Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) Improves Chest CT Image Quality and Reduces Radiation Exposure

Objective To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT). Methods Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe's test. Results At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA. Conclusion For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.
A generalized Cahn–Hilliard equation incorporating geometrically linear elasticity

We consider a generalisation of the Cahn-Hilliard equation that incorporates an elastic energy density which, being quasiconvex, incorporates microstructure formation on smaller length scales. We prove global existence of weak solutions in certain microstructural regimes in (one and) two dimensions and present sufficient conditions for uniqueness. First numerical computations to illustrate some characteristic properties of the solutions are presented and compared to earlier Cahn-Hilliard models with elasticity. 2010 Mathematics Subject Classification: Primary 35K55, 74B99, 74Q15; Secondary 35K40, 65M60, 74N99.
MAP-MRF approach for image denoising

Digital images gets degraded due to several reasons. The aim of denoising involves the restoration of signal to yield visually good quality representation. In this paper, we are referring to an image corrupted by additive Gaussian noise. The image is modeled as MRF and maximum-a-posteriori (MAP) estimate is obtained using graduated non-convexity technique. Results suggest that the proposed technique yields better compared to other techniques.
Labor Market Institutions, Wages, and Investment

Labor market institutions, via their effect on the wage structure, affect the investment decisions of firms in labor markets with frictions. This observation helps explain rising wage inequality in the US, but a relatively stable wage structure in Europe in the 1980s. These different trends are the result of different investment decisions by firms for the jobs typically held by less skilled workers. Firms in Europe have more incentives to invest in less skilled workers, because minimum wages or union contracts mandate that relatively high wages have to be paid to these workers. I report some empirical evidence for investments in training and physical capital across the Atlantic, which is roughly in line with this theoretical reasoning.
Transformational Leadership and Asian Indian Values: Duty, Selfless Service, and Nonviolence

As reports increase of scandals and unethical behavior in Western corporations and organizations, public faith in leadership has declined, indicating the need for a reevaluation of what makes a good leader. We propose a transformational style of ethical leadership, supplemented by traditional Indian values, for leadership that focuses on duty, selfless service, and nonviolence. We address Indian cultural values of dharma, karma–yoga, and ahimsa in reference to transformational leadership, highlighting the contributions of influential Indian leaders like Mohandas K. Gandhi. We describe the consonance of these values and counseling psychology ideals, as well as their applications for leadership practice, training, and research.
GATA6 Is a Crucial Regulator of Shh in the Limb Bud

In the limb bud, patterning along the anterior-posterior (A-P) axis is controlled by Sonic Hedgehog (Shh), a signaling molecule secreted by the "Zone of Polarizing Activity", an organizer tissue located in the posterior margin of the limb bud. We have found that the transcription factors GATA4 and GATA6, which are key regulators of cell identity, are expressed in an anterior to posterior gradient in the early limb bud, raising the possibility that GATA transcription factors may play an additional role in patterning this tissue. While both GATA4 and GATA6 are expressed in an A-P gradient in the forelimb buds, the hindlimb buds principally express GATA6 in an A-P gradient. Thus, to specifically examine the role of GATA6 in limb patterning we generated Prx1-Cre; GATA6(fl/fl) mice, which conditionally delete GATA6 from their developing limb buds. We found that these animals display ectopic expression of both Shh and its transcriptional targets specifically in the anterior mesenchyme of the hindlimb buds. Loss of GATA6 in the developing limbs results in the formation of preaxial polydactyly in the hindlimbs. Conversely, forced expression of GATA6 throughout the limb bud represses expression of Shh and results in hypomorphic limbs. We have found that GATA6 can bind to chromatin (isolated from limb buds) encoding either Shh or Gli1 regulatory elements that drive expression of these genes in this tissue, and demonstrated that GATA6 works synergistically with FOG co-factors to repress expression of luciferase reporters driven by these sequences. Most significantly, we have found that conditional loss of Shh in limb buds lacking GATA6 prevents development of hindlimb polydactyly in these compound mutant embryos, indicating that GATA6 expression in the anterior region of the limb bud blocks hindlimb polydactyly by repressing ectopic expression of Shh.
Adapting the polyhedral model as a framework for efficient speculative parallelization

