Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychophysics with Junctions in Real Images

Junctions, formed at the intersection of image contours, are thought to play an important and early role in vision. The interest in junctions can be attributed in part to the notion that they are local image features that are easy to detect but that nonetheless provide valuable information about important events in the world, such as occlusion and transparency. Here I test the notion that there are locally defined junctions in real images that might be detected with simple, early visual mechanisms. Human observers were used as a tool to measure the visual information available in local regions of real images. One set of observers was made to label all the points in a set of real images where one edge occluded another. A second set of observers was presented with variable-size circular subregions of these images, and was asked to judge whether the regions were centered on an occlusion point. This task is easy if junctions are visible, but I found performance to be poor for small regions, not approaching ceiling levels until observers were given fairly large (∼ 50 pixels in diameter) regions over which to make the judgment. Control experiments ruled out the possibility that the effects are just due to junctions at multiple scales. Experiments reported here suggest that, although some junctions in real images are locally defined and can be detected with simple mechanisms, a substantial fraction necessitate the use of more complex and global processes. This raises the possibility that junctions in such cases may not be detected prior to scene interpretation.
Automating ad hoc data representation transformations

To maximize run-time performance, programmers often specialize their code by hand, replacing library collections and containers by custom objects in which data is restructured for efficient access. However, changing the data representation is a tedious and error-prone process that makes it hard to test, maintain and evolve the source code. We present an automated and composable mechanism that allows programmers to safely change the data representation in delimited scopes containing anything from expressions to entire class definitions. To achieve this, programmers define a transformation and our mechanism automatically and transparently applies it during compilation, eliminating the need to manually change the source code. Our technique leverages the type system in order to offer correctness guarantees on the transformation and its interaction with object-oriented language features, such as dynamic dispatch, inheritance and generics. We have embedded this technique in a Scala compiler plugin and used it in four very different transformations, ranging from improving the data layout and encoding, to retrofitting specialization and value class status, and all the way to collection deforestation. On our benchmarks, the technique obtained speedups between 1.8x and 24.5x.
Diagnosis of Breast Masses from Dynamic Contrast-Enhanced and Diffusion-Weighted MR: A Machine Learning Approach

Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported. Materials and Methods The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination. Results Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively. Conclusion Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.
DDOA: A Dirichlet-Based Detection Scheme for Opportunistic Attacks in Smart Grid Cyber-Physical System

In the hierarchical control paradigm of a smart grid cyber-physical system, decentralized local agents (LAs) can potentially be compromised by opportunistic attackers to manipulate electricity prices for illicit financial gains. In this paper, to address such opportunistic attacks, we propose a Dirichlet-based detection scheme, where a Dirichlet-based probabilistic model is built to assess the reputation levels of LAs. Initial reputation levels of the LAs are first trained using the proposed model, based on their historical operating observations. An adaptive detection algorithm with reputation incentive mechanism is then employed to detect opportunistic attackers. We demonstrate the utility of our proposed scheme using data collected from the IEEE 39-bus power system with the PowerWorld simulator.
Coworker Relationships and Informal Communication in High-Intensity Telecommuting

Given that high-intensity telecommuters report feeling socially isolated, this study uses structuration and constructivist theories to examine the role of coworker relationships and informal communication in the context of high-intensity telecommuting. Teleworkers' organizational commitment, job satisfaction, informal communication with peers, and coworker liking were examined. Coworker liking was positively related to teleworkers' informal communication satisfaction with coworkers, and with their organizational commitment and job satisfaction. Complaining talk was negatively related to teleworkers' commitment and satisfaction; informal communication satisfaction was positively related to organizational commitment. Coworker liking moderated the effect of teleworkers' complaining talk on organizational commitment, but not job satisfaction. The buffering effect of coworker relationships is discussed as a resource for organizational members engaged in high-intensity telecommuting.
Annexin V for Flow Cytometric Detection of Phosphatidylserine Expression on B Cells Undergoing Apoptosis

