Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Computation of Reverse PageRank Problem with Evaluating Single Page

PageRank is a core component in search engine. In a wide range of PageRank applications, only few pages often need to be evaluated in some scenarios. In this paper, we propose Markov chain Monte Carlo (MCMC) method for solving reverse PageRank problem. Different from the previous MCMC method that solves the problem via simulating surfing the internet forward, we adopt MCMC method to solve PageRank problem via simulating surfing the internet backward, called reverse PageRank. For a selected page, the MCMC method can be used to simulate the network to find all the possible paths from any other page to the selected page. The major advantage of the proposed method is that only one page is evaluated, instead of computing the whole pages. Following this method, we can independently evaluate the importance of the selected page. As MCMC method has parallelism and robustness in nature, it is suitable to be implemented and optimized on GPU. We have simulated the real world networks to evaluate the performance of the method. The results demonstrate that our MCMC method can be implemented efficiently on GPU and outperforms the other existing methods when few pages are evaluated. We then discuss and analyze the important properties of the MCMC method for reverse PageRank problem based on the experiment results.
Localization in UWB Sensor Networks for Agriculture Environment

Information technologies are becoming more and more important for agricultural development. In the intelligent monitoring and control of agriculture environments, localization has many attractive applications. On the basis of IEEE 802.15. 4a, we adopt the farm channel model CM7 to position in ultra- wideband (UWB) sensor networks for agriculture environments. Maximum likelihood estimation (MLE) based UWB RSSI ranging is proposed, the relation between localization error and ranging error is analyzed. Then, we joint impulse radio UWB, RSSI ranging and localization algorithm, passive UWB RFID Tags to provide a novel UWB sensor localization network in agriculture environments. Simulation results indicate that our ranging method, localization algorithm and network model achieve a significant improvement in the performance of ranging and localization for applications in precision agriculture.
Planning by Prioritized Sweeping with Small Backups

Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the successor states. Consequently, its computation time is proportional to the number of successor states. In this paper, we introduce a new planning backup that uses only the current value of a single successor state and has a computation time independent of the number of successor states. This new backup, which we call a small backup, opens the door to a new class of model-based reinforcement learning methods that exhibit much finer control over their planning process than traditional methods. We empirically demonstrate that this increased flexibility allows for more efficient planning by showing that an implementation of prioritized sweeping based on small backups achieves a substantial performance improvement over classical implementations.
The NAO Troposphere-Stratosphere Connection.

Using monthly mean data, daily data, and theoretical arguments, relationships between surface pressure variations associated with the North Atlantic Oscillation (NAO), tropopause height, and the strength of the stratospheric vortex are established. An increase in the NAO index leads to a stronger stratospheric vortex, about 4 days later, as a result of increased equatorward refraction of upward-propagating Rossby waves. At tropopause level the effects of the enhanced NAO index and stratospheric polar vortex are opposite, resulting in a lower tropopause over Iceland and a higher tropopause over the Arctic. The raising of the Arctic tropopause leads to a stretching and spinup of the tropospheric column and is therefore associated with a lowering of the surface pressure near the North Pole. For monthly mean data it is found that a standard deviation increase in the NAO index is associated with a 10% increase in the strength of the stratospheric vortex, as measured by potential vorticity at 500 K. A simple theoretical model predicts that this is associated with about 300-m elevation of the Arctic tropopause, as is observed, and a 5-hPa lowering of the surface pressure at the North Pole. The effects of the spinup of the tropospheric column may project on the NAO pattern so that the stratosphere acts as an integrator of the NAO index.
Viewpoint: On the Generalizability of Lab Behaviour to the Field

to the class of games in which financial payoffs and `doing the right thing' are not necessarily in conflict. We argue that behaviour is crucially linked to not only the preferences of people, but also the properties of the situation. By doing so, we are able to provide a road map of the psychological and economic properties of people and situations that might interfere with generalizability of laboratory result from a broad class of games.
Ecosense: A Context and Semantics Driven Framework for Eco-Aware Ambient Environments

Global climate change can be mitigated with innovative solutions in the ambient intelligence domain that utilize distributed and heterogeneous sensor networks for high resolution sensing of resource utilization (e.g. energy, water, etc.) via intelligent coordination among sensor nodes. We propose the EcoSense framework as a holistic solution that enables dynamic control and reconfiguration of the sensor nodes in an ambient environment based on our resource consumption patterns. EcoSense introduces service and context layers to existing sensor network stacks to enable interoperability between heterogeneous sensor node platforms. A prototype of the framework for an eco-aware smart space has been developed as a pervasive infrastructure for monitoring and minimizing resource consumption at individual levels and also for building eco-aware homes and works paces.
For Prisoners, “Work Works”: Qualitative Findings From an Israeli Program

