Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Depth Proof Systems

A proof system for a language L is a function f such that Range(f) is exactly L. In this article, we look at proof systems from a circuit complexity point of view and study proof systems that are computationally very restricted. The restriction we study is proof systems that can be computed by bounded fanin circuits of constant depth (NC0) or of O(log log n) depth but with O(1) alternations (poly log AC0). Each output bit depends on very few input bits; thus such proof systems correspond to a kind of local error correction on a theorem-proof pair. We identify exactly how much power we need for proof systems to capture all regular languages. We show that all regular languages have poly log AC0 proof systems, and from a previous result (Beyersdorff et al. [2011a], where NC0 proof systems were first introduced), this is tight. Our technique also shows that Maj has poly log AC0 proof system. We explore the question of whether Taut has NC0 proof systems. Addressing this question about 2TAUT, and since 2TAUT is closely related to reachability in graphs, we ask the same question about Reachability. We show that if Directed reachability has NC0 proof systems, then so does 2TAUT. We then show that both Undirected Reachability and Directed UnReachability have NC0 proof systems, but Directed Reachability is still open. In the context of how much power is needed for proof systems for languages in NP, we observe that proof systems for a good fraction of languages in NP do not need the full power of AC0; they have SAC0 or coSAC0 proof systems.
The Speculative Prefetcher and Evaluator Processor for Pipelined Memory Hierarchies

We consider extensible processor designs in which the number of gates and the distance that a signal traverses in one clock period are, within a given technology, independent of system size. Consequently such designs scale with system size (in particular, with memory latency) as well as with technological advancement. We assume aggressive memories that are not only hierarchical in nature, but are also heavily pipelined, accepting requests at a constant rate. In such a setting, we propose a processor organization called the speculative prefetcher and evaluator (SPE), which performs memory accesses on speculated addresses and executes operations on speculated operand values. The speculation policy simply assumes the absence of dependences among suitable sets of instructions that are executed concurrently and it is not based on estimating properties of the program under execution. The SPE also supports branch target speculation; however, the performance results of this paper only assume static prediction of loop branches. In order to appraise the performance of the SPE, we evaluate the execution time on various algorithms. First we consider a class of programs, based on loops, which includes a number of interesting algorithms such as matrix addition and multiplication, FFT, bitonic merging and sorting, finite-difference solutions for some PDEs, and digital filtering simulations. Then, we consider a recursive implementation of quicksort. For all these programs, the execution time is proportional to the number of executed instructions, that is, the cycle-per-instruction metric is constant, even if memory latency is pessimistically taken to grow linearly with the physical address. The result for the loop class exploits only the pipelinability of the memory. The result for quicksort also exploits the hierarchical nature of memory
Web-based survey of fertility issues in young women with breast cancer.

PURPOSE Young women with breast cancer often seek advice about whether treatment will affect their fertility. We sought to gain a better understanding of women's attitudes about fertility and how these concerns affect decision making. PATIENTS AND METHODS We developed a survey about fertility issues for young women with a history of early-stage breast cancer. The survey was e-mailed to all registered Young Survival Coalition survivor members (N = 1,702). E-mail reminders were used. RESULTS Six hundred fifty-seven eligible respondents completed the survey. Mean age at breast cancer diagnosis was 32.9 years; mean current age was 35.8 years. Ninety percent of women were white; 62% were married; 76% were college graduates. Stages at diagnosis were as follows: 0, 10%; I, 27%; II, 47%; III, 13%. Sixty-two percent of women were within 2 years of diagnosis. Fifty-seven percent recalled substantial concern at diagnosis about becoming infertile with treatment. In multivariate logistic regression, greater concern about infertility was associated with wish for children/more children (odds ratio [OR], 120; P < .0001), number of prior pregnancies (OR, 0.78; P = .01), and prior difficulty conceiving (OR, 1.86; P = .08). Twenty-nine percent of women reported that infertility concerns influenced treatment decisions. Seventy-two percent of women reported discussing fertility concerns with their doctors; 51% felt their concerns were addressed adequately. Women seemed to overestimate their risk of becoming postmenopausal with treatment. CONCLUSION Fertility after treatment is a major concern for young women with breast cancer. There is a need to communicate with and educate young patients regarding fertility issues at diagnosis and a need for future research directed at preserving fertility for young breast cancer survivors.
Inflammatory histopathogenesis of nasopalatine duct cyst: a clinicopathological study of 41 cases.

