Miracle-dz/UniFAR-FTUs huggingface dataset:
FORECASTING THE CATEGORIZATION OF END USER SERVICE THROUGH CONSIDERATION OF ONLINE USER CLASSIFICATION ATTITUDE

Fortunately, while using the recognition and rapid development of social systems, increasingly more users enjoy discussing their encounters, for example reviews, ratings, photos and moods. Social systems gather volumes of understanding contributed by users around the globe. This publish is flexible. It always contains item/services descriptions. We advise the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. We explore the user’s social circle, and split the social networking into three components, direct buddies, mutual buddies, combined with indirect buddies, to deep understand social users’ rating behavior diffusions. Within this paper, we advise a person-service rating conjecture model according to probabilistic matrix factorization by exploring rating behaviors. A sense of the rating schedule is suggested to represent user daily rating behavior. The similarity between user rating schedules is required to represent interpersonal rating behavior similarity. We conduct numerous experiments in Yelp and Douban Movie datasets. The experimental connection between our model show significant improvement.
Guillotine subdivisions approximate polygonal subdivisions: a simple new method for the geometric k-MST problem

We show that any rectilinear polygonal subdivision in the plane can be converted into a " guillotine " subdivision whose length is at most twice that of the original subdivision. " Guillotine " subdivisions have a simple recursive structure that allows one to search for " optimal " such subdivisions in polynomial time, using dynamic programming. In particular , a consequence of our main theorem is a very simple proof that the k-MST problem in the plane has a constant factor polynomial-time approximation algorithm, and the constant factor that we obtain is a substantial improvement over all previous bounds: We obtain a factor of 2 for the Ll metric, and a factor of 2fi for the Lz (Euclidean) metric.
New Subsampling Algorithms for Fast Least Squares Regression

We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data (n ≫ p). We propose three methods which solve the big data problem by subsampling the covariance matrix using either a single or two stage estimation. All three run in the order of size of input i.e. O(np) and our best method, Uluru, gives an error bound of O(√p/n) which is independent of the amount of subsampling as long as it is above a threshold. We provide theoretical bounds for our algorithms in the fixed design (with Randomized Hadamard preconditioning) as well as sub-Gaussian random design setting. We also compare the performance of our methods on synthetic and real-world datasets and show that if observations are i.i. d. , sub-Gaussian then one can directly subsample without the expensive Randomized Hadamard preconditioning without loss of accuracy.
Herschel-ATLAS/GAMA: a difference between star formation rates in strong-line and weak-line radio galaxies

We have constructed a sample of radio-loud objects with optical spectroscopy from the Galaxy and Mass Assembly (GAMA) project over the Herschel Astrophysical Terahertz Large Area Survey (Herschel-ATLAS) Phase 1 fields. Classifying the radio sources in terms of their optical spectra, we find that strong-emission-line sources (‘high-excitation radio galaxies’) have, on average, a factor of ∼4 higher 250-μm Herschel luminosity than weak-line (‘low-excitation’) radio galaxies and are also more luminous than magnitude-matched radio-quiet galaxies at the same redshift. Using all five H-ATLAS bands, we show that this difference in luminosity between the emission-line classes arises mostly from a difference in the average dust temperature; strong-emission-line sources tend to have comparable dust masses to, but higher dust temperatures than, radio galaxies with weak emission lines. We interpret this as showing that radio galaxies with strong nuclear emission lines are much more likely to be associated with star formation in their host galaxy, although there is certainly not a one-to-one relationship between star formation and strong-line active galactic nuclei (AGN) activity. The strong-line sources are estimated to have star formation rates at least a factor of 3–4 higher than those in the weak-line objects. Our conclusion is consistent with earlier work, generally carried out using much smaller samples, and reinforces the general picture of high-excitation radio galaxies as being located in lower-mass, less evolved host galaxies than their low-excitation counterparts.
The XRCC1 Arg194Trp polymorphism is not a risk factor for glioma: A meta-analysis involving 1,440 cases and 2,562 controls

