Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Motif-Based Diffusion Models for Social Networks

Existing diffusion models for social networks often assume that the activation of a node depends independently on their parents' activations. Some recent work showed that incorporating the structural and behavioral dependency among the parent nodes allows more accurate diffusion models to be inferred. In this paper, we postulate that the latent temporal activation patterns (or motifs) of nodes of different social roles form the underlying information diffusion mechanisms generating the information cascades observed over a social network. We formulate the inference of the temporal activation motifs and a corresponding motif-based diffusion model under a unified probabilistic framework. A two-level EM algorithm is derived so as to infer the diffusion-specific motifs and the diffusion probabilities simultaneously. We applied the proposed model to several real-world datasets with significant improvement on modelling accuracy. We also illustrate how the inferred motifs can be interpreted as the underlying mechanisms causing the diffusion process to happen in different social networks.
Swarming to rank for recommender systems

Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.
Curiosity driven search: when is relevance irrelevant?

Classical information search behaviour models based on work-task scenarios fail to explain common leisure search scenarios motivated by a hedonistic need rather than a defined information need. This paper presents work into such unstructured search driven by curiosity. In order to explore this hedonistic catalyst, a social media search application was designed in which the search experience is triggered by the user's spatio-temporal context during their exploration rather than query-response based information retrieval. We report a study with real users and a simulated casual-leisure search task where results indicated that relevance is not relevant for some searches.
Datascape : A mobile exploration of georeferenced data

We describe a location-aware system for the visual and auditory representation of georeferenced datasets. The portable system is designed to be deployed in vehicles but also can be used for stationary simulations. The system is composed of four major elements: a geodatabase, a data visualization engine, a sonification engine, and a periscope-like display/sensing/interface device. As users travel through space, they are presented with a moveable window into the surrounding data world. Terrain visualization provides grounding in the physical world, while other non-physical data are experienced as an overlaid environment of sonic and visual structures.
Cash Transfers and Child Labour

Cash transfer programs are widely used in settings where child labour is prevalent. Even if many of these programs are explicitly implemented to improve children's welfare, in theory their impact on child labour is undetermined. This paper systematically reviews the empirical evidence on the impact of cash transfers, conditional and unconditional, on child labour. We find no evidence that cash transfer interventions increase child labour in practice. On the contrary, there is broad evidence that cash transfers, conditional and unconditional, lower both the extensive and intensive margin of child labour. Our findings underline the usefulness of cash transfers as a relatively safe policy instrument to improve child welfare, but also point to knowledge gaps that would need to be addressed in future evaluations to provide detailed policy advice.
The Forecasting Performance of Model Free Implied Volatility : Evidence from an Emerging Market

This paper considers an estimator of the model-free implied volatility (MF-IV) derived by Jiang and Tian (2005) and investigates its information content in index option market in Taiwan. We compare the forecasting performance of MF-IV and other volatility forecasts such as the Black-Scholes implied volatility (BS-IV), historical volatility (HV) and GARCH. The empirical results show that MF-IV outperforms other approaches. The results also reveal that the MF-IV is informational efficient and subsumes all information contained in the HV and GARCH (1,1) in forecasting future realized volatility (RV) on weekly forecast horizon.
The Effect of Attending the Flagship State University on Earnings: A Discontinuity-Based Approach

This paper examines the effect of attending the flagship state university on the earnings of 28 to 33 year olds by combining confidential admissions records from a large state university with earnings data collected through the state's unemployment insurance program. To distinguish the effect of attending the flagship state university from the effects of confounding factors correlated with the university's admission decision or the applicant's enrollment decision, I exploit a large discontinuity in the probability of enrollment at the admission cutoff. The results indicate that attending the most selective state university causes earnings to be approximately 20 higher for white men.
Real-Time Radio Wave Propagation for Mobile Ad-Hoc Network Emulation using GPGPUs

The accurate simulation and emulation of mobile radios requires the computation of RF propagation path loss in order to accurately predict connectivity and signal interference. There are many algorithms available for computing the RF propagation path loss between wireless devices including the LongleyRice model, the transmission line matrix (TLM), ray-tracing, and the parabolic equation method. Each of these methods has advantages and disadvantages but all require a large number of floating point operations during execution. In this paper we investigate using general purpose graphics processing units (GPGPUs) to provide the computational capabilities required to perform these RF path loss calculations in real-time in order to support large scale mobile ad-hoc network emulation. Three specific methods, namely the Longley-Rice, TLM, and ray-tracing methods are explored including usage cases and performance analysis on GPUs. The Longley-Rice algorithm is solved in realtime for 1000’s of transmitters and receivers, the TLM method is well suited for GPU acceleration as is ray-tracing. We will discuss the algorithm modifications required for efficient GPU use, precision issues and optimization.
Fast inter-domain mobility with in-packet bloom filters

