Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal design of a stair-climbing mobile robot with flip mechanism

Abstract Stairs overcoming is a primary challenge for mobile robots moving in human environments, and the contradiction between the portability and the adaptability of stair climbing robot is not well resolved. In this paper, we present an optimal design of a flip-type mobile robot in order to improve the adaptability as well as stability while climbing stairs. The kinematic constraints on the flip mechanism are derived to prevent undesired interferences among stairs, wheels and main body during climbing stairs. The objective function is proposed according to the traction demand of the robot during stair-climbing motion for the first time and the value of the objective function is calculated though kinetic analysis. The Taguchi method is using as the optimization tool because of its simplicity and cost-effectiveness both in formulating an objective function and in satisfying multiple constraints simultaneously. The performance of the robot under the optimal parameters is verified through simulations and experiments.
Interaction of spatial solitons in biased photorefractive photovoltaic media

Numerical investigations of the interactions among bright screening-photovoltaic solitons are performed in detail by using the Crank-Nicholson scheme. The numerical results show that a number of parameters, such as the initial separation and phase difference between solitons, can determine the interaction forces of solitons. The numerical study indicates that two in-phase solitons attract each other, and soliton fusions do occur at certain interaction length. The distance of the soliton fusions increases monotonously with the increment of initial separation between two solitons. On the other hand, two interacting beams repel each other when they are 180 degrees out of phase, the force of repulsion between them decreases monotonously with the increase of their initial separation. While the phase difference between two solitons is in the ranges of (0,pi) or (-pi,0), the situation is more complex because the power is transferred between the two solitons. Moreover, interactions among multiple solitons are also investigated.
Diversity of G proteins in signal transduction

The heterotrimeric guanine nucleotide-binding proteins (G proteins) act as switches that regulate information processing circuits connecting cell surface receptors to a variety of effectors. The G proteins are present in all eukaryotic cells, and they control metabolic, humoral, neural, and developmental functions. More than a hundred different kinds of receptors and many different effectors have been described. The G proteins that coordinate receptor-effector activity are derived from a large gene family. At present, the family is known to contain at least sixteen different genes that encode the alpha subunit of the heterotrimer, four that encode beta subunits, and multiple genes encoding gamma subunits. Specific transient interactions between these components generate the pathways that modulate cellular responses to complex chemical signals.
Tri-space and ranking based heterogeneous similarity measure for cross-media retrieval

We study the problem of cross-media retrieval, where the query and the returned results are of different modalities. A novel method is proposed to measure the similarity between heterogeneous media objects for cross-media retrieval. While existing methods only focus on the original low level feature spaces or the third common space, our proposed tri-space explores both of the two kinds of spaces. On one hand, the low level feature spaces can reflect the original accurate information of each modality and the third common space can effectively explore the useful information hidden across modalities. On the other hand, combination of multiple spaces can lead to good results since we can fully use the rich information of tri-space. Moreover, we propose to use ranking orders to represent media objects. Ranking based similarity makes our proposed method less sensitive to actual distance values and thus more stable. Experiments on the Wikipedia dataset demonstrate the effectiveness of our approach.
Upping the Game of Taxi Driving in the Age of Uber

In most cities, taxis play an important role in providing point-to-point transportation service. If the taxi service is reliable, responsive, and cost-effective, past studies show that taxi-like services can be a viable choice in replacing a significant amount of private cars. However, making taxi services efficient is extremely challenging, mainly due to the fact that taxi drivers are self-interested and they operate with only local information. Although past research has demonstrated how recommendation systems could potentially help taxi drivers in improving their performance, most of these efforts are not feasible in practice. This is mostly due to the lack of both the comprehensive data coverage and an efficient recommendation engine that can scale to tens of thousands of drivers. In this paper, we propose a comprehensive working platform called the Driver Guidance System (DGS). With real-time citywide taxi data provided by our collaborator in Singapore, we demonstrate how we can combine real-time data analytics and large-scale optimization to create a guidance system that can potentially benefit tens of thousands of taxi drivers. Via a realistic agent-based simulation, we demonstrate that drivers following DGS can significantly improve their performance over ordinary drivers, regardless of the adoption ratios. We have concluded our system designing and building and have recently entered the field trial phase.
Multiclass cancer classification based on gene expression comparison