The study’s purpose was to assess the contribution of the Work Release Program of the Israel Prison Service (IPS) to the processes of prisoners’ rehabilitation. We interviewed 22 serving and released prisoners who participated in the program at two prisons in central Israel. Most respondents were positive toward the core components of the program, mainly the work outside the prison. Responses also indicated that the program eased their transition from prison life to life outside. However, some respondents criticized the way in which the program was operated. The article discusses the program’s strengths and weaknesses, and proposes some improvements.
Connectionist Learning of Natural Language Lexical Phonotactics ( Running head : Connectionist Learning of Phonotactics )

Connectionist learning of natural language words and their phonetic regularities is presented. The Neural Network (NN) model we employ in this problem is the Simple Recurrent Network, trained with the Backpropagation Through Time (BPTT) learning algorithm. During the training, it was assigned the task of predicting the next phoneme given one phoneme at each moment and keeping information of the past phonemes from a given word in a few context neurons. The phonotactics of the Dutch language was studied among others. The shortcomings of some similar previous implementations are explained and successfully overcome. Among the techniques we employed to achieve the much-improved error rate of 1.1% with monosyllabic words and 3.5% with multisyllabic ones are new methods for network response interpretation, an evolutionary approach in training a set of networks, and the exploitation of the word frequencies in training. Finally, an analysis of the phonotactics rules extracted by a trained network is presented.
TOWARDS A KNOWLEDGE-BASED FREE-TEXT RESPONSE ASSESSMENT SYSTEM

In the present work, the architecture of a knowledge-based free-text response assessment system is presented, which can assess free-text responses on open-ended questions based on text comprehension theories. Its main advantages are the facts of extending the knowledge base and assessing responses on questions of different types. It is constituted from three basic modules: The first module is the Normalization Module (NoM), which is responsible for converting free-text responses into normalized responses, as well as, converting technical text into “functional system”. The second module is the Functional System Module (FSM), which depicts all entities of a technical text and the relations among them, and is presented, by the representation of microstructure and macrostructure of a Computer Networks domain technical text, according to Denhiere-Baudet Text Comprehension Model. The third module is the Assessment Module (AM), responsible for assessing the normalized responses. It is based on data, obtained by experimental studies on microstructure and macrostructure, constructed by secondary school students, during reading technical texts and responding to questions.
Outcome of Alcohol Septal Ablation for Obstructive Hypertrophic Cardiomyopathy

Background— The clinical efficacy of alcohol septal ablation for drug-refractory hypertrophic cardiomyopathy remains unclear. This study examines the outcome of alcohol septal ablation performed at a tertiary hypertrophic cardiomyopathy referral center. Methods and Results— Among 601 patients with severely symptomatic obstructive hypertrophic cardiomyopathy referred for alcohol septal ablation or myectomy from 1998 to 2006, 138 patients (median age, 64 years; 39% men) chose to undergo ablation. Procedural complications included death in 1.4%, sustained ventricular arrhythmias in 3%, tamponade in 3%, and pacemaker implantation in 20%. This rate was higher than a combined complication rate of 5% in age- and gender-matched patients who had undergone septal myectomy at Mayo Clinic (P<0.0001). Four-year survival free of all mortality was 88.0% (95% confidence interval, 79.4 to 97.5%), which was similar to that of the age- and gender-matched patients who had undergone myectomy (P=0.18). Six patients had documented ventricular arrhythmias after ablation, 4 of whom had successful intervention. Four-year survival free of death and severe New York Heart Association class III/IV symptoms after septal ablation was 76.4%, and 71 patients (51%) became asymptomatic. Myectomy patients ≤65 years of age had significantly better survival free of death and severe symptoms (P=0.01). Conclusions— Alcohol septal ablation is an efficacious procedure if performed in an experienced institution and may resolve symptoms in a subset of patients with obstructive hypertrophic cardiomyopathy. However, the procedural complication rate exceeds that of myectomy. Patients ≤65 years of age have better symptom resolution with myectomy. No impairment in short-term survival was noted in this nonrandomized study, but the long-term outcome remains unknown.
Relational Modeling in Social Media

Relational Modeling has been gaining traction towards the support of collective intelligence. Here, we explore the performance of Relational Classification as compared to both supervised and unsupervised Machine Learning methodologies. These techniques are applied to Social Media-based data sources with the goal of understanding consumer behavior. We demonstrate that Relational Modeling can provide comparative performance to Machine Learning methods and demonstrate even higher accuracy when an expanded social network is leveraged towards the learning process.
Matrices, Vector Spaces, and Information Retrieval

