Miracle-dz/UniFAR-FTUs huggingface dataset:
SMU Data Science Review SMU Data Science

Major corporations utilize data from online platforms to make user product or service recommendations. Companies like Netflix, Amazon, Yelp, and Spotify rely on purchasing trends, user reviews, and helpfulness votes to make content recommendations. This strategy can increase user engagement on a company's platform. However, misleading and/or spam reviews significantly hinder the success of these recommendation strategies. The rise of social media has made it increasingly difficult to distinguish between authentic content and advertising, leading to a burst of deceptive reviews across the marketplace. The helpfulness of the review is subjective to a voting system. As such, this study aims to predict product reviews that are helpful and enable strategies to moderate a user review post to improve the helpfulness quality of a review. The prediction of review helpfulness will utilize NLP methods against Amazon product review data. Multiple machine learning principles of different complexities will be implemented in this review to compare the results and ease of implementation (e.g., Naïve Bayes and BERT) to predict a product review's helpfulness. The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features. The removal of duplicate reviews, the imputing of review helpfulness based on word count, and the inclusion of lexical elements are recommended to be included in review analysis. The results of this research indicate that the deployment of these features results in a high F1-Score of 0.83 for predicting helpful Amazon product reviews.
Non-linear mixed regression models

In this paper we present an estimating equation approach to statistical inference for non-linear random effects regression models for correlated data. With this approach, the distribution of the observations and the random effects need not be specified; only their expectation and covariance structure are required. The variance of the data given the random effects may depend on the conditional expectation. An approximation to the conditional expectation about the fitted value of the random effects is used to obtain closed form expressions for the unconditional mean and covariance of the data. The proposed methods are illustrated using data from a mouse skin painting experiment.
A turbulent origin for the rotation of molecular clouds

The observed rotational properties of dark clouds are shown to be consistent with the turbulent character of the interstellar medium. The turbulent flow is easily maintained by the shearing action of differential galactic rotation. The size distribution of molecular clouds inferred from observations is found to closely resemble a Kolmogorov turbulent spectrum. These findings suggest that the processes leading to the formation and evolution of such clouds may depend strongly on the properties and characteristics of interstellar turbulence.
The dark matter interpretation of the 511 keV line

The 511 keV high-precision map obtained by the INTEGRAL/SPI experiment suggested a surprisingly large amount of positrons in our galaxy with respect to ‘naive’ astrophysical expectations. Although an astrophysical origin is not excluded, this signal may shed light on dark matter. Here we discuss a possible MeV dark matter interpretation of the 511 keV line and point out a possible way of validating the MeV dark matter scenario. In particular, we show that the number of electrons generated by MeV annihilating particles would be large enough to lead to a visible Sunyaev–Zel'dovich (SZ) effect signature in clusters of galaxies if their dark matter halo profile behaves as ρ(r)∝1/rγ with γ>1 in the inner part (i.e. on distances corresponding to a sub-arcsecond resolution for an experiment dedicated to SZ measurement).
Attitude-independent magnetometer calibration for marine magnetic surveys: regularization issue

We have developed an attitude-independent calibration method for a shipboard magnetometer to estimate the absolute strength of the geomagnetic field from a marine vessel. The three-axis magnetometer to be calibrated is fixed on a rigid aluminium boom ahead of the vessel to reduce the magnetic effect of the vessel. Due to the constrained manoeuvres of the vessel, a linear observational equation system for calibration parameter estimation is severely ill-posed. Consequently, if the issue is not mitigated, traditional calibration methods may result in unreliable or unsuccessful solutions. In this paper, the ill-posed problem is solved by using the truncated total least squares (TTLS) technique. This method takes advantage of simultaneously considering errors on both sides of the observation equation. Furthermore, the TTLS method suits strongly ill-posed problems. Simulations and experiments have been performed to assess the performance of the TTLS method and to compare it with the performance of conventional regularization approaches such as the Tikhonov method and truncated single value decomposition. The results show that the proposed algorithm can effectively mitigate the ill-posed problem and is more stable than the compared regularization methods for magnetometer calibration applications.
Rate‐dependent ductile failure model

