Miracle-dz/UniFAR-FTUs huggingface dataset:
On approximate differentiability of the maximal function

We prove that if f ∈ L 1 (ℝ n ) is approximately differentiable a.e. , then the Hardy-Littlewood maximal function Mf is also approximately differentiable a.e. Moreover, if we only assume that f ∈ L 1 (ℝ n ), then any open set of ℝ n contains a subset of positive measure such that Mf is approximately differentiable on that set. On the other hand we present an example of f ∈ L 1 (ℝ) such that Mf is not approximately differentiable a.e.
Cognitive Dissonance, Pessimism, and Behavioral Spillover Effects

This paper reports results from a unique two-stage experiment designed to examine the spillover effects of optimism and pessimism. In stage 1, we induce optimism or pessimism onto subjects by randomly assigning a high or low piece rate for performing a cognitive task. We find that participants receiving the low piece rate are significantly more pessimistic with respect to performance on this task. In stage 2 individuals participate in an ultimatum game. We find that minimum acceptable offers are significantly lower for pessimistic subjects, though this pessimism was generated in a completely unrelated environment. These results highlight the existence of important spillover effects that can be behaviorally and economically important - for example, pessimism regarding one’s initial conditions (e.g., living in poverty) may have spillover effects on one’s future labor market outcomes.
Germline BRCA1 mutation reprograms breast epithelial cell metabolism towards mitochondrial-dependent biosynthesis: evidence for metformin-based “starvation” strategies in BRCA1 carriers

We hypothesized that women inheriting one germline mutation of the BRCA1 gene (“one-hit”) undergo cell-type-specific metabolic reprogramming that supports the high biosynthetic requirements of breast epithelial cells to progress to a fully malignant phenotype. Targeted metabolomic analysis was performed in isogenic pairs of nontumorigenic human breast epithelial cells in which the knock-in of 185delAG mutation in a single BRCA1 allele leads to genomic instability. Mutant BRCA1 one-hit epithelial cells displayed constitutively enhanced activation of biosynthetic nodes within mitochondria. This metabolic rewiring involved the increased incorporation of glutamine- and glucose-dependent carbon into tricarboxylic acid (TCA) cycle metabolite pools to ultimately generate elevated levels of acetyl-CoA and malonyl-CoA, the major building blocks for lipid biosynthesis. The significant increase of branched-chain amino acids (BCAAs) including the anabolic trigger leucine, which can not only promote protein translation via mTOR but also feed into the TCA cycle via succinyl-CoA, further underscored the anabolic reprogramming of BRCA1 haploinsufficient cells. The anti-diabetic biguanide metformin “reversed” the metabolomic signature and anabolic phenotype of BRCA1 one-hit cells by shutting down mitochondria-driven generation of precursors for lipogenic pathways and reducing the BCAA pool for protein synthesis and TCA fueling. Metformin-induced restriction of mitochondrial biosynthetic capacity was sufficient to impair the tumor-initiating capacity of BRCA1 one-hit cells in mammosphere assays. Metabolic rewiring of the breast epithelium towards increased anabolism might constitute an unanticipated and inherited form of metabolic reprogramming linked to increased risk of oncogenesis in women bearing pathogenic germline BRCA1 mutations. The ability of metformin to constrain the production of mitochondrial-dependent biosynthetic intermediates might open a new avenue for “starvation” chemopreventive strategies in BRCA1 carriers.
Sparse delay-Doppler image reconstruction under off-grid problem

Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay-Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay-Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel and OMP based sparse reconstruction technique with parameter perturbation, named as PPOMP, is proposed for robust delay-Doppler radar processing even under the off-grid case. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. A new performance metric based on Kull-back-Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces.
Nanoscale Strain Fields Research of Boundaries between B2 Matrix and G.P. Zone in Ni-Ti Alloy Thin Films

Ti-47at. %Ni alloy films were prepared by magnetron sputtering followed by 460°C for 40 minutes heat-treatment. The strain fields between B2 phase matrix and G.P. zone were mapped by a combination of high-resolution transmission electron microscopy and geometric phase analysis method. It was found that there is a compressive strain region parallel to the longitudinal axis of G.P. zone with 2 nm in width, −2.2% in average strain at the boundaries between B2 phase and G.P. zone.
Do Wealth Differences Affect Fairness Considerations?