Abstract As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer patients to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification.
A Unified Implicit Dialog Framework for Conversational Commerce

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users' input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.
Employee's Perceptions of Market Orientation in a Transitional Economy

Abstract Market orientation places a special emphasis on the dissemination of and responsiveness to market intelligence throughout the whole organization. However, it is still not well understood how employees at different levels perceive their firm's market orientation. This paper represents a first attempt to study market orientation at the employee level in the whole organization. Based on a survey of 1,357 managers and workers in China, we find that junior managers and front-line workers are less likely to perceive their firms as market-oriented than senior managers, group culture facilitates employees' market orientation perceptions, and a positive personal attitude toward change is beneficial to developing market orientation behaviors. The results also show that market orientation has a strong, positive impact on employees' job satisfaction, organizational commitment, and confidence in the firm's future performance. Implications on how to promote employee's market orientation behavior in a transitional economy are discussed.
A signal decomposition based Kalman smoother for T-wave alternans detection

The paper introduces an Extended Kalman Smoother (EKS) for T-wave alternans (TWA) detection, based on a dynamical model which is not directly dependent on amplitude (EKS3). In this framework we consider separate states for PQRS and an amplitude-free state model for T-wave. There are some theoretical advantages that EKS3 has over other frameworks recently introduced with the same aims (e.g., EKS6-4obs, Akhbari et al., 2014 and EKS25-4obs, Akhbari et al., 2013). For instance, no longer depending directly on the amplitude of the Gaussian kernel, it is able to model the nuances in the T-waves, even when small or abrupt changes happen in the signal. Moreover, it reduces the nonlinearity of the model and it uses only three states, resulting in a significant decrease in complexity. We compared the proposed method with EKS6-4obs and EKS25-4obs using data from the 2008 Physionet TWA challenge dataset. While all the methods showed similar performances in estimating the average TWA value, the reduced standard deviation displayed by EKS3 facilitates the adjudication of TWA's presence, when it assumes small values.
Quasar Black Hole Mass Estimates from High-Ionization Lines: Breaking a Taboo?

Can high ionization lines such as CIV λ 1549 provide useful virial broadening estimators for computing the mass of the supermassive black holes that power the quasar phenomenon? The question has been dismissed by several workers as a rhetorical one because blue-shifted, non-virial emission associated with gas outflows is often prominent in CIV λ 1549 line profiles. In this contribution, we first summarize the evidence suggesting that the FWHM of low-ionization lines like H β and MgII λ 2800 provide reliable virial broadening estimators over a broad range of luminosity. We confirm that the line widths of CIV λ 1549 is not immediately offering a virial broadening estimator equivalent to the width of low-ionization lines. However, capitalizing on the results of Coatman et al. (2016) and Sulentic et al. (2017), we suggest a correction to FWHM CIV λ 1549 for Eddington ratio and luminosity effects that, however, remains cumbersome to apply in practice. Intermediate ionization lines (IP ∼ 20–30 eV; AlIII λ 1860 and SiIII] λ 1892) may provide a better virial broadening estimator for high redshift quasars, but larger samples are needed to assess their reliability. Ultimately, they may be associated with the broad-line region radius estimated from the photoionization method introduced by Negrete et al. (2013) to obtain black hole mass estimates independent from scaling laws.
On the automatic construction of an Arabic thesaurus

Despite its accuracy, the traditional approach of manually constructing a thesaurus can be a very complex task with many challenges. On the other hand, constructing the thesaurus automatically has been found to be very useful in avoiding a number of drawbacks of the manual approach. Automating the process of thesaurus construction can save time, effort and cost in addition to allowing easy maintenance and expansion of the constructed thesaurus. Several approaches have been proposed to build thesauri in many languages (mainly English). To the best of our knowledge, there are very limited efforts towards automatically building a high-quality large-scale thesaurus for the Arabic language. To fill this knowledge gap, the paper aims to automatically build a thesaurus and compare various methods for this task. To this end, a dataset of 14,148 Arabic documents is collected on different topics such as Arts, Politics, etc. The dataset is analyzed to assign weights to each term using three different weighting approaches: Term Frequency-Inverse Document Frequency (TF-IDF), Pointwise Mutual Information (PMI) and Latent Semantic Analysis (LSA). Then, three different similarity measures (Cosine, Jaccard and Dice) are used to compute term-term similarity. We test the constructed thesauri on 20 queries to evaluate their accuracies and determine which combination performs the best. Recall and precision are the main accuracy measures used to evaluate the retrieval process. The experimental results demonstrated the superiority of TF-IDF approach over PMI and LSA approaches.
Intranet and Knowledge Management: Putting the CART before the Horse?