Previous reports have indicated that the X-ray repair cross-complementing gene 1 (XRCC1) Arg194Trp polymorphism may be a risk factor for several types of cancer. Published studies on the association of XRCC1 Arg194Trp polymorphisms with glioma risk have yeilded conflicting results. The present study aimed to obtain a more precise estimation of this association. Meta-analyses assessing the association of the XRCC1 Arg194Trp variation with glioma were conducted and subgroup analyses based on ethnicity and source of controls were also performed. Eligible studies were identified during the period before May 2012. A total of four case-control studies comprising 1,440 cases and 2,562 controls were finally selected for analysis. The overall data failed to indicate a significant association of the XRCC1 Arg194Trp polymorphism with glioma risk [Trp vs. Arg: odds ratio (OR) = 1.01, 95% confidence interval (95% CI) = 0.77–1.33; Trp/Trp vs. Arg/Arg: OR = 1.56, 95% CI = 0.96–2.54; dominant model: OR = 0.98, 95% CI = 0.74–1.31; recessive model: OR = 1.48, 95% CI = 0.92–2.38]. Similarly, in the subgroup analysis based on ethnicity and source of controls, no associations were observed. In conclusion, the results of the present study failed to suggest an association between the XRCC1 Arg194Trp polymorphism and glioma risk. Further large and well-designed studies are required to confirm this conclusion.
Volume reduction surgery impairs immediate postoperative pulmonary function in canine emphysema.

RATIONALE In severe pulmonary emphysema, lung volume reduction surgery (LVRS) improves pulmonary function over a 2-yr period in selected patients. However, the changes in lung function and maximal flow (Vmax) occurring immediately postoperatively are not clear and may contribute to the high morbidity observed. In the present study, we used a chronic canine model of upper lobe emphysema to address this question. METHODS Bilateral upper lobe emphysema was produced by the intrabronchial administration of papain. Measurements were made before and immediately after LVRS was performed. A vacuum-assisted surgical system (VALR Surgical System; Spiration, Redmond, WA) that deploys a compression sleeve over portions of the disease tissue was used to produce LVRS. Changes in Vmax were interpreted in terms of the wave-speed theory of flow limitation in which a pressure sensor was placed into the airway to determine the site of limitation and intrabronchial pressures. RESULTS In the emphysema group, total lung capacity postemphysema increased to approximately 20% above the preemphysema value, whereas Vmax was reduced as compared with a control group. After LVRS, tidal respiratory compliance and Vmax decreased, whereas lung elastic recoil and frictional resistance increased in both the emphysema and control groups as compared with presurgery. CONCLUSION The acute effect of LVRS leads to an impairment in lung mechanical properties. These changes could contribute to ventilatory complications, including the difficulty of weaning patients from mechanical ventilation and the mortality observed from this procedure.
A robust and efficient parallel SVD solver based on restarted Lanczos bidiagonalization.

Lanczos bidiagonalization is a competitive method for computing a partial singular value decomposition of a large sparse matrix, that is, when only a subset of the singular values and corresponding singular vectors are required. However, a straightforward implementation of the algorithm has the problem of loss of orthogonality between computed Lanczos vectors, and some reorthogonalization technique must be applied. Also, an effective restarting strategy must be used to prevent excessive growth of the cost of reorthogonalization per iteration. On the other hand, if the method is to be implemented on a distributed-memory parallel computer, then additional precautions are required so that parallel efficiency is maintained as the number of processors increases. In this paper, we present a Lanczos bidiagonalization procedure implemented in SLEPc, a software library for the solution of large, sparse eigenvalue problems on parallel computers. The solver is numerically robust and scales well up to hundreds of processors.
The performance impact of incomplete bypassing in processor pipelines

Misprediction of conditional branches is a major cause for reduced performance in processor implementations with large numbers of functional units. We present a hardware scheme which records the path leading to a conditional branch in order to predict the outcome of the branch instruction more accurately. The proposed scheme is analyzed using instruction traces from integer benchmark programs. The results indicate that knowledge of path information leads to better prediction than knowledge of simply the previous branch outcomes for a given number of history items. The results further show that even for equivalent hardware cost, path-based correlation often outperforms patient-based correlation, especially when history information is periodically destroyed, for example, due to context switches.
Combustion response of ammonium perchlorate

A modified Price–Boggs–Derr model is applied to compute the linear and nonlinear combustion response properties of monopropellant ammonium perchlorate (AP). The kinetics constants were changed to achieve good agreement with response function data as well as with steady-state data.The numerical method was first validated with the classical theory. Computations using the Levine and Culick boundary condition in the limit of small perturbations were compared with the exact mathematical solution for linear response, and the effect of perturbation amplitude was explored. Then, using the AP model for the boundary condition, various linear and nonlinear computations were performed. Supplemental mathematical analyses relate the AP model to the basic two parameters of the classical theory and show the key factors determining the nature of the combustion response.
Corticostriatal Connectivity Underlies Individual Differences in the Balance between Habitual and Goal-Directed Action Control