The influence of relative wealth on fairness considerations is analyzed in an ultimatum game experiment in which participants receive large and widely unequal initial endowments. Subjects initially demonstrate a concern for fairness. With time however, behavior becomes at odds with both subgame perfection and fairness. Evidence of learning is detected for both proposers and receivers in the estimation of a structural reinforcement learning model. The estimation results suggest that, guided by foregone best responses and an acquired sense of deservingness, rich subjects become more selfish, whereas poor subjects, influenced only by their own experience, learn to tolerate this behavior. Copyright 2006 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
Analysis of switching dynamics with competing neural networks

We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from di erent dynamical systems which alternate in time, a phenomenon which appears in many natural systems. In our approach, predictors compete for data points of a given time series. We combine competition and evolutionary inertia to a learning rule. Under this learning rule the system evolves such that the predictors, which nally survive, unambiguously identify the underlying processes. The segmentation achieved by this method is very precise and transients are included, a fact, which makes our approach promising for future applications. key words: neural networks, non-linear dynamics, chaos, time series analysis, prediction, competing neural networks
Assimilation of Radar Radial Velocity Data with the Wrf Ensemble- 3dvar Hybrid System for the Prediction of Hurricane Ike (2008)

An enhanced version of the hybrid ensemble-3DVAR data assimilation system for the WRF model is applied to the assimilation of radial velocity (Vr) data from two coastal WSR-88D radars for the prediction of Hurricane Ike (2008) before and during its landfall. In this hybrid system, flow-dependent ensemble covariance is incorporated into the varitional cost function using the extended control variable method. The analysis ensemble is generated by updating each forecast ensemble member with perturbed radar observations using the hybrid scheme itself. The Vr data are assimilated every 30 minutes for 3 hours immediately after Ike entered the coverage of the two coastal radars. The hybrid method produces temperature increments showing rainband structures and positive increments in the vortex core region, and a warm core throughout the hurricane depth in the final analysis. In contrast, the 3DVAR produces much weaker and smoother increments with negative values at the vortex center at lower levels. Wind forecasts from the hybrid analyses fit the observed radial velocity better than that from 3DVAR, and the 3-h accumulated precipitation forecasts from the hybrid are also more skillful. The track forecast is slightly improved by the hybrid method and slightly degraded by the 3DVAR compared to the forecast from the GFS analysis. All experiments assimilating the radar data show much improved intensity analyses and forecasts compared to the experiment without assimilating radar data. The better forecast of the hybrid indicates that the hybrid method produces dynamically more consistent state estimations. Little benefit of including the tuned static component of background error covariance in the hybrid is found. 1. Introduction Tropical cyclones (TCs) are among the most costly forms of natural disaster (Pielke et al. 2008). An accurate TC forecast will require not only a numerical model to realistically simulate both the TC itself and its environment, but also a data assimilation (DA) system that can effectively use the observations to accurately estimate the initial TC vortex and the environment where the TC is embedded in. To address the TC initialization issue, many previous studies adopted the vortex relocation and/or bogussing (e.g., Liu et al. 2000; Kurihara et al. 1995; Zou and Xiao 2000) techniques. While such techniques are non-trivial and have been shown to improve the hurricane forecast, how to maintain the dynamical and thermo-dynamical coherency of the hurricane and its environment is probably the biggest challenge with such methods. Recently, several studies have explored the use …
The Study of Flipped-Classroom for Pre-Service Science Teachers

The relatively new methodology, flipped-classroom, is one of blended learning instruction methodologies in which the traditional-classroom is inverted. This methodology asserts that students can participate and engage more successfully in their class and can attain better learning when their classroom is flipped. This work presents a two-year study to measure the effects of the flipped-classroom model on the performance, perceptions, and emotions for teacher training students in science education. Particularly, this research was carried out during two courses, 2014/2015 and 2015/2016, in a general science subject. With a post-task questionnaire, we obtained the information to assess their performance, perceptions, and emotions, toward the class. The results confirmed that a statistically significant difference was found on all assessments with the flipped-classroom students, performing higher on average, showing favorable perceptions, and demonstrating positive emotions about the flipped-classroom model. Thus, the students were ready to take more courses pursuing a flipped-classroom model. The results achieved in this study show a promising inclination about the performance, perceptions, and emotions of students toward the flipped-classroom methodology, and will provide an entirely a new impetus for this relatively new instruction methodology.
A Subspace Learning Approach to High-Dimensional Matrix Decomposition with Efficient Information Sampling