Apoptosis, or programmed cell death, is a general mechanism for removal of unwanted cells from the immune system. It is characterized by chromatin condensation, a reduction in cell volume, and endonuclease cleavage of DNA into oligonucleosomal length fragments. Apoptosis is also accompanied by a loss of membrane phospholipid asymmetry, resulting in the exposure of phosphatidylserine at the surface of the cell. Expression of phosphatidylserine at the cell surface plays an important role in the recognition and removal of rpoptotic cells by macrophages. Here we describe a new method for the detection of apoptotic cells by flow cytometry, using the binding of fluorescein isothiocyanatelabeled annexin V t o phosphatidylserine. When Burkitt lymphoma cell lines and freshly isolated germinal center B
The Impact of Social Ties on Coalition Strength and Effectiveness: The Case of the Battered Women's Movement in St Louis

Although a number of researchers have studied social movement coalitions, we still need to develop better ways to analyze why some initiatives succeed and others fail. In this study, I compare three different coalitions in which battered women's movement activists participated to determine the factors that made some coalitions stronger and more effective than others. Using qualitative data from a 16-year longitudinal study, I found that an important variable affecting a coalition's strength is the quality of the social relationships among its members. In particular, close personal ties encourage a degree of trust, the sharing of detailed knowledge, and collective problem-solving mechanisms that enable coalition members to anticipate one another's preferences and behavior and overcome conflicts as they arise. The greater coordination capacity that results can make it possible for a social movement to exert increased political power and position it to take advantage of new political opportunities when they arise.
Quantification of voriconazole in plasma by liquid chromatography-tandem mass spectrometry

Abstract A convenient liquid chromatography-tandem mass spectrometry method for the quantification of the triazole antifungal agent voriconazole in plasma samples is described. Fenbuconazole is used as an internal standard. After protein precipitation, automated solid-phase extraction is applied. Electrospray ionization in the positive mode is used and the following mass transitions are recorded: voriconazole, 350→127; and fenbuconazol, 337→125. The analytical run time is 4min. The response was linear from 78 to 5000μg/L. The total coefficient of variation (n=16) was 12.6% for a low-concentration pool (143μg/L), 4.7% for a medium-concentration pool (419μg/L), and 5.0% for a high-concentration pool (4304μg/L). The method is proposed for future investigations that should be performed to test the hypothesis that therapeutic drug monitoring of voriconazole is clinically useful.
Ideal Images of Educational Leadership in Mexico City and South Texas

Abstract This study used a qualitative approach to analyze ideal images of educational leadership among administrators in Mexico City and South Texas. Looking at educational leadership from a cross-cultural perspective revealed issues that are hidden when working in just one culture. Though both groups indicated that participation, clear communication, planned change, and attention to values were components of their best leadership experiences, there were subtle differences in how they discussed each dimension.
Single‐Session Approaches to Therapy: Time to Review

In 2001 Bloom produced a comprehensive review of single-session approaches to therapy (SST). In his paper he outlined the concept of single session therapy and considered the evidence that could be used to support implementation of single session approaches. At the time, Bloom's was probably the most comprehensive review of research on single session therapy available: he considered papers that provided overviews of approaches to developing and delivering single-session treatment, as well as papers that used controlled and uncontrolled methods to evaluate outcomes. What I will do in this paper is to informally review the literature on single session therapy that has been published since Bloom's 2001 paper. The paper is not intended as a formal review or meta-analysis of research data, partly because the field still does not have such methodological rigour.
Learning k-Testable and k-Piecewise Testable Languages from Positive Data

The families of locally testable (LT ) and piecewise testable (PWT ) languages have been deeply studied in formal language theory. They have in common that the role played by the segments of length k of their words in the first family is played in the second by their subwords (sequences of non necessarily consecutive symbols), also of length k. We propose algorithms that, given k > 0, identify both families of languages (k-PWT and k-LT ) from positive data in the limit. The first one identifies the family of k-PWT languages making use of a combinatorial property discovered by Simon and improves the complexity of a previous algorithm for that family [25]. The second algorithm identifies the family of k-LT languages using a result about the cascade product of automata. In this product, for each k, one of the factors is a fixed transducer and the second is the automaton obtained by the first algorithm for k = 1 using the transduction of the sample as input.
Role Stress: Burnout Antecedent in Nursing Professionals