We propose a fast inter-domain mobility signaling protocol using in-packet Bloom filters. The intermediate routers collect a bi-directional Bloom filter on the first message, and on subsequent mobility signaling messages. The Bloom filter describes the path from the sender to the receiver and is used to forward the subsequent data packets in the session. In the case of single mobile node, a single message is sufficient to prove authenticity and return routability. For dual mobility scenarios, return routability tests can be delayed until after restarting communications. The protocol also makes bicasting simple, requiring the sender to simply bitwise OR the two Bloom filters describing paths to the old and new locations.
Scalable Parallel Scientific Computing Using Twister 4 Azure

Recent advances in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure and storage services offers a very attractive environment for scientists to perform data analytics. The challenges to large-scale distributed computations demand new frameworks that are specifically tailored for cloud characteristics in order to easily and effectively harness the power of clouds. Twister4Azure is a distributed decentralized iterative MapReduce runtime for Windows Azure Cloud. It extends the familiar, easy-to-use MapReduce programming model with iterative extensions, enabling a wide array of data mining and data analysis applications on the Azure cloud. This paper discusses the applicability of Twister4Azure for scientific computation with highlighted features of fault-tolerance, efficiency and simplicity. We study four data-intensive applications − two iterative scientific applications, Multi-Dimensional Scaling and KMeans Clustering; two data– intensive pleasingly parallel scientific applications, BLAST+ sequence searching and SmithWaterman sequence alignment. Performance measurements show comparable or a factor of 2 to 4 better results than the traditional MapReduce runtimes deployed on up to 256 instances and for jobs with tens of thousands of tasks. We also study and present solutions to several factors that affect the performance of iterative MapReduce appications on Windows Azure Cloud. KeywordsIterative MapReduce, Cloud Computing, HPC, Scientific applications, Azure
Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units

Rectifier neuron units (ReLUs) have been widely used in deep convolutional networks. An ReLU converts negative values to zeros, and does not change positive values, which leads to a high sparsity of neurons. In this work, we first examine the sparsity of the outputs of ReLUs in some popular deep convolutional architectures. And then we use the sparsity property of ReLUs to accelerate the calculation of convolution by skipping calculations of zero-valued neurons. The proposed sparse convolution algorithm achieves some speedup improvements on CPUs compared to the traditional matrix-matrix multiplication algorithm for convolution when the sparsity is not less than 0.9.
Social support, coping, and positive perceptions as potential protective factors for the well-being of mothers of children with intellectual and developmental disabilities

Objectives: Behavioral and emotional problems exhibited by children with intellectual and developmental disabilities (IDD) have been identified as significant stressors for family members in both cross-sectional and longitudinal research. However, there is variability in the extent to which family members are affected by behavioral and emotional problems. In the present study, we explored whether perceived social support, positive perceptions, or coping style explain some of this variability and specifically whether these three variables function as protective factors. Methods: One hundred and thirty-eight mothers of children aged between four and 18 years old with IDD participated in a cross-sectional survey. Results: Using moderated multiple regression models, we found consistent evidence that perceived social support functioned as a protective factor — affecting the relationship between child behavioral and emotional problems and maternal depression, life satisfaction, and positive affect. There was no evidence that coping and positive perceptions acted as a protective factors. Conclusion: Building social support through parental interventions, especially in families of children with behavioral and emotional problems, may result in improved well-being for mothers of children with IDD.
Probabilistic interval-valued computation: toward a practical surrogate for statistics inside CAD tools

Interval methods offer a general, fine-grain strategy for modeling correlated range uncertainties in numerical algorithms. We present a new, improved interval algebra that extends the classical affine form to a more rigorous statistical foundation. Range uncertainties now take the form of confidence intervals. In place of pessimistic interval bounds, we minimize the probability of numerical "escape"; this can tighten interval bounds by 10times, while yielding 10-100times speedups over Monte Carlo. The formulation relies on three critical ideas: liberating the affine model from the assumption of symmetric intervals; a unifying optimization formulation; and a concrete probabilistic model. We refer to these as probabilistic intervals, for brevity. Our goal is to understand where we might use these as a surrogate for expensive, explicit statistical computations. Results from sparse matrices and graph delay algorithms demonstrate the utility of the approach, and the remaining challenges
On unbiased parameter estimation in FIR filtering of signals corrupted by noise

