Miracle-dz/UniFAR-FTUs huggingface dataset:
A Solution to the Doublet-Triplet Splitting Problem in the Type IIB Supergravity

The doublet{triplet mass splitting problem is one of the most serious problems in supersymmetric grand unied theories (GUTs). A class of models based on a product gauge group, such as the SU(5)GUTU(3)H or the SU(5)GUTU(2)H, realize naturally the desired mass splitting that is protected by an unbroken R symmetry. It has been pointed out that various features in the models suggest that these product-group unication models are embedded in a supersymmetric brane world. We show an explicit construction of those models in the supersymmetric brane world based on the Type IIB supergravity in ten dimensions. We consider T 6 =(Z12 Z2) orientifold for the compactied six extra dimensions. We nd that all of the particles needed for the GUT-symmetry-breaking sector are obtained from the D-brane fluctuations. The three families of quarks and leptons are introduced at an orbifold singularity, although their origin remains unexplained. This paper includes extensive discussion on anomaly cancellation in a given orbifold geometry. Relation to the Type IIB string theory, realization of R symmetry as a rotation of extra-dimensional space, and eective superpotential at low energies are also discussed.
A new approach in the theory of ordinary differential equations with small parameter

A topological degree based averaging principle has been proposed by J. Mawhin in his PhD thesis [J. Mawhin, Le Probleme des Solutions Periodiques en Mecanique non Lineaire, These de doctorat en sciences, Universite de Liege, 1969]. In the present paper we extend this result for the case when the unperturbed system is nonlinear but possesses a set of T-periodic solutions whose initial conditions constitute a boundary of some open set.
The Mediating Effect of Emotion Inhibition and Emotion Regulation Between Adolescents’ Perceived Parental Psychological Control and Depression

The purpose of this study is to investigate the relationship between parental psychological control (PPC) as perceived by adolescents, emotion inhibition, emotion regulation, and depression in adolescents using a structural equation model. The participants were 316 middle school students in metropolitan areas of South Korea who completed the Domain-Specific Assessment of Psychological Control (DAPC), Children’s Sadness Management Scale (CSMS) and Children’s Anger Management Scale (CAMS), and the Center for Epidemiological Studies–Depression Scale (CES-D) survey. Data were analyzed using descriptive statistics, correlation analysis, structural model verification, and bootstrap analysis. The results of this study indicated that PPC had a direct effect on depression. In addition, PPC had a significant effect on the emotion inhibition of dysfunctional emotion regulation, and emotion inhibition significantly affected depression. Therefore, emotion inhibition had a mediating effect between PPC and depression. Finally, this study’s implications and limitations are presented.
Modeling TSP with Particle Swarm Optimization and Genetic Algorithm

Traveling Salesman Problem (TSP) is a classical problem of optimization for researchers and its modeling is of great interest for Engineering, Operations Research and Computer Science. For solving TSP, many methods have been proposed, including heuristic ones. Our work extends the hybrid model, based on Particle Swarm Optimization, Genetic Algorithms and Fast Local Search, for the symmetric blind travelling salesman problem proposed by Thiago R. Machado and Heitor S. Lopes. We have replaced the fast local search with mutation to avoid the overhead for finding optimal solution. We have also replaced the one point crossover with uniform crossover as one point crossover is found to generate invalid tours in most of the cases. We argue that this model is more efficient as compared to the model proposed by Thiago R. Machado and Heitor S. We implement a prototype of the model and show its feasibility.
CoreTSAR: Task Scheduling for Accelerator-aware Runtimes

Heterogeneous supercomputers that incorporate computational accelerators such as GPUs are increasingly popular due to their high peak performance, energy efficiency and comparatively low cost. Unfortunately, the programming models and frameworks designed to extract performance from all computational units still lack the flexibility of their CPU-only counterparts. Accelerated OpenMP improves this situation by supporting natural migration of OpenMP code from CPUs to a GPU. However, these implementations currently lose one of OpenMP’s best features, its flexibility: typical OpenMP applications can run on any number of CPUs. GPU implementations do not transparently employ multiple GPUs on a node or a mix of GPUs and CPUs. To address these shortcomings, we present CoreTSAR, our runtime library for dynamically scheduling tasks across heterogeneous resources, and propose straightforward extensions that incorporate this functionality into Accelerated OpenMP. We show that our approach can provide nearly linear speedup to four GPUs over only using CPUs or one GPU while increasing the overall flexibility of Accelerated OpenMP.
Deliver Multicast Traffic over VPLS Domain Using Aggregated Multicast Trees

