Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Visual-verbal Integration in Working Memory during Childhood and its Relation to Language and Reading

This Dissertation is brought to you for free and open access by the Department of Psychology at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Psychology Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact scholarworks@gsu.edu. Recommended Citation Smith, M. Katrina, "Development of Visual-verbal Integration in Working Memory during Childhood and its Relation to Language and Reading. " Dissertation, Georgia State University, 2005. https://scholarworks.gsu.edu/psych_diss/1 brought to you by CORE View metadata, citation and similar papers at core.ac. uk
Domain-Independent Construction of Pattern Database Heuristics for Cost-Optimal Planning

Heuristic search is a leading approach to domain-independent planning. For cost-optimal planning, however, existing admissible heuristics are generally too weak to effectively guide the search. Pattern database heuristics (PDBs), which are based on abstractions of the search space, are currently one of the most promising approaches to developing better admissible heuristics. The informedness of PDB heuristics depends crucially on the selection of appropriate abstractions (patterns). Although PDBs have been applied to many search problems, including planning, there are not many insights into how to select good patterns, even manually. What constitutes a good pattern depends on the problem domain, making the task even more difficult for domain-independent planning, where the process needs to be completely automatic and generaL We present a novel way of constructing good patterns automatically from the specification of planning problem instances. We demonstrate that this allows a domain-independent planner to solve planning problems optimally in some very challenging domains, including a STRIPS formulation of the Sokoban puzzle.
Computational studies of the shear flow behaviour of a model for nematic liquid crystalline polymers

We present an accurate and efficient numerical method to the solve the microstructure-flow problem that constitutes the coupling of the molecular-based Doi-Marrucci-Greco model to the Cauchy equation of motion and continuity equation. We also provide a general introduction to the problem of the flow of complex fluids. Preliminary investigations of the predictive capabilities of the Doi-Marrucci-Greco model show that with increasing shear rate, in accordance with both experimental observations and other theoretical predictions, the model exhibits three flow regimes: steady linear shear flow at low shear rate; steady roll cells at intermediate shear rates; and irregular flow and orientation patterns at high shear rates. Given sufficiently high shear rates, the irregular flow structure is accompanied by the formation of ? 1 and ? 1/2 strength disclinations. Furthermore, at shear rates large enough to inhibit the tumbling of the average molecular orientation, the solution no longer contains disclinations or roll cells, but retains some structure in the flow direction that has the visual appearance of stripes.
Biofilm formation of Klebsiella pneumoniae on urethral catheters requires either type 1 or type 3 fimbriae

Urinary catheters are standard medical devices utilized in both hospital and nursing home settings, but are associated with a high frequency of catheter-associated urinary tract infections (CAUTI). In particular, biofilm formation on the catheter surface by uropathogens such as Klebsiella pneumoniae causes severe problems. Here we demonstrate that type 1 and type 3 fimbriae expressed by K. pneumoniae enhance biofilm formation on urinary catheters in a catheterized bladder model that mirrors the physico-chemical conditions present in catheterized patients. Furthermore, we show that both fimbrial types are able to functionally compensate for each other during biofilm formation on urinary catheters. In situ monitoring of fimbrial expression revealed that neither of the two fimbrial types is expressed when cells are grown planktonically. Interestingly, during biofilm formation on catheters, both fimbrial types are expressed, suggesting that they are both important in promoting biofilm formation on catheters. Additionally, transformed into and expressed by a nonfimbriated Escherichia coli strain, both fimbrial types significantly increased biofilm formation on catheters compared with the wild-type E. coli strain. The widespread occurrence of the two fimbrial types in different species of pathogenic bacteria stresses the need for further assessment of their role during urinary tract infections.
Detecting Data Semantic: A Data Leakage Prevention Approach

Data leakage prevention systems (DLPSs) are increasingly being implemented by organizations. Unlike standard security mechanisms such as firewalls and intrusion detection systems, DLPSs are designated systems used to protect in use, at rest and in transit data. DLPSs analytically use the content and surrounding context of confidential data to detect and prevent unauthorized access to confidential data. DLPSs that use content analysis techniques are largely dependent upon data fingerprinting, regular expressions, and statistical analysis to detect data leaks. Given that data is susceptible to change, data fingerprinting and regular expressions suffer from shortcomings in detecting the semantics of evolved confidential data. However, statistical analysis can manage any data that appears fuzzy in nature or has other variations. Thus, DLPSs with statistical analysis capabilities can approximate the presence of data semantics. In this paper, a statistical data leakage prevention (DLP) model is presented to classify data on the basis of semantics. This study contributes to the data leakage prevention field by using data statistical analysis to detect evolved confidential data. The approach was based on using the well-known information retrieval function Term Frequency-Inverse Document Frequency (TF-IDF) to classify documents under certain topics. A Singular Value Decomposition (SVD) matrix was also used to visualize the classification results. The results showed that the proposed statistical DLP approach could correctly classify documents even in cases of extreme modification. It also had a high level of precision and recall scores.
A Hybrid Video-on-Demand Data Broadcasting and Receiving Scheme of Harmonic and Staggered Schemes