This paper addresses the problem of unbiased estimation of the FIR filter parameters in the face of corrupting noises at the filter input and output sides. The asymptotic analysis reveals that an unbiased parameter estimate is attainable provided that the variance of the input corrupting noise can be estimated. An objective function is properly introduced so that the input noise variance can be easily obtained as a minimum point of this objective function. Consequently this paves the way for achieving an unbiased estimate of the FIR filter parameters. The distinctive feature of the developed algorithm is that the estimation process of the input noise variance is detached from that of the FIR filter parameters and no alternating iterations are required. The performance of the developed unbiased parameter estimation algorithm is substantiated using simulation data.
On the Performance of Multi-Target Passive Sonar Tracking in Clutter

The problem of tracking multiple targets with passive bearings-only measurements is extremely challenging and rarely addressed explicitly in the literature. The difficulty arises mainly due to the low information content and non-linearity of the received measurements. Furthermore, effects such as high clutter density, closely spaced targets, and crossing targets are commonplace in passive tracking scenarios, adding to the difficulty of maintaining continuous and accurate target tracks. While many algorithms for multi-target tracking in clutter have been proposed, results on their application to passive tracking are scarce. The aim of this paper is to review a selection of state-of-the-art multi-target tracking techniques, apply them to the problem of bearings-only multi-target tracking in clutter, and evaluate and compare their performance under challenging scenarios. The types of algorithms addressed here include Sequential Monte Carlo (SMC) methods and Markov Chain Monte Carlo (MCMC) methods, as well as the more traditional Joint Integrated Probabilistic Data Association (JIPDA), and Multiple Hypothesis Tracking (MHT).
The Hubble series: convergence properties and redshift variables

In cosmography, cosmokinetics and cosmology, it is quite common to encounter physical quantities expanded as a Taylor series in the cosmological redshift z. Perhaps the most well-known exemplar of this phenomenon is the Hubble relation between distance and redshift. However, we now have considerable high-z data available; for instance, we have supernova data at least back to redshift z ≈ 1.75. This opens up the theoretical question as to whether or not the Hubble series (or more generally any series expansion based on the z-redshift) actually converges for large redshift. Based on a combination of mathematical and physical reasonings, we argue that the radius of convergence of any series expansion in z is less than or equal to 1, and that z-based expansions must break down for z > 1, corresponding to a universe less than half of its current size. Furthermore, we shall argue on theoretical grounds for the utility of an improved parametrization y = z/(1 + z). In terms of the y-redshift, we again argue that the radius of convergence of any series expansion in y is less than or equal to 1, so that y-based expansions are likely to be good all the way back to the big bang (y = 1), but that y-based expansions must break down for y < −1, now corresponding to a universe more than twice its current size.
A Fault Detection and Diagnosis Framework for Ambient Intelligent Systems

Ambient intelligence (AmI) systems are smart interactive systems that perceive their surroundings using sensors and act upon them using actuators. One of the most common applications of such systems is Smart Homes. In this context, the ambient system can offer a great level of dependability if it is able to exploit available sensor data in order to autonomously perform diagnosis. However, ambient environments are dynamic in a sense that components, in general, and actuators and sensors, in particular, can be added or removed from the system at run-time. This dynamicity raises new challenges not addressed in the state of the art of fault detection and diagnosis techniques. Unlike classical control theory methods, control-loops between ambient system components cannot be pre-determined at design time. In this paper we propose a new approach based on the modeling of physical phenomena, allowing one to use available resources to predict the values that are supposed to be read by sensors. Comparing the predictions and the real readings allows us to detect potential faults. Fault detection may be followed by fault isolation, which tries to identify the faulty component precisely.
Form Factors of Dimesoatoms for Discrete-Continuum Transitions

An approach for calculation of transition form factors of hydrogen-like elementary atoms (EA) is proposed. A general formula for bound-continuous transition form factors of EA is derived. It is shown that these form factors can be represented in the form of finite sum of terms with simple analytical structure and may be numerically evaluated with arbitrary degree of accuracy. As an application of the presented form factors, the calculation of the spectra of products from EA ionization is considered.
IBAW: an implication-tree based alternative-wiring logic transformation algorithm