Why are some individuals more susceptible to the formation of inflexible habits than others? In the present study, we used diffusion tensor imaging to demonstrate that brain connectivity predicts individual differences in relative goal-directed and habitual behavioral control in humans. Specifically, vulnerability to habitual “slips of action” toward no-longer-rewarding outcomes was predicted by estimated white matter tract strength in the premotor cortex seeded from the posterior putamen (as well as by gray matter density in the posterior putamen as determined with voxel-based morphometry). In contrast, flexible goal-directed action was predicted by estimated tract strength in the ventromedial prefrontal cortex seeded from the caudate. These findings suggest that integrity of dissociable corticostriatal pathways underlies individual differences in action control in the healthy population, which may ultimately mediate vulnerability to impulse control disorders.
An Empirical Study of Spectral Social Network Partition on GPGPU Platforms

Social network analysis has attracted extensive research attention recently. By setting persons or more general entities as network vertices and using edges to represent their interactions, network provides an effective tool in representing complex social relations. Dividing such a network into different clusters in accordance with its inherent structure has been widely investigated. A method called modularity optimization is a principled and widely adopted way to handle this problem. But with high computational cost, an approximate solution, the spectral method, has been introduced to ensure the tractability for large-scale networks. Our work implemented the spectral method on a platform with general-purpose computing on graphics processing units (GPGPU) and compared the results with conventional solutions, which reported significantly improved running speed.
CodeBender: Remote Software Protection Using Orthogonal Replacement

CodeBender implements a novel client replacement strategy to counter the malicious host problem and address the problem of guaranteeing client-code security. CodeBender is a tool that implements a novel client-replacement strategy to counter the malicious host problem. It works by limiting the client code's validity and, when the code expires, by having the server provide a new client that replaces the former one. The complexity of analyzing frequently changing, always different (orthogonal) program code deters an adversary's reverse engineering efforts. We've implemented CodeBender and tested its practicability in two case studies.
Optimal Nonparametric Estimation of First-price Auctions

This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auctioni data. Considering first-price sealed-bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theoiy of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two-step kernel-based estimator that converges at the optimal rate.
Amino Acids Regulate Transgene Expression in MDCK Cells

Gene expression and cell growth rely on the intracellular concentration of amino acids, which in metazoans depends on extracellular amino acid availability and transmembrane transport. To investigate the impact of extracellular amino acid concentrations on the expression of a concentrative amino acid transporter, we overexpressed the main kidney proximal tubule luminal neutral amino acid transporter B0AT1-collectrin (SLC6A19-TMEM27) in MDCK cell epithelia. Exogenously expressed proteins co-localized at the luminal membrane and mediated neutral amino acid uptake. However, the transgenes were lost over few cell culture passages. In contrast, the expression of a control transgene remained stable. To test whether this loss was due to inappropriately high amino acid uptake, freshly transduced MDCK cell lines were cultivated either with physiological amounts of amino acids or with the high concentration found in standard cell culture media. Expression of exogenous transporters was unaffected by physiological amino acid concentration in the media. Interestingly, mycoplasma infection resulted in a significant increase in transgene expression and correlated with the rapid metabolism of L-arginine. However, L-arginine metabolites were shown to play no role in transgene expression. In contrast, activation of the GCN2 pathway revealed by an increase in eIF2α phosphorylation may trigger transgene derepression. Taken together, high extracellular amino acid concentration provided by cell culture media appears to inhibit the constitutive expression of concentrative amino acid transporters whereas L-arginine depletion by mycoplasma induces the expression of transgenes possibly via stimulation of the GCN2 pathway.
Tracing the German centennial flood in the stream of tweets: first lessons learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results confirm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions.
Multi-Rank Adaptive Beamforming with Linear and Quadratic Constraints

Extensions of MVDR and Capon estimation techniques are presented for the situation in which the signal is either rank-one of unknown orientation in a subspace or multi-rank. Only signal-plus-noise snapshots are available. The relationships among linearly and quadratically-constrained approaches are clarified and a unified treatment is given that includes both direct and sidelobe canceller architectures. The unifying component is the multi-rank MVDR beamformer followed by post processing. Detection statistics are presented for the situation in which there is no signal-free training data. Simulations are used to compare rank-one and multi-rank performance.
An approach for classifying large dataset using ensemble classifiers