OBJECTIVE The aim of this study is to characterize immunohistochemical profiles of lining epithelia of nasopalatine duct cyst (NPC) as well as to correlate those findings with their clinicopathological features to understand the histopathogenesis of NPC. MATERIALS AND METHODS Forty-one surgical specimens from NPC were examined for clinical profiles and expression of keratin-7, 13, MUC-1, and P63 by immunohistochemistry, compared to radicular cyst (RC) and maxillary sinusitis. RESULTS Nasopalatine duct cyst was clinically characterized by male predominant occurrence: 44% of the cases involved tooth roots, and 70% with inflammatory backgrounds. Lining epithelia of NPCs without daughter cysts were immunohistochemically distinguished into three layers: a keratin 7-positive (+) ciliated cell layer in the surface, a keratin-13+ middle layer, and a MUC-1+/P63+ lower half, indicating that they were not respiratory epithelia, and the same layering pattern was observed in RC. However, those immunolocalization patterns of the main cyst lining with daughter cyst were exactly the same as those of daughter cyst linings as well as duct epithelia of mucous glands. CONCLUSIONS Two possible histopathogenesis of NPC were clarified: one was inflammatory cyst like RC and the other was salivary duct cyst-like mucocele.
Adaptive Wavelets for Characterizing Magnetic Flux Leakage Signals from Pipeline inspection

This paper presents an iterative inversion scheme using radial basis function neural network (RBFNN) for predicting the depth profile of a defect in the pipe-wall from the information in the magnetic flux leakage (MFL) signal. Due to the high dimensionality of the data the method uses a multi-resolution approach with adaptive wavelets. The algorithm is fast and provides full three dimensional profile of the defect in the pipewall which is important for predicting the remaining life of the pipe.
Strigolactones as Germination Stimulants for Root Parasitic Plants

Witchweeds (Striga spp.) and broomrapes (Orobanche and Phelipanche spp.) are the two most devastating root parasitic plants belonging to the family Orobanchaceae and are causing enormous crop losses throughout the world. Seeds of these root parasites will not germinate unless they are exposed to chemical stimuli, ‘germination stimulants’ produced by and released from plant roots. Most of the germination stimulants identified so far are strigolactones (SLs), which also function as host recognition signals for arbuscular mycorrhizal fungi and a novel class of plant hormones inhibiting shoot branching. In this review, we focus on SLs as germination stimulants for root parasitic plants. In addition, we discuss how quantitative and qualitative differences in SL exudation among sorghum cultivars influence their susceptibility to Striga.
DSM-III-R generalized anxiety disorder in the National Comorbidity Survey.

BACKGROUND Nationally representative general population data are presented on the current, 12-month, and lifetime prevalence of DSM-III-R generalized anxiety disorder (GAD) as well as on risk factors, comorbidity, and related impairments. METHODS The data are from the National Comorbidity Survey, a large general population survey of persons aged 15 to 54 years in the noninstitutionalized civilian population of the United States. DSM-III-R GAD was assessed by lay interviewers using a revised version of the Composite International Diagnostic Interview. RESULTS Generalized anxiety disorder was found to be a relatively rare current disorder with a current prevalence of 1.6% but was found to be a more frequent lifetime disorder affecting 5.1% of the US population aged 15 to 45 years. Generalized anxiety disorder was twice as common among women as among men. Multivariate logistic regression analysis showed that being older than 24 years, separated, widowed, divorced, unemployed, and a homemaker are significant correlates of GAD. Consistent with studies in treatment samples, we found that GAD was frequently associated with a wide spectrum of other mental disorders, with a lifetime comorbidity among 90.4% of the people who had a history of GAD. CONCLUSION Contrary to the traditional view that GAD is a mild disorder, we found that the majority of people with GAD, whether they were comorbid or not, reported substantial interference with their life, a high degree of professional help seeking, and a high use of medication because of their GAD symptoms. Although lifetime GAD is highly comorbid, the proportion of current GAD that is not accompanied by any other current diagnosis is high enough to indicate that GAD should be considered an independent disorder rather than exclusively a residual or prodrome of other disorders.
Comprehensive Study of Heart Disease Diagnosis Using Data Mining and Soft Computing Techniques

2 Abstract - Heart disease diagnosis is a challenging task which can offer automated prediction about the heart disease of patient so that further treatment can be made easy. Due to this fact, heart disease diagnosis has received immense interest globally among medical community. Here, artificial intelligence played an important role in diagnosis of heart disease with improved effectiveness. Based on this perspective, several researches have been conducted in the literature recently. So, analyzing those diagnosis techniques can lead to new development in this area. Accordingly, we present a detailed survey of 47 articles published in the standard journals from the year 2005 to 2013. The survey of the papers related to heart disease and also the survey of many categories of heart disease such as coronary heart disease, coronary artery disease, heart failure, ischemic heart disease, cardiovascular disease, congenital heart disease, valvular heart disease and hypoplastic left heart syndrome are presented in this paper. From the survey the finding is that neural network based techniques contribute more effectiveness and some techniques have obtained more than 90% accuracy. Finally, some of the research issue is also addressed to precede the further research on the same direction.
Aquarius radiometer status

Aquarius was launched on June 10, 2011 as part of the Aquarius/SAC-D observatory and the instrument has been operating continuously since being turned on in August of the same year. The initial map of sea surface salinity was released one month later (September) and the quality of the retrieval has continuously improved since then. The Aquarius radiometers include several special features such as measurement of the third Stokes parameter, fast sampling, and careful thermal control, and a combined passive/active instrument. Aquarius is working well and in addition to helping measure salinity, the radiometer special features are generating new results.
Increased urine catecholamines and cortisol in women with irritable bowel syndrome.

