Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling Frequency and the Comparison between Conformable Matched Model and Hedonic Regression Price Indexes

Matched Model price indexes are generally thought to over-estimate the quality-adjusted price level. This bias stems from the fact that only a fraction of the models are sold in consecutive sampling periods and that the price/performance ratio of these models is worse than that of new models. The "unrepresentativeness" of the sample used to construct such indexes could potentially be reduced by obtaining higher frequency data, thus increasing the fraction of models that are matched. We propose a set of matched model price indexes that is conformable with regression based price indexes in order to test this hypothesis. Using computer prices from the Buy Direct press we find that the bias in the matched model price index increases with the sampling frequency. This result suggests that models that last for only a brief time are models for which the price/performance ratio has deteriorated very rapidly. Thus, increasing the sampling frequency actually increases the selection bias.
View Selection Strategies for Mult i-View , Wide-Baseline Stereo MS-CIS-94-18 GRASP LAB 373

Recovering 3D depth information from two or more 2D intensity images is a long standing problem in the computer vision community. This paper presents a multi-baseline, coarse-to-fine stereo algorithm which utilizes any number of images (more than 2) and multiple image scales to recover 3D depth information. Several "view-selection strategies" are introduced for combining information across the multi-baseline and across scale space. The control strategies allow us to exploit, maximally, the benefits of large and small baselines and mask sizes while minimizing errors. Results of recovering 3D depth information from a human head are presented. The resulting depth maps are of good accuracy with a depth resolution of approximately 5mm.
Next Generation Wearable Devices: Smart Health Monitoring Device and Smart Sousveillance Hat using Device to Device (D2D) Communications in LTE Assisted Networks

Next generation wearable devices such as Smart health monitoring device and Smart Sousveillance hat, are capable of using wearable sensors for measuring physiological information, sousveillanace, navigation, as well as smart device to smart device communications. Smart health monitoring device collect and observe different health related information deploying biosensors and can predict health problems. Smart Sousveillance hat provides the brainwaves based fatigue state, training and sousveillance around the wearer. These wearable smart devices deploy the device to device communications in LTE assisted networks with D2D server and D2D Application server for spectrum utilization and cellular coverage, which make them portable, social, commercial and sustainable. Thus, the wearable device technology will merge with the smart communications besides the health and wellness. The simulation shows that LTE-D2D communications provides two times more energy efficiency than LTE cellular communications and significant LTE-D2D data rate with D2D-SINR and relative mobility of D2D devices.
A Light-weight & Robust System for Clinical Concept Disambiguation

This paper presents a system for the normalization of concept mentions in clinical narratives. We evaluate and compare it against a popular, open-source solution that is frequently used for natural language processing of clinical text. The evaluation is based on a manually annotated dataset of 72 discharge summaries taken from the i2b2-corpus. Besides the demonstration and evaluation of our system we provide an in-depth corpus analysis that guided the development of the system. Our focus lies on the task of concept disambiguation, for which we combine two unsupervised approaches that are easy to implement and computationally inexpensive. We show that some ambiguities can only be resolved by adapting to annotation guidelines and preferences which we solve via the introduction of heuristics. Finally, we present an online-demo that gives insights into the individual parts of the normalization pipeline.
Alterations in the ventral attention network during the stop-signal task in children with attention-deficit/hyperactivity disorder: an ERP source imaging study

Objective: Deficits in response inhibition figure prominently in models of ADHD; however, attentional deficiencies may better explain previous findings of impaired response inhibition in ADHD. We tested this hypothesis at the neurophysiological level. Methods: Dense array ERPs were obtained for 46 children with ADHD and 51 controls using the stop-signal task. Early and late components were compared between groups. N2 and P3 components were localized with LAURA distributed linear inverse solution. Results: A success-related N1 modulation was only apparent in the ADHD group. N2 and P3 amplitudes were reduced in ADHD. During the successful inhibition N2, the ADHD group showed reduced activation in right inferior frontal gyrus (rIFG), supplementary motor area (SMA) and right temporoparietal junction (rTPJ), and during failed inhibition in the rIFG. During the successful inhibition P3, reduced activation was found in anterior cingulate cortex (ACC) and SMA. Conclusion: Impairments in the ventral attention network contribute to the psychopathology of ADHD and challenge the dominant view that ADHD is underpinned by impaired inhibitory control. ERP sources of stopping in ADHD
Activation of the SHP-1 Phosphatase in Macrophages through cAMP-Dependent Blocks Induction of Bactericidal Nitric Oxide Adenylate Cyclase Toxin Bordetella pertussis

