Miracle-dz/UniFAR-FTUs huggingface dataset:
Traditional and IS-Enabled Customer Acquisition on the Internet

Geographic variation in consumer use of Internet retailers is partly explained by variation in offline shopping costs. Explanations for geographic variation in the efficacy of different customer acquisition methods including traditional methods of offline word-of-mouth (WOM) and magazine advertising and information systems (IS)-enabled methods of online WOM and online search remain unexplored. We estimate a multivariate negative binomial distribution (NBD) model on zip code--level customer counts from a leading Internet retailer and provide new insights into factors explaining geographic variation in the success of these methods. First, we show that target customer density explains geographic variation over and above the impact due to the number of potential customers. Moreover, the effect of density is greatest for offline and online WOM acquisitions; this suggests that density contributes to contagion, connectivity, and a hypothesized “social multiplier. ” Second, when senders and recipients of WOM share consumption benefits, WOM is more powerful and compelling. We find that location-based convenience benefits have stronger effects on location-dependent offline WOM acquisitions than on location-independent online WOM acquisitions. Third, acquisition channels contribute differently to the total customer pool---offline WOM acquisitions are clustered, whereas magazine acquisitions are dispersed. Finally, separate click-to-conversion data from Coremetrics.com indicates that using the model-based predictions to target specific markets delivers a twofold improvement in actual click-to-order rates. This paper was accepted by Sandra Slaughter, information systems.
Effects of Early Thyroid Hormone Deficiency on Autobiographical Memory and Hippocampal Structure and Function during Late Childhood and Early Adolescence

The hippocampus, which is a critical brain region for episodic autobiographical memory (AM), is particularly vulnerable to damage following periods of early thyroid hormone (TH) deficiency. Although numerous studies have examined AM performance in adult patients with hippocampal damage, no study has yet examined AM in children exposed to early TH deficiency, such as children with congenital hypothyroidism (CH) and offspring of women who were hypothyroid during pregnancy (HYPO). Given that both animal and human studies have shown that early TH deficiency results in significant hippocampal abnormalities and memory impairments, the purpose of this dissertation was to investigate the effects of early TH deficiency on AM and hippocampal structure and function during childhood. Study I examined AM performance in a large sample of typically developing children and adolescents in order to validate the use of the newly-developed Children‟s Autobiographical Interview (CAI). In Study II, the CAI was used to investigate AM performance in children with early TH deficiency (i.e., CH and HYPO groups). Similar to the findings observed in adults with hippocampal damage, CH and HYPO groups both exhibited weaknesses in episodic AM, but not semantic AM, relative to controls. In addition, structural MRI revealed mild bilateral
semMatch: road semantics-based accurate map matching for challenging positioning data

Map matching has been used to reduce the noisiness of the location estimates by aligning them to the road network on a digital map. A growing number of applications, e.g. energy-efficient localization and cellular provider side localization, depend on the availability of only sparse and coarse-grained positioning data; leading to a challenging map matching process. In this paper, we present semMatch: a system that can provide accurate HMM-based map matching for challenging positioning traces. semMatch leverages the smartphone's inertial sensors to detect different road semantics; such as speed bumps, tunnels, and turns; and uses them in a mathematically-principled way as hints to overcome the sparse, noisy, and coarse-grained input positioning data, improving the HMM map matching accuracy and efficiency. To do that, semMatch applies a series of preprocessing modules to handle the noisy locations. The filtered location data is then processed by the core of semMatch system using a novel incremental HMM algorithm that combines a semantics-enriched digital map and the car's ambient road semantics in its estimation process. We have evaluated semMatch using traces collected from different cities covering more than 150km under different harsh scenarios including coarse-grained cellular-based positioning data, sparse GPS traces with extremely low sampling rate, and noisy traces with a large number of back-and-force transitions. The results show that semMatch significantly outperforms traditional map matching algorithms under all scenarios, with an enhancement of at least 416% and 894% in precision and recall respectively in the most difficult cases. This highlights its promise as a next generation map matching algorithm for challenging environments.
Estimation of Branch Angle from 3D Point Cloud of Plants

Measuring geometric features in plant specimens either quantitatively or qualitatively, is crucial for plant phenotyping. However, traditional measurement methods tend to be manual and can be tedious, or employ coarse 2D imaging techniques. Emerging 3D imaging technologies show much promise in capturing architectural complexity. However, automated 3D acquisition and accurate estimation of plant morphology for the construction of quantitative plant models remain largely aspiration. In this paper, we propose an approach for segmentation and angle estimation directly from dense 3D plant point clouds. Experimental results show that the approach is efficient and reliable, and appears to be a promising 3D acquisition and measurement solution to plant phenotyping for structural analysis and for building Functional-Structural Plant Models (FSPM).
Gatling: Automatic Attack Discovery in Large-Scale Distributed Systems