A rate‐dependent constitutive model for the dynamic deformation of ductile materials is developed. The model introduces a physical length scale into the equations governing the progressive failure of materials due to void growth. Consequently, mesh sensitivity or localization problems inherent to rate‐independent models are precluded. The model is implemented into an explicit, finite‐difference computer code. The insensitivity of the model to changes in the mesh size is demonstrated. Comparisons are provided between numerical simulations and data for uniaxial impact experiments. Excellent agreement is established between the final porosity levels and the width of the damage zone. Also, excellent agreement is provided for the stress histories, including the peak stress values and the spall signal.
Novel eigenanalysis method for direction estimation

A new eigenanalysis-based technique for direction estimation (and for estimation of the parameters of superimposed exponential signals from multiexperiment noisy data) is introduced. This novel technique, which is called MODE (method of direction estimation), offers the performance of the maximum likelihood (ML) method (the MODE and ML estimators coincide as the number of data samples increases) at a modest computational effort, which is comparable to that associated with other eigenanalysis-based techniques such as the MUSIC algorithm. Compared to the latter, MODE offers the advantage of better performance, especially in situations where the sources are highly correlated. The type of performance that can be achieved by MODE is illustrated by means of some numerical examples which also show, for comparison, the corresponding performance achieved by the MUSIC algorithm and a popular approximate ML algorithm.
A high power/weight dexterous manipulator using 'Sensory Glove' based motion control and tactile feedback

Manipulators in one form or another have been in existence for many hundreds of years with the design (ranging from simple hooks or two-jaw systems to complex multi-articulated grippers) usually motivated by the particular class of task. When the problem domain includes a varied range of objects, one approach is to emulate those attributes of the human hand which make it such a versatile end-effector. This work considers the design of a dexterous (3-fingered 1-thumb) hand driven by newly developed high power/weight performance pneumatic muscle actuators (PMA). Although the manipulator can be used in conventional robotic applications the primary aim is for a high-performance tele-presence system with full manipulator input and tactile feedback. The robot manipulator closely replicates the skeletal layout of the human hand, making controlfrom a gloved input device (developed at Salford) feasible, thereby substantially reducing the control difficulties associated with decterous devices and making widevpread low cost use of flexible manipulative devices possible.
Market Transactions and Hypothetical Demand Data: A Comparative Study

Abstract Empirical demand studies have been based on data from (a) actual market transactions or (b) hypothetical questions. Many social scientists are skeptical of the accuracy of responses to hypothetical questions, yet few studies assess the quality of this type of data. This article directly compares the demand relations obtained from actual market transactions and hypothetical survey responses using primary field data and limited dependent variable regression analysis. Using a log-likelihood ratio test, the null hypothesis that the two demand relations are statistically identical cannot be rejected at the 1% level of significance.
A novel method for non-destructive Compton scatter imaging based on the genetic algorithm

Compton scattering tomography is widely used in numerous applications such as biomedical imaging, nondestructive industrial testing and environmental survey, etc. This paper proposes the use of the genetic algorithm (GA), which utilizes bio-inspired mathematical models, to construct an image of the insides of a test object via the scattered photons, from a voxel within the object. A NaI(Tl) scintillation detector and a 185 MBq 137Cs gamma ray source were used in the experimental measurements. The obtained results show that the proposed GA based method performs well in constructing images of objects.
Energy transport in α-helical protein models: One-strand versus three-strand systems

We study the transport of vibrational energy in α-helix forms of proteins within the frame of a steric Davydov model. Our main objective is the comparison of the localization and transport properties of the widely used one-strand approximation of the a helix associated with a linear chain of hydrogen bonded peptide groups and the complete three-dimensional helical system consisting of three such strands being connected via covalent bonds. We construct polaron solutions of the set of discrete equations utilizing a nonlinear map method for finding the ground state. With respect to the polaron wave pattern, we found that while the excitonic wave function of the one-strand system is localized at a site and monotonically decaying apart from it on the three-strand system a multihump structure arises whose envelope exhibits monotonic decay. For the polaronic states the static deformations of the H bonds are stronger for an isolated strand than for a strand embedded in the three-dimensional protein cage. In the excited region the radius of the helix slightly decreases. The participation numbers show that the exciton localization is more strongly pronounced on the one-strand system than on its three-strand counterpart. In our dynamical studies we pay special attention to the mobility of the polarons when their internal pinning modes are initiated. The latter are identified as certain isolated Floquet eigenvalues obtained from the Floquet map, which is derived from the linearized equations in tangent space. It is shown that the polarons on the one-strand system move with higher velocity than on the three-strand system. In the latter case the preferred transport path proceeds across the hydrogen bonds and polaron motion along the covalent channel is suppressed.
Interplay of physical and cognitive performance using hierarchical continuous-time dynamic modelling and a dual-task training regime in AD patients