VPLS multicast has always been the focus in today’s network research. But implementing multicast in VPLS is confronted with two major problems, one is the multicast forwarding states that routers must maintain and manage, the other is the bandwidth waste when using aggregate shared tree to deliver multicast traffic. In this paper, we propose a trade-off approach that can be used to reduce the multicast forwarding states while at the same time keep the bandwidth utilized efficiently. We start our proposal with the set up of aggregate default tree, from it we derived all kinds of candidate shared multicast trees. We also suggest group–tree selection algorithm. Our simulation result shows that the proposed approach can achieve better scalability but introduce little overhead in VPLS network.
MACHINE LEARNING APPROACH FOR CONTEXT-SENSITIVE ERROR DETECTION

Context-sensitive spelling errors are those errors resulting from mistyping or mispronouncing a word, and the resulting misspelled word is a valid language/dictionary word. For example, “This building is bigger then our building”: The word ‘then’ here is a context-sensitive spelling error and the intended word is ‘than’. This paper describes an effective approach for detecting context-sensitive spelling errors. Detecting and correcting context-sensitive spelling errors is a very difficult and important problem that needs careful consideration. Working with this problem will involve facing the very difficult problem of natural language semantics. The proposed approach is a machine-learning-based approach. The approach has been fully implemented and evaluated with a large number of experiments. The results reported in this paper are encouraging and show that the method is effective. Overall, the method is capable of detecting context-sensitive errors with an accuracy in the range of ~86% ~95%.
Developing a Brief Behavior Rating Scale for Progress Monitoring of Depression in School Settings

Frequent formative assessment of students’ functioning, or progress monitoring, is a critical component of multi-tiered systems of support as data inform data-driven decisions about response to treatment. Progress monitoring tools for students’ academic and behavioral functioning are readily available and widely researched; however, despite the documented prevalence of depressive disorders among youth and that schools have been put forth as an ideal location for the delivery of mental health services, there are currently no progress monitoring tools to examine students’ response to interventions that target depression. To address this gap, this study sought to develop a progress monitoring assessment of students’ depressive symptoms using an empirically informed model for creating Brief Behavior Rating Scales (BBRS). Using this model, a four-item BBRS of depressive symptoms (BBRS-D) was created from the item pools of the Beck Depression Inventory for Youth (BDI-Y) and Children’s Depression Inventory (CDI) administered during a treatment study of depression in female youth; the resulting short scale corresponds well to the full-length assessments (i.e., r = .65 and r = .59); however, the BBRS-D possessed lower than adequate internal consistency (α = .50) and test–retest reliability (r = .56). Limitations and future directions are discussed.
Edge-aware image graph expansion methods for oversampled graph Laplacian matrix

Graph signals can represent high-dimensional data effectively and images can also be viewed as signals on weighted graphs by connecting the pixels with their neighboring ones. Recently, we proposed the graph oversampling methods for signal processing on graphs that appends nodes and links to the original graph to obtain an oversampled graph Laplacian matrix. In this paper, we consider new over-sampling methods of image graphs. By using the graph oversampling, we can make a bipartite graph that considers rectangular and diagonal connections simultaneously, while it cannot be realized by conventional critically sampled bipartite graphs. Furthermore, expanding the graphs according to the edge information enables us to decompose the image with the edge-preserving property. We perform the critically sampled graph filter bank on the oversampled graph and show that the proposed method outperforms other transforms, including the critically sampled graph filter banks and the graph Laplacian pyramid, in non-linear approximation and denoising experiments.
Correlating Social Interactions to Release History during Software Evolution

In this paper, we propose a method to reason about the nature of software changes by mining and correlating discussion archives. We employ an information retrieval approach to find correlation between source code change history and history of social interactions surrounding these changes. We apply our correlation method on two software systems, LSEdit and Apache Ant. The results of these exploratory case studies demonstrate the evidence of similarity between the content of free-form text emails among developers and the actual modifications in the code. We identify a set of correlation patterns between discussion and changed code vocabularies and discover that some releases referred to as minor should instead fall under the major category. These patterns can be used to give estimations about the type of a change and time needed to implement it.
INTRACLUSTER MEDIUM ENTROPY PROFILES FOR A CHANDRA ARCHIVAL SAMPLE OF GALAXY CLUSTERS