In this paper, we propose Gatling, a framework that au- tomatically finds performance attacks caused by insider at- tackers in large-scale message-passing distributed systems. In performance attacks, malicious nodes deviate from the protocol when sending or creating messages, with the goal of degrading system performance. We identify a represen- tative set of basic malicious message delivery and lying ac- tions and design a greedy search algorithm that finds effec- tive attacks consisting of a subset of these actions. While lying malicious actions are protocol dependent, requiring the format and meaning of messages, Gatling captures them without needing to modify the target system, by using a type- aware compiler. We have implemented and used Gatling on six systems, a virtual coordinate system, a distributed hash table lookup service and application, two multicast systems and one file sharing application, and found a total of 41 attacks, ranging from a few minutes to a few hours to find each attack.
Clutter estimation based on compressed sensing in bistatic MIMO radar

In this paper, the clutter estimation problem in the bistatic multiple-input and multiple-output (MIMO) radar system is considered, and a novel compressed sensing (CS)-based model is proposed to describe the clutter by exploiting the clutter sparsity in the angle domain. Then, the CS-based methods are adopted to reconstruct the sparse clutter, and to estimate the clutter scattering coefficients and angles. Additionally, different from the traditionally colocated MIMO radar system with the antenna distance being half of wavelength, we show that the optimal antenna distance in the CS-based radar system can be obtained by minimizing the mutual coherence of the dictionary matrix. Moreover, since the sparse reconstruction performance depends on the geographical positions of the clutter scatterers, an indirect method based on the mutual coherence is proposed to measure the estimation performance, and to optimize the radar parameters. Simulation results show that the CS-based method can estimate the clutter information efficiently, and the better estimation performance is achieved by optimizing the radar parameters.
A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, <inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>-measure, and <inline-formula> <tex-math notation="LaTeX">${G}$ </tex-math></inline-formula>-mean.
A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction

Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken both classification ability and cost-sensitive into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between attributes' information and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform than the existing methods, such as, information gain method, total costs methods, in terms of the decrease of misclassification costs with different missing rate and various costs in UCI datasets.
Effect of Intraoperative High-Dose Remifentanil on Postoperative Pain: A Prospective, Double Blind, Randomized Clinical Trial

Background Remifentanil, an ultra-short-acting opioid, is widely used for pain control during surgery. However, regular dose (RD) remifentanil exacerbates postoperative pain in a dose-dependent manner. Recent studies suggest that high-dose (HD) remifentanil offers sustained analgesia in experimental studies. We thus hypothesized that intraoperative administration of high-dose remifentanil may attenuate postoperative pain. Methods In this prospective, randomized, double blind, controlled clinical study, sixty patients undergoing thyroidectomy (18–60 years-of-age) received an intraoperative infusion of 0.2 (RD group) or 1.2 μg kg−1min−1 (HD group) remifentanil during thyroidectomy. A visual analogue scale (VAS) was used to measure pain intensity. Mechanical pain threshold on the forearm was assessed using von Frey filaments before surgery (baseline), 2 h postoperatively and 18–24 h postoperatively. The primary outcome was to compare the difference of VAS score at different time points after operation and morphine consumption 24 h postoperatively between RD and HD groups. The second outcome was to compare the difference of mechanical pain thresholds in the forearm postoperatively between RD and the HD groups. Results VAS scores were lower 30 min postoperatively in the HD group (1.29±1.67, 95% CI 0.64–1.94) compared with the RD group (2.21±1.67, 95% CI 1.57–2.84) (t = 3.427, p = 0.0043, RD group vs. HD group). Postoperative morphine consumption was much lower in the HD group compared with the RD group (1.27±1.88 mg vs. 0.35±1.25 mg, p = 0.033). In both groups, mechanical pain threshold was decreased 18–24 h postoperatively (2.93±0.209 Ln(g) vs. 3.454±2.072 Ln(g), p = 0.032 in RD group; 2.910±0.196 Ln(g) vs. 3.621±0.198 Ln(g), p = 0.006 in HD group, 18–24 h postoperatively vs baseline). Conclusions Intraoperative administration of high-dose remifentanil decreased VAS scores and morphine consumption postoperatively. Thus, modulation of intraoperative opiates may be a simple and effective method of postoperative pain management. Trial Registration This trial is registered in ClinicalTrials.gov, with the Name: Effect of Higher Doses of Remifentanil on Postoperative Pain in Patients Undergoing Thyroidectomy, and ID number: NCT01761149.
LAPACK++: A design overview of object-oriented extensions for high performance linear algebra