The well-known ATPG-based alternative wiring technique, RAMBO, has been shown to be very useful because of its proven powerfulness and flexibility in attacking many design automation problems (e.g. logic optimization, circuit partitioning, and post-layout logic transformation, etc.). Since the ATPG based alternative wire locating procedure is the center engine for all its applications, speeding up of this process should be very crucial and useful, We observe that the bottleneck of the technique lies in the costly redundancy tests among a large number of candidate alternative wires. In this paper, we develop a so-called implication-tree data structure which stores the implication relationship between nodes with determined logic values, and propose a new ATPG-based alternative-wiring algorithm to speed up the engine. The algorithm, Implication-tree Based Alternative-Wiring (IBAW), differs from other ATPG-based algorithms in terms that it selects the source node of alternative wires from the implication-tree, which makes IBAW able to trim out many unnecessary redundancy checking quite easily without calling for complicated procedures. Hence, it produces a steady speeding up of around 3.6 times faster while maintaining the same rewiring capability of the original RAMBO. Our experimental results show that the overall circuit area optimized by IBAW can be slightly better than that by RAMBO, while the runtime is just one-half of the latter.
O R I G I N a L I N V E S T I G a T I O N Open Access

Association between serum adipocyte fatty-acid binding protein concentrations, left ventricular function and myocardial perfusion abnormalities in patients with coronary artery disease Abstract Background: Adipokines, including adipocyte fatty acid-binding protein (A-FABP), have been demonstrated to be involved in the pathogenesis of atherosclerosis. In the present study, we investigated the association of circulating A-FABP level with severity of myocardial perfusion abnormalities analyzed by Tl-201 dipyridamole single-photon emission computed tomography. Methods: A total of 170 patients with coronary artery disease (CAD) from cardiovascular clinics were enrolled in the study. Serum A-FABP levels, echocardiography, and stress myocardial perfusion imaging results were analyzed.
Success In Spite of Ourselves: Violation of Sabbath-Rest in Contemporary Culture

In today’s culture, many people believe success is linked to good behavior. Likewise, the Pharisees viewed righteousness as the path to success, and they regarded rule-keeping, including rules of the Sabbath, as the outward manifestation of righteousness. We postulate their strict adherence to rules may have, in fact, precluded them from experiencing the true blessings God associated with keeping the Sabbath holy. We explore the intended meaning of Sabbath-keeping and its benefits while discussing opposing views of Sabbath violation in contemporary culture. We highlight the consequences of failing to uphold the Sabbath and suggest that any success encountered in doing so is likely temporary and in spite of our behavior rather than due to it.
Making Replication Simple with Ursa

Our goal is to qualitatively simplify developing large scale data replication systems and improving existing ones. To realize this goal, we address a fundamental question: What are the right abstractions for defining replication systems? Our new architecture, Ursa, defines (1) mechanisms that define abstractions for storage, communication, and consistency that automatically handle the bookkeeping needed to allow policies to distribute data however they want, (2) a model of policy that cleanly separates safety and liveness concerns, (3) a way to define safety policies leveraging standard consistency algorithms, and (4) a way to define liveness policies using a declarative language. By capturing the right abstractions, Ursa dramatically reduces the effort needed to construct a new system, and it facilitates innovation by making it easy to evolve existing systems. For example, we build systems that closely approximate Coda, Bayou, Pangaea, Chain Replication, TRIP, and TierStore using fewer than 200 lines of system-specific policy code for each, and we add significant new features like cooperative caching to Coda and TierStore, small-device support for Bayou, or hierarchy to TRIP using fewer than 10 additional lines.
Effective Storm-Relative Helicity and Bulk Shear in Supercell Thunderstorm Environments

Abstract A sample of 1185 Rapid Update Cycle (RUC) model analysis (0 h) proximity soundings, within 40 km and 30 min of radar-identified discrete storms, was categorized by several storm types: significantly tornadic supercells (F2 or greater damage), weakly tornadic supercells (F0–F1 damage), nontornadic supercells, elevated right-moving supercells, storms with marginal supercell characteristics, and nonsupercells. These proximity soundings served as the basis for calculations of storm-relative helicity and bulk shear intended to apply across a broad spectrum of thunderstorm types. An effective storm inflow layer was defined in terms of minimum constraints on lifted parcel CAPE and convective inhibition (CIN). Sixteen CAPE and CIN constraint combinations were examined, and the smallest CAPE (25 and 100 J kg−1) and largest CIN (−250 J kg−1) constraints provided the greatest probability of detecting an effective inflow layer within an 835-supercell subset of the proximity soundings. Effective storm-relativ.