We present radial entropy profiles of the intracluster medium (ICM) for a collection of 239 clusters taken from the Chandra X-ray Observatory's Data Archive. Entropy is of great interest because it controls ICM global properties and records the thermal history of a cluster. Entropy is therefore a useful quantity for studying the effects of feedback on the cluster environment and investigating any breakdown of cluster self-similarity. We find that most ICM entropy profiles are well fitted by a model which is a power law at large radii and approaches a constant value at small radii: K(r) = K0 + K100(r/100 kpc)α, where K0 quantifies the typical excess of core entropy above the best-fitting power law found at larger radii. We also show that the K0 distributions of both the full archival sample and the primary Highest X-Ray Flux Galaxy Cluster Sample of Reiprich (2001) are bimodal with a distinct gap between K0 ≈ 30–50 keV cm2 and population peaks at K0 ∼ 15 keV cm2 and K0 ∼ 150 keV cm2. The effects of point-spread function smearing and angular resolution on best-fit K0 values are investigated using mock Chandra observations and degraded entropy profiles, respectively. We find that neither of these effects is sufficient to explain the entropy-profile flattening we measure at small radii. The influence of profile curvature and number of radial bins on best-fit K0 is also considered, and we find no indication that K0 is significantly impacted by either. For completeness, we include previously unpublished optical spectroscopy of Hα and [N ii] emission lines discussed in Cavagnolo et al. (2008a). All data and results associated with this work are publicly available via the project Web site.
Designing of a Sensor Network Monitoring Server

In this paper, we present a network server that uses AJAX and VML technology to broadcast multiple sensor data in real time, all sensor data are also recorded in a SQL database. The lower layer software collect sensor data through different interface including RS232, 485, network and wireless network, then convert different sensor data into a XML format and send them to the server. User can choose a suitable GUI module (pie, curve, LED, etc) and configure measure range and node information for each sensor. To show flexibility and adaptability of this server, we implemented a WSN application: a temperate and humidity monitoring system over 100 nodes for a granary. The monitoring data are simultaneously refreshed in a PC connected to the server through internet. The server has better performance than traditional way, especially in the case of lot of measure nodes with high measure frequency.
Addressing fidelity within complex health behaviour change interventions: A protocol of a scoping review of intervention fidelity frameworks and models.

Intervention fidelity is crucial to facilitate accurate interpretation of research outcomes, but has been inadequately addressed within complex health behaviour change interventions. Recent research has highlighted a need for practical guidance to improve understanding and use of existing fidelity frameworks and models within complex health behaviour change intervention research. The aim of this paper is to present a protocol for a scoping review of existing intervention fidelity frameworks and models. In accordance with scoping review guidelines, the following stages will be conducted: (1) identifying the research question, (2) identifying potentially relevant studies of fidelity frameworks and models, (3) study screening and selection, (4) charting and extracting data from identified frameworks and models, (5) collating, summarising and reporting the results and (6) consultation with stakeholders. Two reviewers will independently conduct the screening and extraction stages. Identified frameworks will be collated, summarized and categorized iteratively by one reviewer in consultation with the review team. The findings of this review will provide a useful resource by identifying and comparing existing fidelity frameworks and models. It is intended that increased clarity and understanding in this area will facilitate the appropriate selection and application of fidelity frameworks for complex health behaviour change interventions, inform areas for future research, and ultimately contribute towards improving how intervention fidelity is addressed in this area.
Online learning of inverse dynamics via Gaussian Process Regression

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. This paper proposes an approach for online learning of the inverse dynamics model using Gaussian Process Regression. The Sparse Online Gaussian Process (SOGP) algorithm is modified to allow for incremental updates of the model and hyperparameters. The influence of initialization on the performance of the learning algorithms, based on any a-priori knowledge available, is also investigated. The proposed approach is compared to existing learning and fixed control algorithms and shown to be capable of fast initialization and learning rate.
Thymbra capitata Essential Oil Use to Preserve Physicochemical and Microbiological Qualities of Pomegranate Juice

This work studied the possible use of Thymbra capitata essential oil in the preservation of pomegranate juice qualities. We monitored the effect of the oil on the physicochemical and microbiological qualities of the juice during 245 days of storage at 5 and 20 °C. Treated juices showed slight decrease of pH, total sugars and phenolic contents after 245 days of storage. Additionally, the essential oil had a significant effect on the juice microbiological quality and caused a decrease of bacterial count after the first day of storage. In conclusion, Thymbra capitata essential oil may be used as natural preservative in pomegranate juice to preserve its physicochemical and microbiological qualities in a long-term storage.
Hailstorm : Distributed Stream Processing with Exactly Once Semantics CS 240 h Final Project , Spring 2014

In recent years, stream processing has emerged as a data analysis technique to handle real-time applications where the latency of Hadoop is unacceptable. Many popular systems, such as Twitter’s Storm, provide a rigid platform for performing distributed computations over the network. Stormlike systems typically provide at-least-once processing with state management left to the implementor. We present a novel distributed stream processing framework, Hailstorm1, which is written in Haskell and provides a platform to perform distributed computation on streams of data. By restricting the class of computation to commutative monoids, our system is able to provide exactly-once semantics with little performance loss or added complexity.
State of the Art of Online Monitoring and Control of the Coagulation Process