LAPACK++ is an object-oriented C++ extension of the LAPACK (Linear Algebra PACKage) library for solving the common problems of numerical linear algebra: linear systems, linear least squares, and eigenvalue problems on high-performance computer architectures. The advantages of an object-oriented approach include the ability to encapsulate various matrix representations, hide their implementation details, reduce the number of subroutines, simplify their calling sequences, and provide an extendible software framework that can incorporate future extensions of LAPACK, such as ScaLAPACK++ for distributed memory architectures. The authors present an overview of the object-oriented design of the matrix and decomposition classes in C++ and discuss its impact on elegance, generality, and performance.
Quality of Diabetes Care in Primary Health Centres in North Al-Batinah of Oman

Objective: To assess the quality of diabetic care provided in primary health care settings in Oman. Methods: This was a cross-sectional study of randomly selected 500 patients with diabetes mellitus (DM) attending 6 primary care diabetic clinics in the north Al-Batinah region of Oman from January to December 2010. Nine standards on the quality of diabetes care were audited. Results: The mean age of the sample was 51±13 years, ranging from 15 to 87 years; the majority (61%) were females. The mean duration of DM was 4±3 years, ranging from 1 to 18 years. Seventy-seven percent of the patients attended diabetic clinics at least 4 times per year. Of the 9 assessed diabetic standards, HbA1c was documented in 33% of the patients, body mass index in 12%, low-density lipoprotein cholesterol (LDL-C) in 40%, urinary albumin:creatinine ratio in 28%, creatinine in 63% and blood pressure (BP) in 96%. Optimal control among the documented indicators was noted in 32, 21, 25, 85, 95 and 19%, respectively. Twenty percent of the patients had their ECGs done while only 39% of the patients had foot examination. No patient had attained control in all of HbA1c. , BP and LDL-C. Conclusion: There is a gap between the recommended DM care guidelines and current practice with consequent poor quality of care in these patients.
Stages of change towards healthy eating in Portuguese adults

The stages of change towards healthy eating were assessed by a question adapted from Kearney et al. (1999). Physical activity was evaluated by the International Physical Activity Questionnaire (IPAQ; Craig et al. 2003). BMI categories were used according to WHO (1998). The distribution of subjects by stages of change towards healthy eating was compared according to sex, age group, education level, civil status, professional occupation, region of residence, BMI category, physical activity level and smoking habits.
Improvement of syllable based TTS system in assamese using prosody modification

A Unit Selection based Synthesis (USS) system selects appropriate units from the inventory based on target cost and join cost. For a syllable based USS system, it is not practically feasible to cover all possible syllables in a language with all possible contexts. Hence, some discontinuity is observed at the concatenation points in the synthesized speech due to unavailability of units with proper target specifications in the inventory. In this work, we are using prosody modification of the syllables before concatenation to match fundamental frequency (F0) and intensity of adjacent syllables. This approach is also helpful to build a small footprint syllable level USS system, where many examples of the basic units with varying prosodical and spectral features is not available. The discontinuity in pitch and intensity at the concatenation point is reduced after prosody modification. Moreover, naturalness and intelligibility of synthesized speech is found to be improved using the proposed method.
The Safety of Objects: Materialism, Existential Insecurity, and Brand Connection

Over the past 2 decades, a large body of research has examined how materialism is formed and how this value influences well-being. Although these studies have substantially contributed to our understanding of materialism, they shed little light on this value's relationship to consumer behavior. Our research seeks to address this gap by examining the influence of materialism on self- and communal-brand connections. We ground our conceptualization in terror management theory and suggest that materialistic individuals form strong connections to their brands as a response to existential insecurity. We test this premise by conducting a national survey among 314 adults as well as an experiment among 125 college students. Our results provide broad support for our thesis and suggest that the fear of death encourages materialistic individuals to form strong connections with their brands. (c) 2008 by JOURNAL OF CONSUMER RESEARCH, Inc.
Analysis and comparison of quality metrics with reference based on uniform colour spaces

