Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Performance of Wireless Links Using Hybrid ARQ Based on RSM and Turbo Codes

A hybrid ARQ scheme for multiple transmit antennas systems is presented. Space-diversity is achieved through the use of random signal mapping (RSM). The use of RSM instead of other typical space-time coding schemes, as space-time trellis codes or space-time block codes, allows that more antennas may be added to the system with negligible complexity increase at the receiver. Coding gain is achieved with a standard parallel turbo code. Numerical results for frame error rate, throughput and number of required retransmissions for a varying number of transmit antennas are presented. The impact of the proposed scheme in the performance of a higher layer protocol, as TCP, is also investigated through computer simulations.
The MCMI-III in Child Custody Evaluations

Abstract This study provides normative data on the Millon Clinical Multiaxial Inventory-III (MCMI-III) among 259 child custody examinees who were obtained from private practice settings in four different states. The mean MCMI-III profile in this sample was an elevation on Scale Y (Desirability), with subclinical elevations on Scales 4 (Histrionic), 5 (Narcissistic), and 7 (Compulsive). Females scored significantly higher than males on Scales 4,5, and 7. Moreover, mean Base Rate (BR) scores were very low for most scales. With the exception of Scales 4 and 7 for females, the frequency of clinically significant elevations (i.e., BR > 75) on MCMI-III scales was very low among child custody litigants. Results from this study provide empirical support for the position that the MCMI-III does not overpathologize child custody examinees. Some interpretive guidelines are offered to reduce the likelihood of false positive diagnoses on those few scales where such errors may arise. This study provides support for continued use of the MCMI-III in child custody evaluations.
Automatic Content-Based Categorization of Wikipedia Articles

Wikipedia's article contents and its category hierarchy are widely used to produce semantic resources which improve performance on tasks like text classification and keyword extraction. The reverse -- using text classification methods for predicting the categories of Wikipedia articles -- has attracted less attention so far. We propose to "return the favor" and use text classifiers to improve Wikipedia. This could support the emergence of a virtuous circle between the wisdom of the crowds and machine learning/NLP methods. We define the categorization of Wikipedia articles as a multi-label classification task, describe two solutions to the task, and perform experiments that show that our approach is feasible despite the high number of labels.
The impact of the May 12 Wenchuan earthquake on China's stock market — As an example to the Chenyu price index of China

In addition to macroeconomic effects, the stock market conditions also closely relates to a number of unexpected events. The taken place of Wenchuan earthquake on May 12th, 2008, had short-term effects on the stock market, some experienced suspension, but some experienced soaring. 66 listed companies from Sichuan and Chongqing had experienced suspension from 13th of May, due to the earthquake. 45 Shanghai-listed companies from Sichuan and Chongqing suspended; On 14th of May, medicine, agriculture and cement industry stocks rose drastically, there are more than 40 pharmaceutical industry stocks raising more than 10% in the two stock markets. By the GARCH model, this paper analyzes the long-term effects of the earthquake. The results show that the earthquake does not change the general trend of Chinese stock market, except the influencing time is short, and the duration of fluctuating is not long. Thus, long-term impact of the earthquake on Chinese stock market is limited.
Multifunctional aliphatic polyester nanofibers for tissue engineering

Electrospun fibers based on aliphatic polyesters, such as poly(ε-caprolactone) (PCL), have been widely used in regenerative medicine and drug delivery applications due to their biocompatibility, low cost and ease of fabrication. However, these aliphatic polyester fibers are hydrophobic in nature, resulting in poor wettability, and they lack functional groups for decorating the scaffold with chemical and biological cues. Current strategies employed to overcome these challenges include coating and blending the fibers with bioactive components or chemically modifying the fibers with plasma treatment and reactants. In the present study, we report on designing multifunctional electrospun nanofibers based on the inclusion complex of PCL-α-cyclodextrin (PCL-α-CD), which provides both structural support and multiple functionalities for further conjugation of bioactive components. This strategy is independent of any chemical modification of the PCL main chain, and electrospinning of PCL-α-CD is as easy as electrospinning PCL. Here, we describe synthesis of the PCL-α-CD electrospun nanofibers, elucidate composition and structure, and demonstrate the utility of functional groups on the fibers by conjugating a fluorescent small molecule and a polymeric-nanobead to the nanofibers. Furthermore, we demonstrate the application of PCL-α-CD nanofibers for promoting osteogenic differentiation of human adipose-derived stem cells (hADSCs), which induced a higher level of expression of osteogenic markers and enhanced production of extracellular matrix (ECM) proteins or molecules compared with control PCL fibers.
PCP Theorem And Hardness Of Approximation For MAX-SATISFY Over Finite Fields