OBJECTIVES There are few data on the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis in individuals with chronic GI symptoms. The current study was designed to describe and compare urine catecholamine (norepinephrine, epinephrine) and cortisol levels in women diagnosed with irritable bowel syndrome (IBS-patients), women who report similar symptom levels but had not sought health care services (IBS-nonpatients; IBS-NP), and asymptomatic (control) women. METHODS Seventy-three women (24 IBS; 24 IBS-NP; 25 controls) were interviewed for demographic, GI, gynecological, and psychological data and then followed for two menstrual cycles with a daily health diary. Urine samples were obtained in the evening and morning at specific phases across two menstrual cycles. RESULTS Women in the IBS group had significantly higher PM and AM urine norepinephrine levels. Urine epinephrine and cortisol levels were also generally higher in women with IBS. Differences in neuroendocrine indicators of arousal were not accounted for by differences in demographic variables, lifestyle characteristics, menstrual distress, or average daily measures of anxiety or depression. CONCLUSIONS Increases in indicators of sympathetic nervous system activation in women seeking health care for IBS may reflect greater symptom distress or may contribute to increased symptom distress.
Accounting for the residual uncertainty of multi-layer perceptron based features

Multi-Layer Perceptrons (MLPs) are often interpreted as modeling a posterior distribution over classes given input features using the mean field approximation. This approximation is fast but neglects the residual uncertainty of inference at each layer, making inference less robust. In this paper we introduce a new approximation of MLP inference that takes under consideration this residual uncertainty. The proposed algorithm propagates not only the mean, but also the variance of inference through the network. At the current stage, the proposed method can not be used with soft-max layers. Therefore, we illustrate the benefits of this algorithm in a tandem scheme. We use the residual uncertainty of inference of MLP-based features to compensate a GMM-HMM backend with uncertainty decoding. Experiments on the Aurora4 corpus show consistent improvement of performance against conventional MLPs for all scenarios, in particular for clean speech and multi-style training.
Exercise Price of Executive Stock Option and Executive Opportunism in China Listed Companies

Adding stock option to management compensation portfolio is an effective mechanism in aligning the interests of managers with those of shareholders. But this mechanism may be noneffective since the self-dealing opportunism of managers. This thesis analyses the managers will undertake self-dealing opportunities by choosing time and content of information disclosure to affect the exercise price of stock option. Taking the listed companies of China, which initially disclose stock option incentive plan as study subject, and using the events study approach to compute cumulated abnormal returns, I observe a significant decline in stock price before top executives receive the award and a significant increase in stock price shortly after the incentive plans disclosure.
Molecular Imaging in Parkinson's Disease

The present work explores brain functional changes in drug-naive Parkinson's disease (PD) patients by means of molecular imaging techniques. Thirty-one consecutive drug-naive PD patients from the Neurological Clinic of the University of Flor-ence underwent clinical assessment, neuropsychological assessment, MRI, [123I]FP-CIT SPECT, [18F]FDG PET. First, [18F]FDG-PET was employed to identify in drug-naive PD patients brain metabolic alteration uniquely related to disease process and not modulated by anti-parkinsonian therapeutic intervention. Second, [18F]FDG-PET and [123I]FP-CIT SPECT were employed together to explore the early functional changes in brain function related to dopaminergic depletion in the putamen and in the caudate nucleus.The present work explores brain functional changes in drug-naive Parkinson's disease (PD) patients by means of molecular imaging techniques. Thirty-one consecutive drug-naive PD patients from the Neurological Clinic of the University of Flor-ence underwent clinical assessment, neuropsychological assessment, MRI, [123I]FP-CIT SPECT, [18F]FDG PET. First, [18F]FDG-PET was employed to identify in drug-naive PD patients brain metabolic alteration uniquely related to disease process and not modulated by anti-parkinsonian therapeutic intervention. Second, [18F]FDG-PET and [123I]FP-CIT SPECT were employed together to explore the early functional changes in brain function related to dopaminergic depletion in the putamen and in the caudate nucleus.
Towards the Systematic Testing of Aspect-Oriented Programs