This paper is concerned with the problem of low-rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems that scale with the dimension of the data, which limit their scalability. Furthermore, the existing randomized approaches mostly rely on uniform random sampling, which can be quite inefficient for many real world data matrices that exhibit additional structures (e.g. clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out using a small data sketch formed from sampled columns/rows. Even when the data is sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly O(r \mu), where \mu is the coherency parameter and r the rank of the low-rank component. In addition, efficient sampling algorithms are proposed to address the problem of column/row sampling from structured data. The proposed sampling algorithms can be independently used for feature selection from high-dimensional data. The proposed approach is amenable to online implementation and an online scheme is proposed.
E-GOVERNMENT AWARENESS AMONG THE TECHNO-DISADVANTAGED IN THE UNITED STATES

This exploratory research focuses on awareness among techno-disadvantaged citizens in the United States. Specifically, we address whether awareness is associated with visitation and whether there are differences between those who are aware and those who are not aware. Following up on on a theory-based community initiative designed to improve computer literacy and access to information and communication technologies (ICT) for members of an underserved public housing community, a survey was undertaken. The results indicate that awareness is associated with visitation. Differences in demographic characterisitics, perceived ease of use (PEOU), and perceived access barriers between those who are aware of egovernment websites, and those who are not, were found. While nearly half of the respondents are neither aware of nor have visited e-government websites, a slight majority is partaking of egovernment services. We identify directions for future research and conclude by emphasizing the value of a theory-based community initiative to improve computer literacy, provide access to ICT, and advance e-government inclusion.
OpenFlow based Load Balancing for Fat-Tree Networks with Multipath Support

—Data center networks are designed for satisfying the data transmission demand of densely interconnected hosts in the data center. The network topology and routing mechanism can affect the performance and latency signiﬁcantly. Nowadays, the fat-tree network is one of the most widely used topologies for data center networks. Network engineers also adopt load balancing methods in the design of routing algorithms. However, the requirement of load balancing routing in fat-tree networks cannot be fully satisﬁed by traditional approaches. The main reason is the lack of efﬁcient ways to obtain network trafﬁc statistics from each network device. As a solution, the OpenFlow protocol enables monitoring trafﬁc statistics by a centralized controller. Toachieve high performance and low latency, we present a load balancer for OpenFlow based data center networks. We implement a dynamic routing algorithm in the load balancer. The task of the algorithm is to distribute trafﬁc of upcoming network ﬂows and make each alternative path receive equal amounts of trafﬁc load. It can apply to large scale networks and schedule data ﬂows dynamically. Our implementation uses the OpenFlow controller Beacon and network emulator Mininet. The evaluation results demonstrate that our dynamic load balancing routing algorithm is superior over not only the none load balancing routing algorithm but also the static load balancing algorithm.
A Bayesian Approach to Analyse Genetic Variation within RNA Viral Populations

The development of modern and affordable sequencing technologies has allowed the study of viral populations to an unprecedented depth. This is of particular interest for the study of within-host RNA viral populations, where variation due to error-prone polymerases can lead to immune escape, antiviral resistance and adaptation to new host species. Methods to sequence RNA virus genomes include reverse transcription (RT) and polymerase chain reaction (PCR). RT-PCR is a molecular biology technique widely used to amplify DNA from an RNA template. The method itself relies on the in vitro synthesis of copy DNA from RNA followed by multiple cycles of DNA amplification. However, this method introduces artefactual errors that can act as confounding factors when the sequence data are analysed. Although there are a growing number of published studies exploring the intra- and inter-host evolutionary dynamics of RNA viruses, the complexity of the methods used to generate sequences makes it difficult to produce probabilistic statements about the likely sources of observed sequence variants. This complexity is further compounded as both the depth of sequencing and the length of the genome segment of interest increase. Here we develop a Bayesian method to characterise and differentiate between likely structures for the background viral population. This approach can then be used to identify nucleotide sites that show evidence of change in the within-host viral population structure, either over time or relative to a reference sequence (e.g. an inoculum or another source of infection), or both, without having to build complex evolutionary models. Identification of these sites can help to inform the design of more focussed experiments using molecular biology tools, such as site-directed mutagenesis, to assess the function of specific amino acids. We illustrate the method by applying to datasets from experimental transmission of equine influenza, and a pre-clinical vaccine trial for HIV-1.
Biological correlates of psychopathy: a brief review