Abstract Burnout syndrome has been described by different authors as a reaction to work stress. Hence both the role stress components–role ambiguity and role conflict–appear in the literature as burnout antecedent variables. The correlation between these variables is positive. On the other hand, the work social support (from supervisors and colleagues) is a variable with influence on both role stress and burnout, so the people who perceive higher work social support feel lower role stress and lower burnout than the people who perceive lower social support. In this study we present the results of a correlational study of role ambiguity, role conflict, burnout levels (tested with Maslach Burnout Inventory), and the work social support effects on the relations. The sample are 102 nursing professionals, 24 per cent males and 76 per cent females. The results present a positive correlation between both role ambiguity and role conflict, and the emotional exhaustion scale and depersonalization scale, and a negati.
Visualization of construction planning information

A construction schedule helps planners to identity potential construction problems prior to actual building construction. Planners must envision the sequence of construction, the workspace logistics, and utilization of resources and equipment in space and over time. This paper discusses methods of generating, visuaMng, and evahrating construction planning information with CAD based tools. We use a construction example to illustrate how feature extraction of 3D CAD models can help identify construction problems and evaluate the quality of a construction plan through 4D analysis and 4D annotation.
Heuristic Cleaning Algorithms in Log-Structured File Systems

Research results show that while Log-Structured File Systems (LFS) offer the potential for dramatically improved file system performance, the cleaner can seriously degrade performance, by as much as 40% in transaction processing workloads [9]. Our goal is to examine trace data from live file systems and use those to derive simple heuristics that will permit the cleaner to run without interfering with normal file access. Our results show that trivial heuristics perform very well, allowing 97% of all cleaning on the most heavily loaded system we studied to be done in the background.
Rockefeller University's Science Outreach

that children should learn the content of science through the process of scientific inquiry. Therefore, one challenge for science education reform is for students and their teachers to learn the content and process of modern scientific inquiry by engaging in research and by communicating their findings to others. Thus, precollege teachers and students need opportunities to gain mentored laboratory research experience and have access to national and international competitions such as the Westinghouse Science Talent Search and the International Science and Engineering Fair (ISEF). The Rockefeller University’s Precollege Science Education Outreach Program (Science Outreach) has trained over 250 students from diverse schools and 30 teachers since
Measuring Distributional Inequality: Relative Body Mass Index Distributions by Gender, Race/Ethnicity, and Education, United States (1999–2006)

Few studies consider obesity inequalities as a distributional property. This study uses relative distribution methods to explore inequalities in body mass index (BMI; kg/m2). Data from 1999–2006 from the National Health and Nutrition Examination Survey were used to compare BMI distributions by gender, Black/White race, and education subgroups in the United States. For men, comparisons between Whites and Blacks show a polarized relative distribution, with more Black men at increased risk of over or underweight. Comparisons by education (overall and within race/ethnic groups) effects also show a polarized relative distribution, with more cases of the least educated men at the upper and lower tails of the BMI distribution. For women, Blacks have a greater probability of high BMI values largely due to a right-shifted BMI distribution relative to White women. Women with less education also have a BMI distribution shifted to the right compared to the most educated women.
Learning Neural Network Classifiers with Low Model Complexity

Modern neural network architectures for large-scale learning tasks have substantially higher model complexities, which makes understanding, visualizing and training these architectures difficult. Recent contributions to deep learning techniques have focused on architectural modifications to improve parameter efficiency and performance. In this paper, we derive a continuous and differentiable error functional for a neural network that minimizes its empirical error as well as a measure of the model complexity. The latter measure is obtained by deriving a differentiable upper bound on the Vapnik-Chervonenkis (VC) dimension of the classifier layer of a class of deep networks. Using standard backpropagation, we realize a training rule that tries to minimize the error on training samples, while improving generalization by keeping the model complexity low. We demonstrate the effectiveness of our formulation (the Low Complexity Neural Network - LCNN) across several deep learning algorithms, and a variety of large benchmark datasets. We show that hidden layer neurons in the resultant networks learn features that are crisp, and in the case of image datasets, quantitatively sharper. Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Micrometer-Sized Titanium Particles Can Induce Potent Th2-Type Responses through TLR4-Independent Pathways