Various longitudinal studies report positive effects of physical exercise on cognitive performance in patients with Alzheimer's disease (AD). However, most of these studies fail to capture the dynamic nature of the change and interplay of physical fitness and cognition. To overcome this limitation, we employ a hierarchical continuous-time dynamic model to analyze data of a 24-week physical and cognitive dual-task training regime (3 sessions per week) in older adults with AD (N = 17). The model was specified with two fully connected state variables enabling bidirectional effects between physical and cognitive performance over measurement occasions. The results show that physical performance is dynamically linked to cognitive performance. In line with earlier findings, higher physical performance predicted improved memory recognition performance in terms of faster reaction times.
Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes

Rapid and automated extraction of clinical information from patients' notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagnosis, we show evidence of rising mental health symptoms at the population level and our last use case shows the evolution of COVID-19 symptomatology throughout three waves between February 2020 and October 2021. The validation of our tool showed good performance on both specific annotations from our development set (F1 score 0.81) and open source annotated data set (F1 score 0.79). We successfully demonstrate the versatility of our pipeline with three different use cases. Finally, we note that the modular nature of our tool allows for a straightforward adaptation to another biomedical ontology. We also show that our tool is independent of EHR system, and as such generalizable.
Relationship between blood bone metabolic biomarkers and anemia in CKD patients

Introduction: Blood bone metabolic biomarkers are non-invasive indexes for evaluating renal osteodystrophy (ROD). Here the relationships between blood bone metabolic biomarkers and anemia in chronic kidney disease (CKD) patients are investigated, the effects of parathyroidectomy (PTX) on above indices are analyzed. Methods: In this cross-sectional study, 100 healthy controls and 239 CKD patients, including 46 secondary hyperparathyroidism (SHPT) subgroup with PTX, were enrolled. A prospective study with 28 PTX patients was followed up. The degree of anemia was classified as mild, moderate, and severe based on the tertiles of hemoglobin (Hb) levels of the anemic CKD patients, with cutoff values of 83g/L and 102g/L. Bone metabolic biomarkers, including calcium (Ca), phosphorus (P), intact parathyroid hormone (iPTH), fibroblast growth factor 23 (FGF23) and a-klotho were tested. Results: The mean eGFR in CKD patients was 25.74+-35.99 ml/min/1.73 m2 and 84.10% patients had anemia. The baseline Hb levels in the mild, moderate, and severe anemia subgroups were 110.86+-5.99g/L, 92.71+-5.96g/L and 67.38+-10.56g/L, respectively. CKD patients had higher adjusted Ca, P, ALP, iPTH and FGF23 levels, and lower a-klotho levels than controls. Baseline adjusted Ca, P, iPTH and a-klotho levels were associated with Hb in CKD patients. Blood adjusted Ca, P, iPTH levels were correlated with anemia severity. After PTX (median interval: 6.88 months), anemia and high blood adjusted Ca, P, iPTH and FGF23 levels were ameliorated, while a-klotho levels increased. Conclusions: Blood adjusted Ca, P, iPTH and a-klotho levels were correlated with Hb in CKD patients, correcting ROD is supposed to be therapeutic targets for anemia. Keywords: Anemia; Chronic kidney disease-mineral and bone disorder; Bone metabolic biomarkers; Parathyroidectomy; Secondary hyperparathyroidism
epcAware: A Game-based, Energy, Performance and Cost Efﬁcient Resource Management Technique for Multi-access Edge Clouds

—The Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud for analysis, due to longer distances and delays. Fog/edge computing is a new model for analyzing and acting on time-sensitive data (real-time applications) at the network edge, adjacent to where it is produced. The model sends only selected data to the cloud for analysis and long-term storage. Furthermore, cloud services provided by large companies such as Google, can also be localized to minimize the response time and increase service agility. This could be accomplished through deploying small-scale datacenters (reffered to by name as cloudlets) where essential, closer to customers (IoT devices) and connected to a centrealised cloud through networks - which form a multi-access edge cloud (MEC). The MEC setup involves three different parties, i.e. service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management a defﬁcult job. In the literature, various resource management techniques have been suggested in the context of what kind of services should they host and how the available resources should be allocated to customers’ applications, particularly, if mobility is involved. However, the existing literature considers the resource management problem with respect to a single party. In this paper, we assume resource management with respect to all three parties i.e. IaaS, SaaS, NaaS; and suggest a game theoritic resource management technique that minimises infrastructure energy consumption and costs while ensuring applications performance. Our empirical evaluation, using real workload traces from Google’s cluster, suggests that our approach could reduce up to 11.95% energy consumption, and approximately 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27% energy bills and NaaS can increase their costs savings up to
An Effective Image Fusion Method Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network