Purpose – The aim of this paper is to present and provide a critical review of most recent studies inquiring into brain abnormalities in psychopathy. Design/methodology/approach – The authors provide an overview of the findings of neurobiological studies conducted in the last five years. Publications chosen for review were found using Web of Science, PsycINFO and Scopus search engines. Findings – Data in the literature reveal that psychopathy is associated with brain abnormalities in frontal and temporo-limbic regions, i.e. regions responsible for moral decision making, emotional processing and learning. Additionally, interactions between the brain areas have been identified as crucial for the development of psychopathic personality traits. Research findings suggest that the flow of impulses between the frontal cortex and temporo-limbic structures in psychopaths is significantly hindered. Originality/value – The current paper provides an in-depth review of most recent neurobiological studies inquiring into brain abnormalities associated with psychopathic personality traits. Moreover, a particular attention has been paid to identifying abnormalities in brain structures not previously studied in relation to psychopathy (e.g. mirror neuron system, white matter connections).
Tachyon Vacuum in Cubic Superstring Field Theory

In this paper we give an exact analytic solution for tachyon condensation in the modified (picture 0) cubic superstring field theory. We prove the absence of cohomology and, crucially, reproduce the correct value for the D-brane tension. The solution is surprising for two reasons: First, the existence of a tachyon vacuum in this theory has not been definitively established in the level expansion. Second, the solution vanishes in the GSO(-) sector, implying a ``tachyon vacuum'' solution exists even for a BPS D-brane.
Scheduling I/O in virtual machine monitors

This paper explores the relationship between domain scheduling in avirtual machine monitor (VMM) and I/O performance. Traditionally, VMM schedulers have focused on fairly sharing the processor resources among domains while leaving the scheduling of I/O resources as asecondary concern. However, this can resultin poor and/or unpredictable application performance, making virtualization less desirable for applications that require efficient and consistent I/O behavior. This paper is the first to study the impact of the VMM scheduler on performance using multiple guest domains concurrently running different types of applications. In particular, different combinations of processor-intensive, bandwidth-intensive, andlatency-sensitive applications are run concurrently to quantify the impacts of different scheduler configurations on processor and I/O performance. These applications are evaluated on 11 different scheduler configurations within the Xen VMM. These configurations include a variety of scheduler extensions aimed at improving I/O performance. This cross product of scheduler configurations and application types offers insight into the key problems in VMM scheduling for I/O and motivates future innovation in this area.
Noise parameter optimization of UHV/CVD SiGe HBT's for RF and microwave applications

This paper demonstrates a predictive noise parameter estimation methodology for UHV/CVD SiGe HBT's which combines ac measurement, calibrated ac simulation and two of the latest Y-parameter-based noise models: (1) the thermodynamic noise model, and (2) the SPICE noise model. The bias current and frequency dependence of the minimum noise figure, the optimum generator admittance, and the noise resistance are calculated using both models and compared with measurements. The observed agreements and discrepancies are investigated using circuit analysis of the chain noisy two-port representation. For the devices under study, the SPICE model description of thermal noise produces a better overall agreement to data in terms of all the noise parameters. Experiments on devices with different collector doping levels show that both low noise and high breakdown voltage can be realized with one profile without significantly compromising the ac current gain and the ac power gain.
The D816V Mutation of c-Kit Circumvents a Requirement for Src Family Kinases in c-Kit Signal Transduction*

The receptor tyrosine kinase c-Kit plays a critical role in hematopoiesis, and gain-of-function mutations of the receptor are frequently seen in several malignancies, including acute myeloid leukemia, gastrointestinal stromal tumors, and testicular carcinoma. The most common mutation of c-Kit in these disorders is a substitution of the aspartic acid residue in position 816 to a valine (D816V), leading to constitutive activation of the receptor. In this study, we aimed to investigate the role of Src family kinases in c-Kit/D816V signaling. Src family kinases are necessary for the phosphorylation of wild-type c-Kit as well as of activation of downstream signaling pathways including receptor ubiquitination and the Ras/Mek/Erk pathway. Our data demonstrate that, unlike wild-type c-Kit, the phosphorylation of c-Kit/D816V is not dependent on Src family kinases. In addition, we found that neither receptor ubiquitination nor Erk activation by c-Kit/D816V required activation of Src family kinases. In vitro kinase assay using synthetic peptides revealed that c-Kit/D816V had an altered substrate specificity resembling Src and Abl tyrosine kinases. We further present evidence that, in contrast to wild-type c-Kit, Src family kinases are dispensable for c-Kit/D816V cell survival, proliferation, and colony formation. Taken together, we demonstrate that the signal transduction pathways mediated by c-Kit/D816V are markedly different from those activated by wild-type c-Kit and that altered substrate specificity of c-Kit circumvents a need for Src family kinases in signaling of growth and survival, thereby contributing to the transforming potential of c-Kit/D816V.