In providing video-on-demand (VoD) services to a number of clients through networks, the bandwidth requirements of video transmission restrict VoD services. For this reason, many significant broadcasting-based VoD schemes have been proposed to efficiently support services. However, the broadcasting-based VoD schemes approach needs frequency channel hopping, using many channels at the same time and managing many segments of a video. These make it difficult to implement. In this paper, we propose a harmonic staggered broadcasting scheme which has a simple structure and substantially improved VoD efficiency. The proposed scheme substantially reduces the number of segments required and the frequency for switching channels. Also this new scheme performs significantly better than the existing VoD schemes in viewer's waiting time and buffer requirements with simplicity. The numerical results demonstrate that the viewer's waiting time of the harmonic staggered broadcasting scheme is close to the harmonic broadcasting scheme and the buffer requirements of this can be adjusted by a video dividing coefficient h .
A multilevel analysis of the role of parental and community variables on young children's health.

OBJECTIVE The purpose of this study was to examine the associations among community characteristics, family economic adversity, parents' health beliefs, and parents' and children's health using cross-level mediation analyses. METHODS Survey data were collected from a nationally representative sample of 1,337 families with children between 3 and 6 years nested within 45 Trinidad and Tobago community clusters. RESULTS At the individual level, parents' health belief of internality was a significant mediator between family economic adversity, adult health, and children's health. In the cross-level mediation analyses, health belief of internality was a significant mediator between community health problems and children's health. There were between-community variations in the relationship between adult health, health belief of powerful others, and children's health. CONCLUSIONS There is strong evidence of the role of community and family influences on children's health. Findings indicate the need to use a multilevel analytical approach in studies of children's health.
Effect of bipolar pulsed radiofrequency on refractory chronic cervical radicular pain

Rationale: Despite undergoing transforaminal epidural steroid injection (TFESI), many patients complain of persisting cervical radicular pain. For the management of chronic cervical radicular pain, clinicians are widely applying pulsed radiofrequency (PRF) stimulation to dorsal root ganglions (DRGs). To enhance the effect of PRF stimulation, we conducted bipolar PRF stimulation in 2 patients with chronic cervical radicular pain that was refractory to monopolar PRF and repeated TFESIs. Patient concerns: Patients 1 and 2 presented with a numeric rating scale (NRS) score of 7 and 6 for chronic cervical radicular pain, respectively, despite undergoing monopolar PRF and 2 TFESIs. Diagnoses: On cervical magnetic resonance imaging, foraminal stenosis at the right C6–7 and right central to right foraminal disc protrusion on C6–7 were observed in patients 1 and 2, respectively. Two patients showed a positive response on diagnostic right C7 selective nerve root block with 0.5 mL of 1% lidocaine. Interventions: Bipolar PRF stimulation was performed under C-arm fluoroscopy. Two parallel RF cannulas (less than 1 cm apart) were used for DRG stimulation. The PRF treatment was administered at 5 Hz and a 5-ms pulsed width for 360 seconds at 45 V with the constraint that the electrode tip temperature did not exceed 42°C. Outcomes: At the 2-week and 1-month follow-up, after undergoing bipolar PRF, the pain of patient 1 was completely relieved, and at 2, 3, and 6 months, the pain was scored as NRS 2. In patient 2, at the 2-week follow-up after undergoing bipolar PRF, pain severity was reduced from NRS 6 to 2. The effect of bipolar PRF on patient 2 lasted for at least 6 months. No adverse effects were observed in either patient. Lessons: Application of bipolar PRF to DRGs seems to be an effective and safe technique for treating refractory chronic cervical radicular pain.
Different roles of proteolipids and 70‐kDa subunits of V‐ATPase in growth and death of cultured human cells