Coagulation is an essential process for the removal of suspended and colloidal material from water and wastewater. However, no comprehensive or universally accepted mathematical description of the process has been developed so far. Therefore, process optimization and control is usually based on data from jar tests and simple flow-proportional dosing concepts, while more accurate concepts based on water quality parameters that can be measured online are emerging. In addition, there have been attempts to develop software sensors and control schemes that involve advanced mathematical analyses of these parameters. The paper presents an overview of the parameters and physical sensors that are used for feed-forward and feed-backward control schemes and the experiences that have been made with their implementation. Moreover, the development and use of software sensors is described. Finally, the practical applications of different control techniques are given in order to illustrate the state of the art of coagulation control. Some thoughts about research needs conclude this review.
Disambiguating word sentiment polarity through Bayesian modeling and opinion-level features

Many opinion words carry different polarity in different context, posing huge challenges to sentiment analysis research. Previous work on contextual polarity disambiguation makes use of term-level context such as word and patterns, and resolves the polarity with patternbased methods, PMI-based statistical techniques and machine learning methods. The major shortcoming of all such approaches lies in that termlevel features are sometimes ineffective in resolving the polarity. In this work, opinion-level features are studied and a Bayesian model is designed to disambiguate word sentiment polarity. Experiments with Opinmine corpus show that the opinion-level Bayesian model achieves significant performance gain in word polarity disambiguation in two domains.
Engineered stem cell niche matrices for rotator cuff tendon regenerative engineering

Rotator cuff (RC) tears represent a large proportion of musculoskeletal injuries attended to at the clinic and thereby make RC repair surgeries one of the most widely performed musculoskeletal procedures. Despite the high incidence rate of RC tears, operative treatments have provided minimal functional gains and suffer from high re-tear rates. The hypocellular nature of tendon tissue poses a limited capacity for regeneration. In recent years, great strides have been made in the area of tendonogenesis and differentiation towards tendon cells due to a greater understanding of the tendon stem cell niche, development of advanced materials, improved scaffold fabrication techniques, and delineation of the phenotype development process. Though in vitro models for tendonogenesis have shown promising results, in vivo models have been less successful. The present work investigates structured matrices mimicking the tendon microenvironment as cell delivery vehicles in a rat RC tear model. RC injuries augmented with a matrix delivering rat mesenchymal stem cells (rMSCs) showed enhanced regeneration over suture repair alone or repair with augmentation, at 6 and 12-weeks post-surgery. The local delivery of rMSCs led to increased mechanical properties and improved tissue morphology. We hypothesize that the mesenchymal stem cells function to modulate the local immune and bioactivity environment through autocrine/paracrine and/or cell homing mechanisms. This study provides evidence for improved tendon healing with biomimetic matrices and delivered MSCs with the potential for translation to larger, clinical animal models. The enhanced regenerative healing response with stem cell delivering biomimetic matrices may represent a new treatment paradigm for massive RC tendon tears.
The Effect of Attentional Focus Instructions on Golf Swing Performance in Recreational Golfers Defense Committee and Final Reading Approvals of the Thesis Submitted By

The following individuals read and discussed the thesis submitted by student Jeffrey D. Eggleston, and they evaluated his presentation and response to questions during the final oral examination. They found that the student passed the final oral examination. iv ACKNOWLEDGEMENTS I would like to thank my advisors Dr. Eric Dugan and Dr. Laura Petranek for aiding me in finishing this thesis. Without their help, none of this would have been possible. Secondly, I also want to think my family for supporting me throughout this entire process. Even though this thesis took me away from them, they continued supporting me knowing the good that would come from this. Lastly, I would like to thank all of the interns and staff at COBR who helped me pilot test data collections and ensure things were running as smoothly as possible. v ABSTRACT Previous research has addressed the effectiveness of attentional focus instructions in improving golf performance with a single training session. The purpose of the current study was to investigate the effect of external (EF) attentional focus instructions on recreational golfers' performance over a distributed training period and extended retention interval. Performance was measured by club head velocity (CHV) and X-factor as both have been correlated with greater performance. The current study extends the work of An, Wulf, and Kim (2013), by increasing the training period and retention interval. It was hypothesized EF group would have greater CHV and X-factor measurements during the training and retention interval compared to a control (C) group. Repeated measures ANCOVA tested for significant differences in CHV and X-factor measures between EF and C groups. No significant main effects (time or group) or interactions were found during the training period or retention tests for either CHV or X-factor. Future studies should determine if the cue used in the current study was appropriate for eliciting an improvement in performance, or if different components of the swing need to be emphasized for greater performance improvements.