The evolution of digital libraries and the Internet has dramatically transformed the processing, storage, and retrieval of information. Efforts to digitize text, images, video, and audio now consume a substantial portion of both academic and industrial activity. Even when there is no shortage of textual materials on a particular topic, procedures for indexing or extracting the knowledge or conceptual information contained in them can be lacking. Recently developed information retrieval technologies are based on the concept of a vector space. Data are modeled as a matrix, and a user's query of the database is represented as a vector. Relevant documents in the database are then identified via simple vector operations. Orthogonal factorizations of the matrix provide mechanisms for handling uncertainty in the database itself. The purpose of this paper is to show how such fundamental mathematical concepts from linear algebra can be used to manage and index large text collections.
Nonlinear Processes in Geophysics Chaotic properties of a fully developed model turbulence

Shell models of turbulence have been employed as toy models which, in their chaotic states, show statistical properties similar to real fluid turbulence, including Kolmogorov energy spectrum and intermittency. These models are interesting because, at the present stage, it is still quite difficult or almost impossible to study relations between those traditional statistical properties and the structure of the chaos underlying the real fluid turbulence because of huge dimension of the chaotic attractor. In this paper we will give a brief review on the chaotic properties of a shell model (GOY model), with emphasis on its Lyapunov spectrum and unstable periodic orbits, in relation to the Kolmogorov scaling law of the turbulence.
Extracellular Hb Enhances Cardiac Toxicity in Endotoxemic Guinea Pigs: Protective Role of Haptoglobin

Endotoxemia plays a major causative role in the myocardial injury and dysfunction associated with sepsis. Extracellular hemoglobin (Hb) has been shown to enhance the pathophysiology of endotoxemia. In the present study, we examined the myocardial pathophysiology in guinea pigs infused with lipopolysaccharide (LPS), a Gram-negative bacterial endotoxin, and purified Hb. We also examined whether the administration of the Hb scavenger haptoglobin (Hp) could protect against the effects observed. Here, we show that Hb infusion following LPS administration, but not either insult alone, increased myocardial iron deposition, heme oxygenase-1 expression, phagocyte activation and infiltration, as well as oxidative DNA damage and apoptosis assessed by 8-hydroxy-2'-deoxyguanosine (8-OHdG) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) immunostaining, respectively. Co-administration of Hp significantly attenuated the myocardial events induced by the combination of LPS and Hb. These findings may have relevant therapeutic implications for the management of sepsis during concomitant disease or clinical interventions associated with the increased co-exposures to LPS and Hb, such as trauma, surgery or massive blood transfusions.
Testing antidepressant compounds in a neuropsychological model of drug action

Although much research effort has been put into the development of new antidepressant drugs, the process of developing a drug often fails at the stage of large randomized controlled trials (RCTs) in which an initially promising compound appears to lack efficacy after all. Several experimental medicine models have been proposed as preclinical tools in order to predict drug efficacy before the stage large RCTs. Among the various experimental medicine models, the cognitive neuropsychological model has been proposed as a tool to predict the efficacy of antidepressant drug even before the stage of large scale and expensive RCTs. We applied the cognitive neuropsychological model of drug action to test antidepressant effects of a novel compound (ARA290) and a well-known compound (L-tryptophan). We further investigated the model by tapping into HPA-axis reactivity and social decision making as additional outcomes, and investigated their interaction with a genetic marker.
Commentary on Mari Romarheim Haugen: Investigating Periodic Body Motions as a Tacit Reference Structure in Norwegian Telespringar Performance

This study investigates periodic body motion (foot-tapping and vertical hip motions) as a reference structure in a specific style of traditional Norwegian dance music called telespringar . The intimate relationship between music and motion is often highlighted in rhythm studies of telespringar , so this study encompasses both sound and motion analyses. It is based on a motion capture study of three telespringar performers, examining the foot-stamping of one fiddler and vertical hip motions of two dancers. The results indicate that rather than being primarily derived from the sound itself, the underlying rhythmic structures in telespringar depend upon a shared and embodied knowledge of the underlying asymmetrical reference structure that is implicit in the production and perception of telespringar . I explore here how the methodology used by the author might be further justified and explored. I will also propose ways in which the article could be made more accessible to researchers who question the universality of research outcomes arrived at using laboratory-based experimental settings.
Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features