This paper will survey the PCP theorem and its application in solving the MAX-SATISFY. For the problem MAX-SATISFY over F2, a random assignment satisfies half the equations and thus yields a 2-approximation algorithm. It is the best possible approximation algorithm in polynomial time based on P 6= NP , even if each equation only contains exactly three variables, which can be proved by the 3-bit PCP theorem due to Johan H̊astad. Similar results can be extended to MAX-SATISFY over finite fields.
Effects of Abusive Experiences on Adolescent's Suicidal Ideation : Focusing on Mediating Effects of Shame-proneness and Friendships

This study aimed to assess the roll of various abusive factors on suicide thoughts. In particular, the effects of negligence, physical and emotional abuse, as the subordinate variables of abuse, were examined on suicide ideation. In addition, the mediation effects of shame-proneness and friendships were also investigated. To achieve these specific objectives, subordinate dimensions of abuse such as negligence, physical abuse and emotional abuse were measured for 277 male and 275 female students from Seoul middle schools using a measurement tool. Specifically, experiences of being abused and neglected, the level of suicide thoughts using scale for suicidal ideation (SSI), the level of shame-proneness and friendships using personal feelings questionnaire-2(PFQ-2), and schoolmate relationships were measured using a questionnaire. Analysis of collected data revealed that while suicide thoughts showed significant positive relationship with abuse and shame-proneness, there was a significant negative relationship between suicide thoughts and friendships. In particular, negligence, physical abuse and emotional abuse had significant effects on suicide ideation, while emotional abuse had the greatest effect on suicide ideation. Shame-proneness and friendships partially mediated abuse and suicide thoughts. The findings of this study have contributed to understanding of factors related to suicide thoughts. In particular, the mediation effects of shame-proneness and friendships have been highlighted.
Cyclomatic complexity: The nesting problem

Cyclomatic complexity is a metric for the measurement of complexity of a software. This metric although widely cited has many limitations. Many authors criticized cyclomatic complexity in many ways but still it is the most widely accepted idea regarding software complexity. One of the problems in cyclomatic complexity is the nesting problem. A nested construct is more complex than a simple construct, but cyclomatic complexity calculates same complexity for both types of constructs. Many authors addressed this problem but their solutions also has limitations. One of the limitations is that same solutions cannot be applied over nested-loop. In this paper we propose a solution to differentiate between a nested loop and a simple loop.
Ten Years of Landscape Genomics: Challenges and Opportunities

Landscape genomics is a relatively new discipline that aims to reveal the relationship between adaptive genetic imprints in genomes and environmental heterogeneity among natural populations. Although the interest in landscape genomics has increased since this term was coined, studies on this topic remain scarce. Landscape genomics has become a powerful method to scan and determine the genes responsible for the complex adaptive evolution of species at population (mostly) and individual (more rarely) level. This review outlines the sampling strategies, molecular marker types and research categories in 37 articles published during the first 10 years of this field (i.e., 2007–2016). We also address major challenges and future directions for landscape genomics. This review aims to promote interest in conducting additional studies in landscape genomics.
Edinburgh’s Syntax-Based Systems at WMT 2014

This paper describes the string-to-tree systems built at the University of Edin-burgh for the WMT 2014 shared translation task. We developed systems for English-German, Czech-English, French-English, German-English, Hindi-English, and Russian-English. This year we improved our English-German system through target-side compound splitting, morphosyntactic constraints, and reﬁne-ments to parse tree annotation; we addressed the out-of-vocabulary problem us-ing transliteration for Hindi and Russian and using morphological reduction for Russian; we improved our German-English system through tree binarization; and we reduced system development time by ﬁltering the tuning sets.
A distributed source coding framework for multiple sources

In this paper the problem of compressing multiple correlated sources is addressed. It is assumed that the m sources are given as a sequence where an assumption is made only on the correlation between neighbouring sources. The correlation assumption is deterministic and Hamming-distance based (so-called ldquoconstrained correlationrdquo in the literature). The problem of compressing such a sequence of correlated sources is easily solved by repeatedly applying the asymmetric distributed source coding (DSC) for two sources, as in the DISCUS framework of (S.S. Pradhan et al., 2003). In this paper we present an alternative, more general, framework that achieves the same overall compression rate but allows for more flexible compression rates per source. The proposed encoder employs a channel code with a parity check matrix that does not need to be systematic. The proposed decoder consists of three steps. Firstly, difference patterns between sources are recovered from the compressed data via a channel decoder. Subsequently, this information is combined with other parts of the compressed data. Finally, by solving a linear equation and adding difference patterns, the original messages are reconstructed. We show that, for m = 2, several DSC coding schemes from the literature are special cases. We illustrate our framework for a sequence of 3 sources through a (15, 7) BCH code with a non-systematic parity check matrix.
A Study on Document Classification using Machine Learning Techniques