This paper explores the use of intranet-technology to support knowledge intensive decision-making in a technical service delivery process of a major oilfield services company. Our findings show that creating, mobilising, and exchanging knowledge through a system which is based on intranet-technology delivers some forms of benefits to both the organisation and its clients, and helps in understanding what organisational knowledge is to be managed and the process of managing it defines the role of technology that enables knowledge management.
Implicit and self-attributed dependency needs in dependent and histrionic personality disorders.

Theorists speculate that dependent personality disorder (DPD) and histrionic personality disorder (HPD) are both associated with high levels of implicit (i.e., unconscious) dependency needs but speculate that only DPD is associated with high levels of self-attributed (i.e., conscious) dependency needs. To test this hypothesis, 444 undergraduates (236 women and 208 men) completed the Personality Diagnostic Questionnaire-Revised (PDQ-R), along with widely used measures of implicit dependency needs (the Rorschach Oral Dependency Scale; ROD), and self-attributed dependency needs (the Interpersonal Dependency Inventory; IDI). Correlational analyses and comparisons of IDI and ROD scores in participants scoring above and below the PDQ-R DPD and HPD thresholds supported theorists' speculations regarding implicit and self-attributed dependency needs in DPD and HPD. Implications of these results are discussed, and suggestions for future studies are offered.
Dynamic Femtocell gNB On/Off Strategies and Seamless Dual Connectivity in 5G Heterogeneous Cellular Networks

To meet the drastic growth of the mobile traffic, 5G network is designed to optimize the transmission efficiency and provide higher quality of service (QoS). Small cell is considered as a promising and feasible approach to meet the increasing traffic demand. For this purpose, in this paper, we study a dynamic Femtocell gNB (F-gNB) ON/OFF strategies in 5G heterogeneous cellular networks (HCNs), which aims at maximizing the network energy efficiency (NEE) by optimizing jointly the traffic load prediction, the cell association, and the dynamic F-gNB ON/OFF strategies with respected to the time-varying traffic load, while taking into account the load balancing and the outage probability of the network. However, the optimization problem is a nonconvex problem. In order to relax the computation complexity, the original optimization problem is divided into two steps: cell association with load balancing (CALB) scheme and energy efficiency-based dynamic F-gNBs ON/OFF (DFOO) strategies. Specifically, the proposed CALB scheme could guarantee the load balancing as well as the minimum signal-to-interference-plus-noise ratio requirement of user equipments (UEs). In addition, the proposed DFOO strategies consider the operation of base stations (BSs) according to the predicted time-varying traffic load from Markov procedure. Furthermore, dual connectivity-based seamless handover procedure is introduced to guarantee the transmission QoS of UEs. Simulation results illustrate that the proposed DFOO algorithm provide considerable improvement of NEE while ensuring the load balancing of the HCN.
The influence of one-legged training on cardiorespiratory fitness.

Supported by University of Victoria Research Grant. The purpose of this investigation was to examine the influence of single leg training on one- and two-legged maximal oxygen consumption (VO2 max). Nine volunteers completed 7 weeks of one-legged interval training on a cycle ergometer adapted for single leg pedaling. The training regimen consisted of 15-20 intervals of 20 sec duration (1:3 work to rest ratio), 4 days a week at an intensity of 150% of the initial power output that elicited one-legged VO2 max. Pedaling cadence was maintained at 90 rpm. Heart rate (HR) was monitored throughout all training sessions. The results demonstrate that one-legged VO2 max was significantly increased (p < 0.05) from 3.36 l/min in the pretrained condition to 3.57 l/min and 3.69 l/min in the untrained (UT) and trained legs (T), respectively. No significant differences were observed between the UT and T legs for one-legged VO2 max after training. Two-legged VO2 max showed a 5% elevation following single leg training (p < 0.05). These data suggest that one-legged interval training of high intensity and short duration can improve both one- and two-legged maximal oxygen consumption. This finding implies that individuals can maintain aerobic power during rehabilitation following an injury to one limb, through the use of one-legged interval training on a cycle ergometer.J Orthop Sports Phys Ther 1988;10(1);8-11.
Constrictive Pericarditis with a Calcific Mass Invading into the Right Ventricular Myocardium