In this paper, we present a Thread-Level Speculation (TLS) framework whose main feature is to be able to speculatively parallelize a sequential loop nest in various ways, by re-scheduling its iterations. The transformation to be applied is selected at runtime with the goal of minimizing the number of rollbacks and maximizing performance. We perform code transformations by applying the polyhedral model that we adapted for speculative and runtime code parallelization. For this purpose, we design a parallel code pattern which is patched by our runtime system according to the profiling information collected on some execution samples. Adaptability is ensured by considering chunks of code of various sizes, that are launched successively, each of which being parallelized in a different manner, or run sequentially, depending on the currently observed behavior for accessing memory. We show on several benchmarks that our framework yields good performance on codes which could not be handled efficiently by previously proposed TLS systems.
Color interpolation for single CCD color camera

The paper proposes a method to reduce the problems of blurred-edge effects and color alias effects in the color interpolation for a single charge-coupled device (CCD) color camera. We first introduce the background, and review some traditional interpolation methods. Then we explain our proposed methods as well as the results. Conclusions and future works are addressed.
‘Coping with the job’: prison staff responding to self-harm in three English female prisons: a qualitative study

Abstract This study considers how those who work in prisons are affected by and respond to repetitive self-harm of imprisoned women in English prisons. This paper considers the perspectives of custodial staff working in this area on a day-to-day basis. Semi-structured face-to-face interviews were conducted with 14 prison staff and explored using techniques of thematic analysis. The interviews examined: the emotional impact of working with and witnessing self-harm incidents, coping strategies used, training and the support available to prison staff. Findings indicate the strategies used by staff to cope emotionally with such incidents and these include presenting a ‘façade of coping’, rejecting support and becoming desensitised. It is concluded that staff felt they must portray themselves as coping well with self-harm in prison even when they were troubled and emotionally affected by it. However, some did describe accepting help when outside of the prison and this has implications for how support can be offered in the future. It is recommended that more should be done to support and train staff in this area.
A social bookmarking system to support cluster driven archival arrangement

Cultural heritage materials are increasingly being made available through standard search facilities. However, it is challenging to automatically organize these materials in a way that is well aligned with users' specific interests. We report on the development of a social bookmaking system to collect human annotations that are used to measure the performance of three different clustering algorithms. We find that there is a discrepancy between the latent structure present in the data and the clusters annotated by humans. However, it is difficult to detect such discrepancies explicitly.
Distributed Representations for Semantic Matching in non-factoid Question Answering

Users’ interactions with search engines is shifting towards more complex information needs and the need for a deeper semantic understanding of the query intent is needed. In this paper we propose a novel semantic matching criterion that adopts distributed representations of words in order to address complex information needs in a scalable way. We show that combining this criterion with other well established features it is possible to obtain over 22% improvement for MRR and 27% in P@1 over the best performing approach for answering non-factoid questions, a specific form of complex information need. Moreover we show that in our setting our criterion can substitute more complex linguistic feature.
Comparing pharmacologic classes in NDF-RT and SNOMED CT

Background: Clinical decision support systems and semantic mining require interoperable representations of pharmacologic classes across reference terminological systems. We explore two such systems: NDF-RT and SNOMED CT. Methods: We evaluate the overlap of pharmacologic classes in NDF-RT (VA Classes) and SNOMED CT. We compare classes based on the set of their members (drugs) across systems, using the Jaccard coecient as a measure of overlap between two classes. Results: There is a limited overlap among the two systems. The average Jaccard value is 0.293. Only 11.5% of the VA classes have a Jaccard value of 0.75 or above. Conclusions: The analysis of discrepancies between pharmacologic classes across systems oers a strategy for identifying classes in need of critical review. Due to the heterogeneity of the representation of pharmacologic classes in various terminologies, we recommend that drugs, not classes, be annotated in text for semantic mining purposes.
Understanding trends in blood pressure and their associations with body mass index in Chinese children, from 1985 to 2010: a cross-sectional observational study