With the explosion of information fuelled by the growth of the World Wide Web it is no longer feasible for a human observer to understand all the data coming in or even classify it into categories. With this growth of information and simultaneous growth of available computing power automatic classification of data, particularly textual data, gains increasingly high importance. Text classification is a task of automatically sorting a set of documents into categories from a predefined set and is one of the important research issues in the field of text mining. This paper provides a review of generic text classification process, phases of that process and methods being
3D real time object recognition

Object recognition is a natural process of the human brain performed in the visual cortex and relies on a binocular depth perception system that renders a three-dimensional representation of the objects in a scene. Hitherto, computer and software systems are been used to simulate the perception of three-dimensional environments with the aid of sensors to capture real-time images. In the process, such images are used as input data for further analysis and development of algorithms, an essential ingredient for simulating the complexity of human vision, so as to achieve scene interpretation for object recognition, similar to the way the human brain perceives it. The rapid pace of technological advancements in hardware and software, are continuously bringing the machine-based process for object recognition nearer to the inhuman vision prototype. The key in this field, is the development of algorithms in order to achieve robust scene interpretation. A lot of recognisable and significant effort has been successfully carried out over the years in 2D object recognition, as opposed to 3D. It is therefore, within this context and scope of this dissertation, to contribute towards the enhancement of 3D object recognition; a better interpretation and understanding of reality and the relationship between objects in a scene. Through the use and application of low-cost commodity sensors, such as Microsoft Kinect, RGB and depth data of a scene have been retrieved and manipulated in order to generate human-like visual perception data. The goal herein is to show how RGB and depth information can be utilised in order to develop a new class of 3D object recognition algorithms, analogous to the perception processed by the human brain. This dissertation presents my original work for the simulation of human vision in 3D objection recognition, focusing in the following three areas: 3D Human Recognition: The first area addresses the problem of localisation and spatial extent determination of a human in three-dimensional space. To this end, a Conditional Random Field (CRF) pairwise energy function is defined for the segmentation task using features from both RGB and depth space. Furthermore, the maximum a-posteriori (MAP) labelling is determined in polynomial time by minimising the energy function with the use of graph cuts. The novelty of this proposed approach is that no user interaction is required for determining the segmentation, as opposed to related work in the field. Moreover, the
Interconnected Cloud Computing Environments

A brief review of the Internet history reveals the fact that the Internet evolved after the formation of primarily independent networks. Similarly, interconnected clouds, also called Inter-cloud, can be viewed as a natural evolution of cloud computing. Recent studies show the benefits in utilizing multiple clouds and present attempts for the realization of an Inter-cloud or federated cloud environment. However, cloud vendors have not taken into account cloud interoperability issues, and each cloud comes with its own solution and interfaces for services. This survey initially discusses all the relevant aspects motivating cloud interoperability. Furthermore, it categorizes and identifies possible cloud interoperability scenarios and architectures. The spectrum of challenges and obstacles that the Inter-cloud realization is faced with are covered, a taxonomy of them is provided, and fitting enablers that tackle each challenge are identified. All these aspects require a comprehensive review of the state of the art, including ongoing projects and studies in the area. We conclude by discussing future directions and trends toward the holistic approach in this regard.
ESR1 amplification in endometrial carcinomas: hope or hyperbole?

The ESR1 gene maps 6q25 and encodes for oestrogen receptor alpha, which has been shown to play a pivotal role in the development of breast and endometrial cancer. It has recently been reported that oestrogen receptor alpha expression may be driven in some cases by ESR1 gene amplification and that this phenomenon may be an early event in breast and endometrial carcinogenesis. Although copy number gains of 6q have been reported by several groups, their prevalence, association with oestrogen receptor alpha expression, and clinical implications have been a matter of controversy. Here we discuss the key issues regarding the methods employed in the identification of ESR1 amplification, and briefly review the current literature and recent controversies on the subject of ESR1 amplification in endometrial and breast cancers. Copyright © 2008 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Road collapse in Magnum

This paper describes the implementation of a triangulation based collapse algorithm in the general-purpose object oriented DBMS Magnum. The contribution of the paper is twofold. First, we show that true integration of complex spatial functionality in a DBMS can be achieved. Second, we worked out a collapse algorithm to be used in the complex area of map generalization.
Key Characteristics of Teacher Leaders in Schools.