We present a rare and unique case of calcific constrictive pericarditis with a calcified pericardial mass invading the right ventricular myocardium. Perioperative two‐dimensional and three‐dimensional transesophageal echocardiography revealed the extent and structure of the pericardial mass and led to the repair of the right ventricular free wall as a surgical intervention.
Lossless Frame Memory Compression with Low Complexity using PCT and AGR for Efficient High Resolution Video Processing

This paper propose a lossless frame memory compression algorithm with low complexity based on the photo core transform (PCT) with adaptive prediction and adaptive Golomb-Rice (AGR) coding for high resolution video applications. The main goal of the proposed algorithm is to reduce the amount of frame memory in video codec losslessly for high resolution video applications. The proposed method has a block-based structure in which the basic unit of processing is a macroblock (MB) of each video frame. The combination of the PCT with adaptive prediction and AGR is utilized to achieve lossless and low complexity compression. The proposed algorithm does not need any memory operation to store data, so that it is able to minimize the implementation cost. Simulation results illustrate that the proposed algorithm achieves a superior compression performance to the existing methods it is easily applicable to hardware devices without image quality degradation and with negligible structure modification. Moreover, the proposed algorithm can be employed to real-time applications in terms of its considerable improvement of the compression ratio but a slight increment of its execution time.
Implementation strategies for effective change: A handbook for instructional leaders

The purpose of this study was to research, develop, and validate a handbook of effective strategies that an instructional leader can implement and that are more likely to bring about and sustain a successful change process. Implementation Strategies for Effective Change: A Handbook for Instructional Leaders was developed using the research and development methodology as recommended by Gall, Borg, and Gall (1989, 1996) and Dick and Carey (1985) through a seven-step development cycle. The review of literature and proof of concept questionnaire provided information for developing the outline and initial prototype. Experts in the field served as preliminary field evaluators using a Likert scale and open-ended questions to provide feedback. Revisions were made based on their feedback. The main field test was conducted with potential users of the guide. Final revisions were based on main field test evaluators’ feedback. The conclusions of the study were: (1) the role of the principal has changed from that of a manager to an instructional leader who possesses a broad base of pedagogical and content knowledge and who understands how to lead a staff through a complicated change process; (2) a handbook that provides guidance and support for principals leading a change process focused on the improvement of instruction to raise student achievement would be a useful resource; (3) development of a comprehensive handbook or guide that blends theory, research, and practice for instructional leaders would be a beneficial resource; (4) the study produced a comprehensive guide to assist instructional leaders who are leading a change process focused on the improvement of instruction to raise student achievement, and (5) the strategies included in the handbook allow principals to adjust and adapt according to their schools’ unique situation and
Measuring Key Disparities in Human Development: The Gender Inequality Index

Gender inequality remains a major barrier to human development. Girls and women have made major strides since 1990, but they have not yet gained gender equity. In this paper, we review ways to measure and monitor gender inequality, providing a critique of existing measures including the first global gender indices that were launched in the 1995 Human Development Report – the Gender-related Development Index and the Gender Empowerment Measure - and introduce a new index that is presented in the 2010 Human Development Report. The Gender Inequality Index, which addresses the key criticisms of previous measures, is unique in including critical issues of educational attainment, economic and political participation, and reproductive health issues and in accounting for overlapping inequalities at the national level. As such, it represents an important advance on existing global measures of gender equity. Measures of the disadvantages for women raise awareness of problems, permit monitoring of progress towards gender equity objectives and help keep governments accountable. In this light, the Gender Inequality Index is designed to reveal the extent to which the realization of a country’s human development potential is curtailed by gender inequality, and provides empirical foundations for policy analysis and advocacy efforts. We also compare our results with the results of alternative gender inequality indices, finding significant variation in rankings across the various indices due largely to differences in the elements of gender inequality they seek to measure.