Background: The vacuolar‐type proton‐translocating adenosine triphosphatase (V‐ATPase) plays important roles in cell growth and tumour progression. V‐ATPase is composed of two distinct structures, a hydrophilic catalytic cytosolic sector (V1) and a hydrophobic transmembrane sector (V0). The V1 sector is composed of 5–8 different subunits with the structure A3B3C1D1E1F1G1H1. The V0 sector is composed of 5 different subunits with the structure 1161381191166. The over‐expression of 16‐kDa proteolipid subunit of V‐ATPase in the perinuclear region of the human adventitial fibroblasts promotes phenotypic modulation that contributes to neointimal formation and medial thickening. A relationship between oncogenicity and the expression of the 16‐kDa proteolipid has also been suggested in human pancreatic carcinoma tissue.
Decision Support System for Breast Cancer Diagnosis by a Meta-Learning Approach Based on Grammar Evolution

The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a low percentage of women who suffer it can be detected using mammography methods. Therefore, it is necessary to develop new strategies to detect its formation in early stages. Many machine learning techniques have been applied in order to help doctors in the diagnosis decision process, but its definition and application are complex, getting results which are not often the desired. In this article we present an automatic way to build decision support systems by means of the combination of several machine learning techniques using a Meta-learning approach based on Grammar Evolution (MGE). We will study its application over different mammographic datasets to assess the improvement of the results.
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens

In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits. We show how to derive lower confidence bounds required by our algorithm. Experimental comparisons to previously proposed active learning algorithms show superior performance on some standard UCI datasets.
Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

This work evaluates the performance of an image compression system based on wavelet-based subband decomposition and vector quantization. The images are decomposed using wavelet filters into a set of subbands with different resolutions corresponding to different frequency bands. The resulting subbands are vector quantized using the Linde-Buzo-Gray (LBG) algorithm and various fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive neural network through an unsupervised learning process. The quality of the multiresolution codebooks designed by these algorithms is measured on the reconstructed images belonging to the training set used for multiresolution codebook design and the reconstructed images from a testing set.
Development of Real-Time Screening System for Structural Surface Damage Using Object Detection and Generative Model Based on Deep Learning

Recently, assessing the integrity of the structures accurately and reliably has become extremely important in various fields in order to increase operational lifetime and improve safety. Detecting surface damage such as cracks, spalling and so on in evaluating the soundness of the structure is particularly important as it is one of the major factors causing deterioration and destruction of the structure. In this study, we develop a real-time screening system for structural surface damage by using object detection technique and generative model based on deep learning. The accuracy of object detection depends largely on the quality and quantity of images given as training data in advance. These images are not kept even by road management companies, etc., and tend to be short. In this study, we generate images of the crack using deep generative model and introduce them into object detection technique. Finally, we investigate whether the detection accuracy of damage is improved by introducing generated images as training data.
Pretreatment proliferation parameters do not add predictive power to clinical factors in cervical cancer treated with definitive radiation therapy.

PURPOSE To examine the prognostic value of tumor proliferation measurements in women with carcinoma of the uterine cervix. We report an update of a prospective study focusing on whether pretreatment proliferation parameters are associated with clinical outcome, relative to other established clinical factors. MATERIALS AND METHODS One hundred and one patients were recruited into the study from years 1991 to 1999. The LI for in vivo bromodeoxyuridine incorporation by the tumor and the potential doubling time (T(pot)) were determined by flow cytometry (fc). LI and its staining pattern were also assessed by immunohistochemistry (ih) using tissue sections. Apoptosis was assessed histologically using morphological criteria. Patients were treated with definitive radiation therapy. RESULTS A successful fc measurement for LI-fc and T(pot) was possible in 95 patients (94%). The median/mean LI-fc was 6.6/7.6% (range 1.4-36.1%), and for LI-ih, 10.8/11.5%. To date, 43 patients have died of disease, and the median follow-up for alive patients is 6.2 years (range 1.3-9.3 years). Among 88 patients who completely responded to treatment, 40 patients have relapsed (14 pelvic, 23 distant, and 3 pelvic and distant). In univariate analysis, the significant factors for adverse disease-free survival were large tumor size (P = 0.0001), low hemoglobin (P = 0.001), pelvic lymph node status (P = 0.004), stage (P = 0.013), and overall treatment time (P = 0.0008). In multivariate analysis, only tumor size, pelvic lymph node status, and overall treatment time remained significant for disease-free survival. LI-fc, LI-ih, T(pot), ploidy, pattern of bromodeoxyuridine staining, and apoptosis were not significantly associated with clinical outcome in univariate or multivariate analyses. CONCLUSIONS These mature data indicate that none of the pretreatment proliferation parameters have prognostic significance in the radical radiotherapy of carcinoma of the uterine cervix, despite the significance of overall treatment time for treatment outcome.
Elitist Artificial Bee Colony for constrained real-parameter optimization