The adenylate cyclase toxin–hemolysin (CyaA) plays a key role in the virulence of Bordetella pertussis. CyaA penetrates complement receptor 3–expressing phagocytes and catalyzes uncontrolled conversion of cytosolic ATP to the key second messenger molecule cAMP. This paralyzes the capacity of neutrophils and macrophages to kill bacteria by complement-dependent oxidative burst and opsonophagocytic mechanisms. We show that cAMP signaling through the protein kinase A (PKA) pathway activates Src homology domain 2 containing protein tyrosine phosphatase (SHP) 1 and suppresses production of bactericidal NO in macrophage cells. Selective activation of PKA by the cell-permeable analog N 6 -benzoyladenosine-3 9 ,5 9 -cyclic monophosphate interfered with LPS-induced inducible NO synthase (iNOS) expression in RAW264.7 macrophages, whereas inhibition of PKA by H-89 largely restored the production of iNOS in CyaA-treated murine macrophages. CyaA/cAMP signaling induced SHP phosphatase–dependent dephosphorylation of the c-Fos subunit of the transcription factor AP-1 and thereby inhibited TLR4-triggered induction of iNOS gene expression. Selective small interfering RNA knockdown of SHP-1, but not of the SHP-2 phosphatase, rescued production of TLR-inducible NO in toxin-treated cells. Finally, inhibition of SHP phosphatase activity by NSC87877 abrogated B. pertussis survival inside murine macrophages. These results reveal that an as yet unknown cAMP-activated signaling pathway controls SHP-1 phosphatase activity and may regulate numerous receptor signaling pathways in leukocytes.
Green IGP link weights for energy-efficiency and load-balancing in IP backbone networks

The energy consumption of backbone networks has become a primary concern for network operators and regulators due to the pervasive deployment of wired backbone networks to meet the requirements of bandwidth-hungry applications. While traditional optimization of IGP link weights has been used in IP based load-balancing operations, in this paper we introduce a novel link weight setting algorithm, the Green Load-balancing Algorithm (GLA), which is able to jointly optimize both energy efficiency and load-balancing in backbone networks. Such a scheme can be directly applied on top of existing link sleeping techniques in order to achieve substantially improved energy saving gains. The contribution is a practical solution that opens a new dimension of energy efficiency optimization, but without sacrificing traditional traffic engineering performance in plain IP routing environments. In order to evaluate the efficiency of the proposed optimization scheme without losing generality, we applied it to a set of recently proposed but diverse algorithms for link sleeping operations in the literature. Evaluation results based on the European academic network topology, GÉANT, and its real traffic matrices show that GLA can achieve significantly improved energy efficiency compared to the original standalone algorithms, while also maintaining near-optimal load-balancing performance.
Universalization of narrow methods: Case study on autoencoders

The problem of bridging the gap between efficient but narrow methods of machine learning, and universal but inefficient methods was considered. Our main claim, which is methodologically important to the field of Artificial General Intelligence (AGI), is that neither narrow nor basic universal methods are sufficient for AGI. This claim was illustrated on example of pattern recognition task using stacked autoencoders and their two extensions with more exhaustive search and richer solution space. These three types of classifiers were evaluated on the base of a criterion that account for both error rate and training time. Depending on the urgency of the task to be solved, less or more universal methods appeared to be better. Thus, AGI might start with narrow methods, but should be able to perform their “universalization” (i.e. extension of the model space possibly up to Turing-complete space if it is appropriate in a certain situation).
Inhaled dsRNA and rhinovirus evoke neutrophilic exacerbation and lung expression of thymic stromal lymphopoietin in allergic mice with established experimental asthma

Rhinovirus infection or dsRNA stimulation increased thymic stromal lymphopoietin (TSLP), an upstream pro‐allergic cytokine, in asthmatic bronchial epithelial cells. We hypothesized that dsRNA challenges superimposed on established experimental allergic asthma constitute a useful exacerbation model. We further hypothesized that TSLP is induced at dsRNA‐ and rhinoviral infection‐induced exacerbations.
Automated Image Analysis of the Host-Pathogen Interaction between Phagocytes and Aspergillus fumigatus

Aspergillus fumigatus is a ubiquitous airborne fungus and opportunistic human pathogen. In immunocompromised hosts, the fungus can cause life-threatening diseases like invasive pulmonary aspergillosis. Since the incidence of fungal systemic infections drastically increased over the last years, it is a major goal to investigate the pathobiology of A. fumigatus and in particular the interactions of A. fumigatus conidia with immune cells. Many of these studies include the activity of immune effector cells, in particular of macrophages, when they are confronted with conidia of A. fumigus wild-type and mutant strains. Here, we report the development of an automated analysis of confocal laser scanning microscopy images from macrophages coincubated with different A. fumigatus strains. At present, microscopy images are often analysed manually, including cell counting and determination of interrelations between cells, which is very time consuming and error-prone. Automation of this process overcomes these disadvantages and standardises the analysis, which is a prerequisite for further systems biological studies including mathematical modeling of the infection process. For this purpose, the cells in our experimental setup were differentially stained and monitored by confocal laser scanning microscopy. To perform the image analysis in an automatic fashion, we developed a ruleset that is generally applicable to phagocytosis assays and in the present case was processed by the software Definiens Developer XD. As a result of a complete image analysis we obtained features such as size, shape, number of cells and cell-cell contacts. The analysis reported here, reveals that different mutants of A. fumigatus have a major influence on the ability of macrophages to adhere and to phagocytose the respective conidia. In particular, we observe that the phagocytosis ratio and the aggregation behaviour of pksP mutant compared to wild-type conidia are both significantly increased.
Computational Fluid Dynamic Approach for Biological System Modeling