The instant invention relates to the production of composite glass articles involving the bringing together of two or more glasses, while in the molten state, into an integral unit. More particularly, the present invention is concerned with the manufacture of such articles wherein a reaction occurs at the interface between the laminae of molten glass such that an opal phase is spontaneously produced in situ thereat.
Join Optimization in the MapReduce Environment for Column-wise Data Store

The chain join processing which combines records from two or more tables sequentially has been well studied in the centralized databases. However, it has seldom been discussed in the cloud computing era, and remains imperative to be solved, especially where structured (or relational) data are stored in a column (attribute) wise fashion in distributed file systems (e.g., Google File System) over hundreds of or even thousands of commodities PCs. In this paper, we propose a novel method for chain join processing, which is one of the common primitives in the cloud era for column-wise stored data analysis. By effectively selecting the dedicated records (tuples) for the chain join based on the information exploited within bipartite join graph, communication cost for record transmission could be reduced dramatically. A bushy tree structure is deployed to regulate the chain join sequence, which further reduces the number of intermediate results generated and transmitted, and explores higher parallelism in join processing, while results in more efficient join processing. Our extensive performance study confirms the effectiveness and efficiency of our methods.
Functionalization Methods of Carbon Nanotubes and Its Applications

In 1991 Iijima [Iijima, 1991] presented transmission electron microscopy observations of elongated and concentric layered microtubules made of carbon atoms, nowadays which were called carbon nanotubes (CNTs). In order to understand the structure of a carbon nanotube, it can be first imagined as a rolled up sheet of graphene with planar-hexagonal arrangement of carbon atoms distributed in a honeycomb lattice. CNTs are classified into single-walled carbon nanotube, double-walled carbon nanotube, and multi-walled carbon nanotube according to the rolling layers of graphene sheets. Several techniques for producing CNTs have been established and all of them have advantages as well as disadvantages. Usually being applied methods are high temperature techniques, namely arc discharge [Bystrzejewski et al., 2008; Sun et al., 2007] and laser ablation [Ruummeli et al., 2007], as well as chemical vapor deposition (CVD) with its most common variants [Ayala et al., 2008; Nikolaev et al., 1999]. CNTs possess unique electronic, chemical, and mechanical properties that make them leading materials for a variety of potential applications. But the outer wall of pristine CNTs is, in principle, conceived as chemically inert. This is not always desirable for applications and for this reason. One of the most promising routes to overcome this difficulty is to functionalize CNT. Functionalization extends their properties and consequently their application potential. Hence understanding their functionalization is crucial for fully exploiting their potential. Researchers have developed some effective methods to functionalize CNT surfaces such as covalent modification or noncovalent approaches including polymer wrapping, biomolecule binding, and metal ion binding. Other functionalization routes which have been thought to make them chemically active in the past few years, including chemical and solid-phase or hydro-mechanochemical method will also be introuduced in details in the following contributions. The advantages and shortcomings of every functionalization method will be showed clearly. At the same time some characterization methods for CNTs will be also refered. In the last part of this chapter the applications of carbon nanotubes as functional materials in the fields of biosensing, fuel cells, medical treatment, and so on, especially in the field of thermal conductivity are summarized. It is our hope that all the content of this chapter can supply helps to the researchers in the study of carbon nanotubes.
Power-managed smart lighting using a semantic interoperability architecture

We present a power-managed smart lighting system that allows collaboration of Consumer Electronics (CE) lighting-devices and corresponding system architectures provided by different CE suppliers. In the example scenario, the rooms of a building are categorized as low- and high-priority, each category utilizing a different system architecture. The rooms collaborate through a semantic interoperability platform. The overall smart lighting system conforms to a power quota regime and maintains a target power consumption level by automatically adjusting power consumed by luminaries in the building. Experiments with CE devices of different suppliers operating on different networks show that the semantic interoperability architecture allows device collaboration that can lead to lower power cost.
Cell-surface heparan sulfate proteoglycans are essential components of the unconventional export machinery of FGF-2

FGF-2 is an unconventionally secreted lectin that transmits proangiogenic signals through a ternary complex with high-affinity FGF receptors and heparan sulfate proteoglycans (HSPGs). Although FGF-2 signal transduction is understood in great detail, its mechanism of release from cells, which is independent of the classical secretory pathway, remains elusive. To test the hypothesis that FGF-2 secretion is linked to its cell-surface ligands, we studied FGF-2 release using mutants defective for HSPG binding and cells with impaired HSPG biosynthesis. Here, we report that a functional interaction between FGF-2 and HSPGs is required for net export of FGF-2 from mammalian cells. FGF-2 release requires extracellular, membrane-proximal HSPGs. We propose that extracellular HSPGs form a molecular trap that drives FGF-2 translocation across the plasma membrane.