Current railroad wayside Hot Bearing Detector systems were developed in the 1960s to identify failing friction bearings. While the electronics used in these systems have been upgraded to microprocessor technology, the basic detection principles have not changed over the last 30 years. In this paper, we present a novel method to detect, recognize, and classify a variety of railroad wheel-bearing defects using audible acoustic signals at several different train speeds. Our algorithm consists of a data preprocessor, a feature extractor, and a single multilayer neural network. The feature extractor can use any one of four different transforms to generate feature vectors from input acoustic data: the fast Fourier transform (FFT), the continuous wavelet transform, the discrete wavelet transform, and the wavelet packet. The classification performance using each feature vector type is presented. This algorithm can be applied to many kinds of bearings in rotational machinery to perform nondestructive fault detection and identification.
First-principles study of strain-induced modulation of energy gaps of graphene/BN and BN bilayers

First-principles calculations based on density functional theory are performed on graphene/BN and BN bilayers to investigate the effect of the strain on their energy gaps. For the graphene/BN bilayer, the bands have characteristic graphenelike features with a small band gap at K. Application of strain modulates the band gap, whose magnitude depends on the strength of interaction between constituent monolayers. For the BN bilayer, on the other hand, a large band gap is predicted, which remains nearly the same for small strains. The increased inhomogeneity in charge density of different carbon sublattices due to a stronger interplanar interaction is the cause of the predicted variation in the band gap with strains applied along the perpendicular direction in the graphene/BN bilayer.
Adaptive Wavelet Shrinkage For Image Denoising Based On SURE Rule

Based on the wavelet shrinkage denoising theory proposed by D.L. Donoho, a new thresholding function is presented in this paper, which is rather similar to hard thresholding one. However it has infinite-order continuous derivative. Compared to soft thresholding function, it can reserve better image details due to its "hard" characteristic. Moreover, the new one makes it possible to construct an adaptive algorithm for image denoising. By using the new thresholding function, a new adaptive shrinkage method is presented based on Stein's unbiased risk estimate (SURE). The two examples Lenna and Barbara are given. The results indicted that for image denoising application, the proposed method is very effective in adaptively finding the optimal solution in the least mean square error (LMSE) sense
Latent Dirichlet Allocation and Singular Value Decomposition Based Multi-document Summarization

Multi-Document Summarization deals with computing a summary for a set of related articles such that they give the user a general view about the events. One of the objectives is that the sentences should cover the different events in the documents with the information covered in as few sentences as possible. Latent Dirichlet Allocation can breakdown these documents into different topics or events. However to reduce the common information content the sentences of the summary need to be orthogonal to each other since orthogonal vectors have the lowest possible similarity and correlation between them. Singular Value Decompositions used to get the orthogonal representations of vectors and representing sentences as vectors, we can get the sentences that are orthogonal to each other in the LDA mixture model weighted term domain. Thus using LDA we find the different topics in the documents and using SVD we find the sentences that best represent these topics. Finally we present the evaluation of the algorithms on the DUC2002 Corpus multi-document summarization tasks using the ROUGE evaluator to evaluate the summaries. Compared to DUC 2002 winners, our algorithms gave significantly better ROUGE-1 recall measures.
Stabilization of atmospheric carbon dioxide via zero emissions—An alternative way to a stable global environment. Part 1: Examination of the traditional stabilization concept

The concept of “stabilization” of atmospheric CO2 concentration is re-examined in connection with climate-change mitigation strategies. A new “zero-emissions stabilization (Z-stabilization)” is proposed, where CO2 emissions are reduced to zero at some time and thereafter the concentration is decreased by natural removal processes, eventually reaching an equilibrated stable state. Simplified climate experiments show that, under Z-stabilization, considerably larger emissions are permissible in the near future compared with traditional stabilization, with the same constraint on temperature rise. Over longer time scales, the concentration and temperature decrease close to their equilibrium values, much lower than those under traditional stabilization. The smaller temperature rise at final state is essential to avoid longer-term risk of sea level rise, a significant concern under traditional stabilization. Because of these advantages a Z-stabilization pathway can be a candidate of practical mitigation strategies as treated in Part 2.
Advanced diagnostics and anomaly detection for railroad safety applications: Using a wireless, IoT-enabled measurement system

Accidents involving trains have been attributed to degraded track and rolling stock. Detection of anomalies that indicate degraded condition is critical. In this paper we present results of an experiment using a sensor system mounted on one of the 110 boxcars on a train on a high-tonnage loop test track. The sensor was a microelectromechanical systems (MEMS) triaxial accelerometer module mounted on the hubs of the wheels of the boxcar. Sensor data were wirelessly transmitted to a collection gateway hub mounted inside the boxcar. The purpose of the experiment was to evaluate the feasibility of using a rotating triaxial accelerometer-based system designed to be mounted inside of a helicopter gearbox, and to use the system to detect anomalies in railroad tracks and rolling stock as well as anomalies of bearings, rotating shafts and gears. The results confirm it is feasible to identify, locate, and characterize such anomalies.