Various biological system models have been proposed in systems biology, which are based on the complex biological reactions kinetic of various components. These models are not practical because we lack of kinetic information. In this paper, it is found that the enzymatic reaction and multi-order reaction rate is often controlled by the transport of the reactants in biological systems. A Computational Fluid Dynamic (CFD) approach, which is based on transport of the components and kinetics of biological reactions, is introduced for biological system modeling. We apply this approach to a biological wastewater treatment system for the study of metabolism of organic carbon substrates and the population of microbial. The results show that CFD model coupled with reaction kinetics is more accurate and more feasible than kinetic models for biological system modeling.
Learning the sparsity basis in low-rank plus sparse model for dynamic MRI reconstruction

Modeling a temporal image sequence as a super-position of sparse and low-rank component stems from studies in principal component pursuit (PCP). Recently this technique was applied for dynamic MRI reconstruction with two modifications. First, unlike the original PCP, the problem was to recover the image sequence from under-sampled measurements. Second, the sparse component of the signal was not sparse in itself but in a transform domain. Recent studies in dynamic MRI reconstruction showed that, instead of using a fixed sparsity basis, better recovery results can be achieved when the sparsifying dictionary is adaptively learned from the data using Blind Compressed Sensing (BCS) framework. In this work, we demonstrate that learning the sparsity basis using BCS like techniques improve the recovery accuracy from PCP when applied to dynamic MRI reconstruction problems.
Electric charge and magnetic flux on rotating black holes in a force-free magnetosphere

The electric charge on rotating black holes is calculated to be ~ BJ in the force-free configuration of Ghosh (2000), with a horizon flux of ~ BM^2. This charge is gravitationally weak for B ~ 10^{15} G, so that the Kerr metric applies. Being similar to the electric charge of a magnetar, both electric charge and magnetic flux should be, in sign and order of magnitude, continuous during stellar collapse into a black hole. Extraction of the rotational energy from newly formed black holes may proceed by interaction with the magnetic field. Keywords:black hole physics --magnetic fields
Home-grown teachers: Will their rural roots keep them in Virginia's rural schools?

HOME-GROWN TEACHERS: WILL THEIR RURAL ROOTS KEEP THEM IN VIRGINIA’S RURAL SCHOOLS? By Camilla M. Hodgson, Ph.D. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2010. Major Director: Jacqueline T. McDonnough Associate Professor, Department of Teaching and Learning The purpose of this research, based on Tonnies’s theory of Gemeinschaft and Durkheim’s theory of mechanical solidarity, was to determine if there were differences between rural Virginia teachers from rural areas and those from nonrural areas in their perceptions of salary, isolation, working conditions, NCLB requirements, and job satisfaction, as well as their plans to remain in rural schools. It also was to determine if there was a relationship between rural and nonrural backgrounds and the rural teachers’ intentions to remain employed in rural schools. Rural teachers from each of Virginia’s eight Superintendents’ Regions were selected and were asked to respond to an on-line survey. Their responses were collected and the data were analyzed using SPSS. The findings of this research showed that perceptions of isolation and working conditions were significant predictors of teachers’ plans to remain in rural schools. Although the findings of this research did not show rural origin to be significant, the concept of community that is inherent in ruralness was indicated by the significance
Model-Driven Evaluation of Software Architecture Quality Using Model Clone Detection

As software architecture methods and tools become increasingly model-driven, evaluating architecture artifacts must adjust correspondingly. Model-driven evaluation of architecture quality has advantages over traditional evaluation techniques, especially when applied in a model-driven context. One approach we found successful in performing model-driven analysis involves using model clone detection, whereby we detect subsystems that are similar to example systems that are positive and negative quality indicators. In this paper we present our ideas on applying model clone detection to realize model-driven evaluation of software architectures, which contain many high-level systems and interactions. We propose having model-based representations of architectural patterns and styles, and employing model clone detection to identify positive and negative architectural aspects for evaluation, including reliability and security. We provide our insights on how this research can be applied to popular architectural paradigms, relation to previous work, and present discussion points on how it will impact software architecture quality evaluation.
Local reference filter for life-long vision aided inertial navigation