Objective Understanding trends in blood pressure (BP) in childhood is crucial to addressing and reducing the burden of adulthood hypertension and associated mortality in the future. In view of growing obesity in Chinese children, we sought to investigate the trends in BP and the influence of body mass index (BMI) on them. Design, setting and participants We included 1 010 153 children aged 8–17 years, with completed records from a large national successive cross-sectional survey, the Chinese National Survey on Students’ Constitution and Health, between 1985 and 2010. Main outcome measurements BP was measured according to the recommendation of the National High Blood Pressure Education Program Working Group, and the elevated BP was based on sex-, age- and height-specific 95th centile of the recommendation. Results The adjusted mean systolic BP in boys and girls decreased by 3.9 and 5.6 mm Hg between 1985 and 2005, and increased by 1.3 and 1.0 mm Hg between 2005 and 2010, respectively. Corresponding adjusted prevalence of elevated systolic BP in boys and girls declined from 5.1% and 5.5% to 3.5% and 2.5% between 1985 and 2005, and increased to 4.9% and 3.5% in 2010, respectively. Adjusted mean BMI of boys and girls in 2010 was 2.0 and 1.2 kg/m2 higher than those in 1985, respectively. The prevalence of obesity rose from 0% to 3.4% in boys and 0.9% in girls. Further adjusting for BMI did not change these trends in systolic BP. A similar pattern was also observed in diastolic BP. Conclusions After declining for 20 years, BP levels in Chinese children started to climb upwards. These trends in BP cannot be fully explained by BMI. The investigation of other determinants of BP may provide additional opportunity to curb the current upward BP trend in Chinese children.
From subspace learning to distance learning: A geometrical optimization approach

In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection onto that subspace. A proper weighting of the two iterations enables to continuously interpolate between the problem of learning a subspace and learning a distance when the subspace is fixed.
Posttranslational Modification of Human Glyoxalase 1 Indicates Redox-Dependent Regulation

Background Glyoxalase 1 (Glo1) and glyoxalase 2 (Glo2) are ubiquitously expressed cytosolic enzymes that catalyze the conversion of toxic α-oxo-aldehydes into the corresponding α-hydroxy acids using L-glutathione (GSH) as a cofactor. Human Glo1 exists in various isoforms; however, the nature of its modifications and their distinct functional assignment is mostly unknown. Methodology/Principal Findings We characterized native Glo1 purified from human erythrocytes by mass spectrometry. The enzyme was found to undergo four so far unidentified posttranslational modifications: (i) removal of the N-terminal methionine 1, (ii) N-terminal acetylation at alanine 2, (iii) a vicinal disulfide bridge between cysteine residues 19 and 20, and (iv) a mixed disulfide with glutathione on cysteine 139. Glutathionylation of Glo1 was confirmed by immunological methods. Both, N-acetylation and the oxidation state of Cys19/20, did not impact enzyme activity. In contrast, glutathionylation strongly inhibited Glo1 activity in vitro. The discussed mechanism for enzyme inhibition by glutathionylation was validated by molecular dynamics simulation. Conclusion/Significance It is shown for the first time that Glo1 activity directly can be regulated by an oxidative posttranslational modification that was found in the native enzyme, i.e., glutathionylation. Inhibition of Glo1 by chemical reaction with its co-factor and the role of its intramolecular disulfides are expected to be important factors within the context of redox-dependent regulation of glucose metabolism in cells.
The blazar S5 0014+813: a real or apparent monster?

A strong hard X‐ray luminosity from a blazar flags the presenc e of a very powerful jet. If the jet power is in turn related to the mass accretion rate, the mo st luminous hard X‐ray blazars should pinpoint the largest accretion rates, and thus the la rgest black hole masses. These ideas are confirmed by the Swift satellite observations of the blazar S5 0014+813, at the red shift z = 3.366. Swift detected this source with all its three instruments, from th e optical to the hard X‐rays. Through the construction of its spectral energy distribution we are confident that its optical‐UV emission is thermal in origin. Associat ing it to the emission of a standard optically thick geometrically thin accretion disk, we find a black hole mass M � 4×10 10 M⊙, radiating at 40% the Eddington value. The derived mass is among the largest ever found. Super‐Eddington slim disks or thick disks with the presence of a collimating funnel can in principle reduce the black hole mass estimate, but tend to produce spectra bluer than observed.
Autopoiesic Content: A Conceptual Model for Enabling Situated Self-generative Content for Public Displays