Wear debris in joint replacements has been suggested as a cause of associated tissue-damaging inflammation. In this study, we examined whether solid titanium microparticles (mTi) of sufficient size to accumulate as wear debris could stimulate innate or adaptive immunity in vivo. mTi, administered in conjunction with OVA, promoted total and Ag-specific elevations in serum IgE and IgG1. Analysis of transferred transgenic OVA-specific naive T cells further showed that mTi acted as an adjuvant to drive Ag-specific Th2 cell differentiation in vivo. Assessment of the innate response indicated that mTi induced rapid recruitment and differentiation of alternatively activated macrophages in vivo, through IL-4– and TLR4-independent pathways. These studies suggest that solid microparticles alone can act as adjuvants to induce potent innate and adaptive Th2-type immune responses and further suggest that wear debris in joint replacements may have Th2-type inflammatory properties.
Connect the dot: Computing feed-links for network extension

Road network analysis can require distance from points that are not on the net- work themselves. We study the algorithmic problem of connecting a point inside a face (region) of the road network to its boundary while minimizing the detour factor of that point to any point on the boundary of the face. We show that the optimal single connection (feed-link) can be computed in O(λ7(n )l ogn) time, where n is the number of vertices that bounds the face andλ7(n) is the slightly superlinear maximum length of a Davenport- Schinzel sequence of order 7 on n symbols. We also present approximation results for placing more feed-links, deal with the case that there are obstacles in the face of the road network that contains the point to be connected, and present various related results.
AGILE SOFTWARE DEVELOPMENT: A SURVEY OF EARLY ADOPTERS

In the past few years, anecdotal evidence of mostly positive experiences with the implementation of agile development methods has emerged from case studies. We used a survey strategy, which enables data gathering from a larger sample of organizations, to learn about the factors driving the adoption and use of agile practices, and their benefits and challenges as percieved by early adopters of this software development methodology. Our survey reveals that personal interest is the driving force behind agile adoption, and conversely, organizational resistance and managerial apathy are key inhibitors. The ability to meet client needs and the delivery of quality software products on time are significant benefits of agile development, while a steep learning curve and its unsuitability for projects characterized by distributed environments and large development teams are identified as minor concerns. This article reports these and other findings accompanied by relevant interpretive analysis.
Alternatively spliced type II procollagen mRNAs define distinct populations of cells during vertebral development: differential expression of the amino-propeptide

Type II collagen is a major component of cartilage providing structural integrity to the tissue. Type II procollagen can be expressed in two forms by differential splicing of the primary gene transcript. The two mRNAs either include (type IIA) or exclude (type IIB) an exon (exon 2) encoding the major portion of the amino (NH2)-propeptide (Ryan, M. C., and L. J. Sandell. 1990. J. Biol. Chem. 265:10334-10339). The expression of the two procollagens was examined in order to establish a potential functional significance for the two type II procollagen mRNAs. First, to establish whether the two mRNAs are functional, we showed that both mRNAs can be translated and the proteins secreted into the extracellular environment. Both proteins were identified as type II procollagens. Secondly, to test the hypothesis that differential expression of type II procollagens may be a marker for a distinct population of cells, specific procollagen mRNAs were localized in tissue by in situ hybridization to oligonucleotides spanning the exon junctions. Embryonic vertebral column was chosen as a source of tissue undergoing rapid chondrogenesis, allowing the examination of a variety of cell types related to cartilage. In this issue, each procollagen mRNA had a distinct tissue distribution during chondrogenesis with type IIB expressed in chondrocytes and type IIA expressed in cells surrounding cartilage in prechondrocytes. The morphology of the cells expressing the two collagen types was distinct: the cells expressing type IIA are narrow, elongated, and "fibroblastic" in appearance while the cells expressing type IIB are large and round. The expression of type IIB appears to be correlated with abundant synthesis and accumulation of cartilagenous extracellular matrix. The expression of type IIB is spatially correlated with the high level expression of the cartilage proteoglycan, aggrecan, establishing type IIB procollagen and aggrecan as markers for the chondrocyte phenotype. Transcripts of type II collagen, primarily type IIA, are also expressed in embryonic spinal ganglion. While small amounts of type II collagen have been previously detected in noncartilagenous tissues, the detection of this new form of the collagen in relatively high abundance in embryonic nerve tissue is unique. Taken together, these findings imply a potential functional difference between type IIA and type IIB procollagens and indicate that the removal of exon 2 from the pre-mRNA, and consequently the NH2- propeptide from the collagen molecule, may be an important step in chondrogenesis. In addition, type II procollagen, specifically type IIA, may function in noncartilage tissues, particularly during development.