Filter based system state estimation is widely used for hard-realtime applications. In long-term filter operation the estimation of unobservable system states can lead to numerical instability due to unbounded state uncertainties. We introduce a filter concept that estimates system states in respect to changing local references instead of one global reference. In this way unbounded state covariances can be reset in a consistent way. We show how local reference (LR) filtering can be integrated into filter prediction to be used in square root filter implementations. The concept of LR-filtering is applied to the problem of vision aided inertial navigation (LR-INS). The results of a simulated 24 h quadrotor flight using the LR-INS demonstrate longterm filter stability. Real quadrotor flight experiments show the usability of the LR-INS for a highly dynamic system with limited computational resources.
Heat Shock Protein B1-Deficient Mice Display Impaired Wound Healing

There is large literature describing in vitro experiments on heat shock protein (hsp)B1 but understanding of its function in vivo is limited to studies in mice overexpressing human hspB1 protein. Experiments in cells have shown that hspB1 has chaperone activity, a cytoprotective role, regulates inflammatory gene expression, and drives cell proliferation. To investigate the function of the protein in vivo we generated hspB1-deficient mice. HspB1-deficient fibroblasts display increased expression of the pro-inflammatory cytokine, interleukin-6, compared to wild-type cells, but reduced proliferation. HspB1-deficient fibroblasts exhibit reduced entry into S phase and increased expression of cyclin-dependent kinase inhibitors p27kip1 and p21waf1. The expression of hspB1 protein and mRNA is also controlled by the cell cycle. To investigate the physiological function of hspB1 in regulating inflammation and cell proliferation we used an excisional cutaneous wound healing model. There was a significant impairment in the rate of healing of wounds in hspB1-deficient mice, characterised by reduced re-epithelialisation and collagen deposition but also increased inflammation. HspB1 deficiency augments neutrophil infiltration in wounds, driven by increased chemokine (C-X-C motif) ligand 1 expression. This appears to be a general mechanism as similar results were obtained in the air-pouch and peritonitis models of acute inflammation.
Need for an efficient adult trap for the surveillance of dengue vectors

The emergence and re-emergence of arboviral diseases transmitted by Aedes aegypti and Ae. albopictus continue to be a major threat in the tropics and subtropics. Associations between currently used indices and dengue transmission have not been proven to be satisfactorily predictive of dengue epidemics. Classical larval indices in dengue surveillance have limited use in assessing transmission risk and are a poor proxy for measuring adult emergence. Besides, collection of larval indices is labour intensive and plagued by difficulties of access particularly in urban settings. The re-emergence of dengue disease in many countries despite lower immature indices has warranted the need for more effective indices in dengue vector surveillance and control. Reliable and highly useful indices could be developed with the help of efficient and appropriate entomological tools. Most current programmes emphasize reduction of immature Ae. aegypti density, but it is of little value because its relation to transmission risk is weak. More attention should be paid to methods directed toward adult rather than immature Ae. aegypti. Collection of sufficient numbers of adult mosquitoes is important to understand disease transmission dynamics and to devise an appropriate control strategy. Even though, use of certain traps such as BG-Sentinel traps has been attempted in monitoring Ae. aegypti population, their utility is limited due to various setbacks which make these insufficient for entomological and epidemiological studies. Thus, there is an urgent need for the development of an ideal trap that could be used for adult vector surveillance. The present review critically analyzes the setbacks in the existing tools of entomological surveillance of dengue vectors and highlights the importance and necessity of more improved, more sensitive and reliable adult trap that could be used for surveillance of dengue vectors.
Feature propagation on image webs for enhanced image retrieval

The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. In cases where the visual appearance of semantically similar images differs largely, feature histograms mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting them with features of related images. We establish image relations by image web construction and adapt a label propagation scheme from the domain of semi-supervised learning for feature augmentation. While the benefit of feature augmentation has been shown before, our approach refrains from the use of semantic labels. Instead we show how to increase the performance of the bag-of-features model substantially on a completely unlabeled image corpus.
On Computing Minimum Unsatisfiable Cores

Certifying a SAT solver for unsatisfiable instances is a computationally hard problem. Nevertheless, in the utilization of SAT in industrial settings, one often needs to be able to generate unsatisfiability proofs, either to guarantee the correctness of the SAT solver or as part of the utilization of SAT in some applications (e.g. in model checking). As part of the process of generating unsatisfiable proofs, one is also interested in unsatisfiable subformulas of the original formula, also known as unsatisfiable cores. Furthermore, it may by useful identifying theminimum unsatisfiable core of a given problem instance, i.e. the smallest number of clauses that make the instance unsatisfiable. This approach is be very useful in AI problems where identifying the minimum core is crucial for correcting the minimum amount of inconsistent information (e.g. in knowledge bases).