Image quality assessment is becoming increasingly used in many applications. In most of the existing image quality assessment approaches, the main objective is to develop measures that are consistent with the subjective evaluation. Therefore, the performance of a given image quality metric is evaluated against the MOS determined from a series of subjective tests performed on a database. A plethora of image quality metrics has been developed. However, a few studies have been reported on the analysis and comparison of these metrics. This study attempts to provide a new framework for analysing and comparing some of the most common image quality metrics. Three quality representative metrics of the most known approaches have been chosen for this study. The Peak Signal to Noise Ratio (PSNR), the Visible Differences Predictor (VDP) and the Mean Structural SIMilarity index (SSIM). The two latter are found to be consistent with Weber's law. However, subjective testing in literature and computations derived from uniform color spaces such as CIE-L*a*b* suggest a different photometric invariance law. In this paper, we establish this photometric invariance law and show through numerical simulations how to check whether a given quality metric is compliant with this law.
DC FAULT PROTECTION IN SHIPBOARD POWER SYSTEMS USING Z-SOURCE BREAKERS

DC faults may cause severe disruptions in continuity of service to vital loads in a shipboard integrated power system, hence detection, isolation and protection against dc faults must be incorporated in both medium-voltage dc and low-voltage dc systems. A standard z-source breaker can identify and de-energize a faulty dc load on both buses quickly. However, until now, provisions for re-energizing have not been described. In this paper, a new z-source breaker is designed and control and detection techniques to identify the fault duration as well as to re-energize the load efficiently once the fault is cleared are developed. Dc fault characteristics on both mediumand low-voltage dc buses in a complete model of the allelectric-ship are demonstrated and the effective use of the new z-source breaker for the reenergizing process in protecting and restoring the continuity of ship service with a minimal effect is investigated.
Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease

Long-range gene–gene interactions are biologically compelling models for disease genetics and can provide insights on relevant mechanisms and pathways. Despite considerable effort, rigorous interaction mapping in humans has remained prohibitively difficult due to computational and statistical limitations. We introduce a novel algorithmic approach to find long-range interactions in common diseases using a standard two-locus test that contrasts the linkage disequilibrium between SNPs in cases and controls. Our ultrafast method overcomes the computational burden of a genome × genome scan by using a novel randomization technique that requires 10× to 100× fewer tests than a brute-force approach. By sampling small groups of cases and highlighting combinations of alleles carried by all individuals in the group, this algorithm drastically trims the universe of combinations while simultaneously guaranteeing that all statistically significant pairs are reported. Our implementation can comprehensively scan large data sets (2K cases, 3K controls, 500K SNPs) to find all candidate pairwise interactions (LD-contrast ) in a few hours—a task that typically took days or weeks to complete by methods running on equivalent desktop computers. We applied our method to the Wellcome Trust bipolar disorder data and found a significant interaction between SNPs located within genes encoding two calcium channel subunits: RYR2 on chr1q43 and CACNA2D4 on chr12p13 (LD-contrast test, ). We replicated this pattern of interchromosomal LD between the genes in a separate bipolar data set from the GAIN project, demonstrating an example of gene–gene interaction that plays a role in the largely uncharted genetic landscape of bipolar disorder.
General cluster updating method for Monte Carlo simulations.

Cet article presente un algorithme de mise a jour de cluster general pour des simulations de Monte Carlo. La methode contient des fonctions de probabilites arbitraires qui peuvent etre utilisees par minimiser le temps de relaxation. Ceci est applicable aux systemes ou l'energie d'interaction a une symetrie globale. Des cas speciaux couvrent cette mise a jour site par site standard ainsi que la methode de mise a jour de cluster proposee par Swenden et Wang pour les modeles d'Ising et de Potts
On $l_q$  Optimization and Matrix Completion

Rank minimization problems, which consist of finding a matrix of minimum rank subject to linear constraints, have been proposed in many areas of engineering and science. A specific problem is the matrix completion problem in which a low rank data matrix can be recovered from incomplete samples of its entries by solving a rank penalized least squares problem. The rank penalty is in fact the <i>l</i><sub>0</sub> “norm” of the matrix singular values. A recent convex relaxation of this penalty is the commonly used <i>l</i><sub>1</sub> norm of the matrix singular values. In this paper, we bridge the gap between these two penalties and propose the <i>lq</i>, 0 <; <i>q</i> <; 1 penalized least squares problem for matrix completion. An iterative algorithm is developed by solving a non-standard optimization problem and a non-trivial convergence result is proved. We illustrate with simulations comparing the reconstruction quality of the three matrix singular value penalty functions: <i>l</i><sub>0</sub>, <i>l</i><sub>1</sub>, and <i>lq</i>, 0 <; <i>q</i> <; 1.
Reflectance Measurements of Human Skin

We use a novel image-based technique to measure the directional reflectance of living human skin. Using only a commercial-grade digital camera and electronic flash, we can collect many samples in a few minutes. By exploiting the body’s own curvature, we obtain data comprehensively covering a wide range of directions. We present results from several different individuals, showing interesting effects both individually and in their differences from each other.