The significant price drops in large LCD panels have led to a massive proliferation of digital public displays in public spaces. Most of these displays, however, simply show some form of traditional advertising, such as short commercials, animated presentations, or still images. Creating content that explicitly takes the particular location and surroundings of a space into account, in order to increase its relevance for passers-by, is typically infeasible due to the high costs associated with customized content. We argue that the concept of autopoiesic content (i.e., self-generative content) could significantly increase the local relevance of such situated public displays without requiring much customization efforts. As a sample application, this position paper outlines the concept and architecture of Funsquare, a large public display system that uses autopoiesic content to facilitate social interaction.
International Journal of Production Research Static Neural Network Process Models: Considerations and Case Studies S Tatic Neural Network Process Models: Considerations and Case Studies

Taylor & Francis makes every effort to ensure the accuracy of all the information (the " Content ") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. Neural networks are beginning to be used for the modelling of complex manufacturing processes, usually for process and quality control. Often these models are used to identify optimal process settings. Since a neural network is an empirical model, it is highly dependent on the data used in construction and validation. Using data directly from production ensures availability and ® delity, however, the samples may not re¯ ect the entire range of probable operation and, in particular, may not include the optimal process settings. Supplementing production data with observations gathered from designed experiments alleviates the problem of overly focused or incomplete production data sets. This paper considers practical aspects of building and validating neural network models of manufacturing processes , and illustrates the recommended approaches with two diverse case studies.
Duration Dependence and Labor Market Conditions: Theory and Evidence from a Field Experiment

This paper studies the role of employer behavior in generating "negative duration dependence" -- the adverse effect of a longer unemployment spell -- by sending fictitious resumes to real job postings in 100 U.S. cities. Our results indicate that the likelihood of receiving a callback for an interview significantly decreases with the length of a worker's unemployment spell, with the majority of this decline occurring during the first eight months. We explore how this effect varies with local labor market conditions, and find that duration dependence is stronger when the labor market is tighter. We develop a theoretical framework that shows how the sign of this interaction effect can be used to discern among leading models of duration dependence based on employer screening, employer ranking, and human capital depreciation. Our results suggest that employer screening plays an important role in generating duration dependence; employers use the unemployment spell length as a signal of unobserved productivity and recognize that this signal is less informative in weak labor markets.
Environment Design in Human Computation

Human computation is a new research area that takes advantage of human brain power to solve problems that computers cannot efficiently solve yet. In human computation systems, such as Games with A Purpose (GWAP) and crowdsourcing markets, users are given computationally hard tasks, and get rewards by accomplishing the tasks. In this work, we are interested in how to design a human computation system. In particular, we focus on the situation where developers cannot re-design the whole application but can only make limited changes to the systems. We model this situation as an environment design problem [10] with multiple agents. Under the assumption that agents do not interact, we define a m by n decision matrix, whose entry [i, j] represents the utility value of historical decision agent i made in environment j. Our goal is to find the environment for each agent to maximize the system payoff while minimizing the number of entries needed. To achieve this goal, we propose two different approaches, agent type elicitation and collaborative filtering, under different conditions. We also derive the convergence bound on the number of entries needed for each method.
A Survey on Diabetic Retinopathy Detection Techniques

Nowadays Diabetic retinopathy has become a common eye disease in most of the developed countries. it occurs in almost 80% of all diabetic cases and is the leading cause of blindness. Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. So the importance of automatic assessment of macular edema increased. In this paper we have done a survey on the different techniques used for detection of macular edema. It helps the ophthalmologists to apply proper treatments that might eliminate the disease or decrease the severity of it. Diabetic retinopathy is composed of a characteristic group of lesions found in the retina of individuals having had diabetes for several years. Detecting the exudates in early stage can prevent vision loss. Many approaches have been developed so far for the assessment of macular edema. KeywordsDiabetic macular edema, Exudate, Retinopathy