Efficiency of general classification models in various problems is different according to the characteristics and the space of the problem. Even in a particular issue, it may not be distinguished a special privilege for a classifier method than the others. Ensemble classifier methods aim to combine the results of several classifiers to cover the deficiency of each classifier by others. This combination faces high computational complexity if it includes a lazy base classifier, especially when handling large datasets. In this paper a method is proposed to combine the results of classifiers, which uses clustering as a part of the training, resulting in reducing the computational complexity, while it provides an acceptable accuracy. In this method the base classifiers are trained by a part of the input dataset, first. Then, according to the labels defined by the base classifiers, the clusters are created for another part of dataset. Finally, the samples contained in the clusters, the cluster that each sample belongs to it, and the distance of each sample to the center of all clusters are given to an artificial neural network and the final class label of test data is determined by the neural network. Experiments on several datasets show advantages of proposed model.
SBML-Bridge : An XML Pathway Converting Tool

Many universities and research groups worldwide, working on life sciences, produce nowadays large amounts of biological data through the conduction of biological experiments. In initial research efforts found in literature, biological data have been coded in XML format, which is capable of expressing compound properties and relations between them, trying to simulate large metabolic pathways for “in silico” experiments. However, the existing coding schemas produced by the main biological organizations are different, due to the presence of syntactic, semantic and schematic heterogeneities. In this article we present an approach to “bridge” all these incompatibilities in XML based representation of biological data and thus to make the information easily accessible for further analysis and usage by other scientists. Two case studies of biological data conversion are presented. SBML Systems Biology Markup Language was selected as a target format and CellML Cell Markup Language and KGML KEGG Markup Language as source formats to be converted. The paper presents all the technical details of the proposed approach along with the corresponding findings and the appropriate discussion.
Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity

This study aims to explore differences between health misinformation and true information by comparing word usage, sentiments, and online popularity between pro- and anti-vaccine headlines (PVHs and AVHs). Text mining and sentiment analysis showed that AVHs were more likely to use negative sentiment words and trust-related words. PVHs were more likely to use words related to positive sentiments. Anti-vaccine messages (AVMs) were more popular online than pro-vaccine messages (PVMs). AVMs’ online popularity was not related to its emotion words usage. Among PVMs, those with more positive sentiment words were more likely to be shared, commented on, and reacted to online. Wordclouds and word networks were created to visualize the word usage and clustering. Future directions regarding message design and automatic detection and analysis techniques are provided.
The G-protein-coupled receptor kinases beta ARK1 and beta ARK2 are widely distributed at synapses in rat brain

The beta-adrenergic receptor kinase (beta ARK) phosphorylates the agonist-occupied beta-adrenergic receptor to promote rapid receptor uncoupling from Gs, thereby attenuating adenylyl cyclase activity. Beta ARK-mediated receptor desensitization may reflect a general molecular mechanism operative on many G-protein-coupled receptor systems and, particularly, synaptic neurotransmitter receptors. Two distinct cDNAs encoding beta ARK isozymes were isolated from rat brain and sequenced. The regional and cellular distributions of these two gene products, termed beta ARK1 and beta ARK2, were determined in brain by in situ hybridization and by immunohistochemistry at the light and electron microscopic levels. The beta ARK isozymes were found to be expressed primarily in neurons distributed throughout the CNS. Ultrastructurally, beta ARK1 and beta ARK2 immunoreactivities were present both in association with postsynaptic densities and, presynaptically, with axon terminals. The beta ARK isozymes have a regional and subcellular distribution consistent with a general role in the desensitization of synaptic receptors.
Arbor: A Robust, Reliable, and Platform-Independent Collection Service

We describe the design and implementation of Arbor, a collection tree protocol for wireless sensor networks. We identify a set of six metrics for collection protocols based on their use in the literature. We identify and measure the issues that harm these metrics in practice, and from these observations define the Arbor protocol. Using three different sensor note platforms, two operating systems, and three testbeds, we evaluate Arbor in comparison to existing collection protocols. We find that for low-rate collection applications, Arbor can achieve three nines of reliability, sends few retransmissions, and can quickly adapt to changes in the network topology. Furthermore, as the Arbor protocol is hardware-independent, it is not constrained to a single link layer.