other hand, vectors in high-dimension space are Usually very similar to each other. In this way, it would be meaningless and vain to identify the most appropriate semantic vector for the words. Therefore, the proposed approach tries to improve the effect of the curse of dimensionality by reducing the vector space dimensions through random indexing. Moreover, the random indexing makes significant improvement in memory consumption of the proposed approach by reducing the vector space dimensions. Additionally, the capability of addressing synonymous and polysemous words will be feasible in the proposed approach by means of the structured co-occurrence through random indexing.