Teacher leaders who share their specialized knowledge, expertise, and experience with other teachers broaden and sustain school and classroom improvement efforts. Teacher leaders can transform classrooms into learning laboratories where every student is engaged in relevant and well-designed curricular content, every teacher embraces the use of more effective instructional strategies, and authentic assessments provide evidence of rich student learning. This work describes four essentialities associated with teacher leaders: a focus on student learning, along with the importance of empowerment, relationships, and collaboration. In addition to gleaning insights from the literature, examples of the impact of teacher leaders in schools are provided to demonstrate the importance of each.
Robust particle PHD filter with sparse representation for multi-target tracking

Recently, sparse representation has been widely used in computer vision and visual tracking applications, including face recognition and object tracking. In this paper, we propose a novel robust multi-target tracking method by applying sparse representation in a particle probability hypothesis density (PHD) filter framework. We employ the dictionary learning method and principle component analysis (PCA) to train a static appearance model offline with sufficient training data. This pre-trained dictionary contains both colour histogram and oriented gradient histogram (HOG) features based on foreground target appearances. The tracker combines the pre-trained dictionary and sparse coding to discriminate the tracked target from background clutter. The sparse coefficients solved by ℓ1-minimization are employed to generate the likelihood function values, which are further applied in the update step of the proposed particle PHD filter. The proposed particle PHD filter is validated on two video sequences from publicly available CAVIAR and PETS2009 datasets, and demonstrates improved tracking performance in comparison with the traditional particle PHD filter.
Synthetic matrix metalloproteinase-sensitive hydrogels for the conduction of tissue regeneration: Engineering cell-invasion characteristics

Synthetic hydrogels have been molecularly engineered to mimic the invasive characteristics of native provisional extracellular matrices: a combination of integrin-binding sites and substrates for matrix metalloproteinases (MMP) was required to render the networks degradable and invasive by cells via cell-secreted MMPs. Degradation of gels was engineered starting from a characterization of the degradation kinetics (kcat and Km) of synthetic MMP substrates in the soluble form and after crosslinking into a 3D hydrogel network. Primary human fibroblasts were demonstrated to proteolytically invade these networks, a process that depended on MMP substrate activity, adhesion ligand concentration, and network crosslinking density. Gels used to deliver recombinant human bone morphogenetic protein-2 to the site of critical defects in rat cranium were completely infiltrated by cells and remodeled into bony tissue within 4 wk at a dose of 5 μg per defect. Bone regeneration was also shown to depend on the proteolytic sensitivity of the matrices. These hydrogels may be useful in tissue engineering and cell biology as alternatives for naturally occurring extracellular matrix-derived materials such as fibrin or collagen.
THE EFFECTS OF CONCRETE-REPRESENTATIONAL-ABSTRACT SEQUENCED INSTRUCTION ON STRUGGLING LEARNERS ACQUISITION, RETENTION, AND SELF-EFFICACY OF FRACTIONS

lessons allowed the students to make sense of the information and thoroughly learn the operations, therefore being able to solve the operations, even after the completion of instruction. A third explanation is that students in the CRA instructional group benefited from direct and explicit instruction associated with the CRA sequenced instruction. Student learning may have been scaffolded as the teachers released control to the students. The scripted lessons provided purposeful modeling of skills before students interacted with the teacher with guided practice. As students performed the computations correctly, more instructional release was given to the students as they participated in independent practice. It may have been the acts of perfect practice performed by students in the CRA group that contributed to mastery and fluency of skills. In contrast, students in the traditional instruction group received less modeling and guided practice, requiring students to create meaning and potentially learn through trial by error instead of mastery by accurate practice. Accordingly, students from both groups might be able to perform better on immediate measures of performance but unable to produce equal results without mastery of skills. Results from this research corroborate findings from previous research that support CRA sequenced instruction for students who are at risk of failure in mathematics. The results of this study add to a growing body of research analyzing intervention supports for student learning of prerequisite skills to algebra, specifically the CRA sequence of teaching middle school students fractions. The results from this study differ from previous research, however, in that results from previous research demonstrated changes in initial student learning of material and does not address students’ retention of learned information. In this study, initial student performance did