A novel algorithm to solve constrained realparameter optimization problems, based on the Artificial Bee Colony algorithm is introduced in this paper. The operators used by the three types of bees (employed, onlooker and scout) are modified in such a way that more diverse and convenient solutions are generated. Furthermore, a dynamic tolerance control mechanism for equality constraints is added to the algorithm in order to facilitate the approach to the feasible region of the search space. Finally, two simple local search operators are applied to the best solution found so far. The algorithm, called Elitist-ABC, is tested on 18 test problems based on the experimental design proposed for the CEC'2010 competition on constrained real-parameter optimization. The results obtained are discussed and some conclusions are drawn.
A survey of formal business process verification : From soundness to variability

Formal verification of business process models is of interest to a number of application areas, including checking for basic process correctness, business compliance, and process variability. A large amount of work on these topics exist, while a comprehensive overview of the field and its directions is lacking. We provide an overview and critical reflections on existing approaches.
The neighborhood alcohol environment and alcohol-related morbidity.

AIMS The aims of this study were (1) to examine the association between neighborhood alcohol outlet density and individual self-reported alcohol-related health outcomes in the last year-sexually transmitted infections (STI), motor vehicle accidents, injury, liver problems, hypertension and experienced violence; (2) to determine whether the relationship between morbidity and alcohol outlet density is mediated by individual alcohol consumption; and (3) to explore the role of alcohol outlet density in explaining any observed racial and ethnic differences in morbidity. METHOD Hierarchical models from a random sample of Los Angeles, CA, and Louisiana residents (N = 2881) from 217 census tracts were utilized. The clustering of health and social outcomes according to neighborhood varied by health problem examined. RESULTS There was substantial clustering of STI (intraclass correlation coefficient, ICC = 12.8%) and experienced violence (ICC = 13.0%); moderate clustering of liver problems (ICC = 3.5%) and hypertension (ICC = 3.9%); and low clustering of motor vehicle accident (ICC = 1.2%) and injury (ICC = 1.4%). Alcohol outlet density was significantly and positively associated with STI (crude OR = 1.80, 95% CI = 1.10-3.00), liver problems (crude OR = 1.33, 95% CI = 1.02-1.75) and experienced violence (crude OR = 1.31, 95% CI = 1.13-1.51) although not with other morbidity outcomes. Mediation analyses of morbidity outcomes revealed partial mediation of individual alcohol consumption in the relationship between alcohol density and STI and violence, and full mediation for liver problems. CONCLUSIONS Findings support the concept that off-premise alcohol outlets in the neighborhood environment may impact health and social outcomes, either directly or indirectly, through individual alcohol consumption and these associations may be heterogeneous with respect to race and ethnicity.
A Novel Architecture for Predictive CyberSecurity Using Non-homogenous Markov Models

Evaluating the security of an enterprise is an important step towards securing its system and resources. However existing research provide limited insight into understanding the impact attacks have on the overall security goals of an enterprise. We still lack effective techniques to accurately measure the predictive security risk of an enterprise taking into account the dynamic attributes associated with vulnerabilities that can change over time. It is therefore critical to establish an effective cyber-security analytics strategy to minimize risk and protect critical infrastructure from external threats before it even starts. In this paper we present an integrated view of security for computer networks within an enterprise, understanding threats and vulnerabilities, performing analysis to evaluate the current as well as future security situation of an enterprise to address potential situations. We formally define a non-homogeneous Markov model for quantitative security evaluation using Attack Graphs which incorporates time dependent covariates, namely the vulnerability age and the vulnerability discovery rate to help visualize the future security state of the network leading to actionable knowledge and insight. We present experimental results from applying this model on a sample network to demonstrate the practicality of our approach.
Robust rule-based prediction

This paper studies a problem of robust rule-based classification, i.e., making predictions in the presence of missing values in data. This study differs from other missing value handling research in that it does not handle missing values but builds a rule-based classification model to tolerate missing values. Based on a commonly used rule-based classification model, we characterize the robustness of a hierarchy of rule sets as k-optimal rule sets with the decreasing size corresponding to the decreasing robustness. We build classifiers based on k-optimal rule sets and show experimentally that they are more robust than some benchmark rule-based classifiers, such as C4.5rules and CBA. We also show that the proposed approach is better than two well-known missing value handling methods for missing values in test data