In order to solve the problem of spectral distortion and the fuzzy texture in visible and infrared image fusion technology, a novel visible and infrared image fusion method based on the Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks (PCNN) is proposed in this paper. First, we gain three components of visible image, luminance I, chrominance H and saturation S, using the IHS transform. Then, we gain three coefficients, low frequency sub-band, passband sub-band and high frequency coefficient by decomposing the component I and infrared image with the help of the NSCT. Next, we use weighted-sum method to fuse the low frequency sub-band and PCNN method to fuse the other sub-band coefficient respectively. At last, we gain the fusion image by using the inverse IHS transform on the fusion component I gained by the inverse NSCT transform. Experiments show that our method have better fusion quality and can be more better to keep the visible spectral and detail information than some traditional methods such as, Laplace method, Wavelet method and Lifting Wavelet method.
Adaptive Hierarchical Down-Sampling for Point Cloud Classification

Deterministic down-sampling of an unordered point cloud in a deep neural network has not been rigorously studied so far. Existing methods down-sample the points regardless of their importance for the network output and often address down-sampling the raw point cloud before processing. As a result, some important points in the point cloud may be removed, while less valuable points may be passed to next layers. In contrast, the proposed adaptive down-sampling method samples the points by taking into account the importance of each point, which varies according to application, task and training data. In this paper, we propose a novel deterministic, adaptive, permutation-invariant down-sampling layer, called Critical Points Layer (CPL), which learns to reduce the number of points in an unordered point cloud while retaining the important (critical) ones. Unlike most graph-based point cloud down-sampling methods that use k-NN to find the neighboring points, CPL is a global down-sampling method, rendering it computationally very efficient. The proposed layer can be used along with a graph-based point cloud convolution layer to form a convolutional neural network, dubbed CP-Net in this paper. We introduce a CP-Net for 3D object classification that achieves high accuracy for the ModelNet 40 dataset among point cloud-based methods, which validates the effectiveness of the CPL.
Lying with maps

Darrell Huff's How to Lie with Statistics was the inspiration for How to Lie with Maps, in which the author showed that geometric distortion and graphic generalization of data are unavoidable elements of cartographic representation. New examples of how ill-conceived or deliberately contrived statistical maps can greatly distort geographic reality demonstrate that lying with maps is a special case of lying with statistics. Issues addressed include the effects of map scale on geometry and feature selection, the importance of using a symbolization metaphor appropriate to the data and the power of data classification to either reveal meaningful spatial trends or promote misleading interpretations.
Nomogram for predicting overall survival time of patients with stage IV colorectal cancer

Abstract Background Prognosis varies among stage IV colorectal cancer (CRC). Our study aimed to build a robust prognostic nomogram for predicting overall survival (OS) of patients with stage IV CRC in order to provide evidence for individualized treatment. Method We collected the information of 16,283 patients with stage IV CRC in the Surveillance, Epidemiology, and End Results (SEER) database and then randomized these patients in a ratio of 7:3 into a training cohort and an internal validation cohort. In addition, 501 patients in the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) database were selected and used as an external validation cohort. Univariate and multivariate Cox analyses were used to screen out significant variables for nomogram establishment. The nomogram model was assessed using time-dependent receiver-operating characteristic curve (time-dependent ROC), concordance index (C-index), calibration curve, and decision curve analysis. Survival curves were plotted using the Kaplan–Meier method. Result The C-index of the nomogram for OS in the training, internal validation, and external validation cohorts were 0.737, 0.727, and 0.655, respectively. ROC analysis and calibration curves pronounced robust discriminative ability of the model. Further, we divided the patients into a high-risk group and a low-risk group according to the nomogram. Corresponding Kaplan–Meier curves showed that the prediction of the nomogram was consistent with the actual practice. Additionally, model comparisons and decision curve analysis proved that the nomogram for predicting prognosis was significantly superior to the tumor-node-metastasis (TNM) staging system. Conclusions We constructed a nomogram to predict OS of the stage IV CRC and externally validate its generalization, which was superior to the TNM staging system.