Miracle-dz/UniFAR-FTUs huggingface dataset:
County and State Health Insurance Coverage Estimation

The Small Area Health Insurance Estimates (SAHIE) program at the U.S Census Bureau is conducting research regarding the model-based estimation of insurance coverage for counties and has produced experimental estimates. There is widespread interest in estimates of health insurance at geographic levels below the state level. While there are several states that produce estimates of insurance coverage for counties within their boundaries, the estimates described here are novel in that they use a uniform method over all of the counties in the U.S. They also make use of several related administrative records, some of which are unavailable to the public. Fisher and Turner(2003) model health insurance coverage, measured by the Annual Social and Economic Supplement to the Current Population Survey (CPS ASEC), as a function of several variables, including administrative records and demographic variables. The primary interest in that paper is to estimate insurance coverage as the CPS ASEC defines it. The Survey of Income and Program Participation (SIPP) was added as a correlated response by Fisher and Turner (2004) with two goals: first, to improve the precision of the estimated CPS ASEC insurance coverage, and, second, to provide estimates of insurance coverage as defined by SIPP. The benefits for the CPS ASEC insurance coverage were small and interest in the SIPP coverage was small, so development of that model has been deferred since the model was complex enough to make fitting a much slower process. One deficiency of the model in Fisher and Turner(2003), labeled FT in this paper, is the variance estimation. There is evidence in that paper that the variances, as modeled, do not fit the data that well. Improvements to the variance model are incorporated here. Since the 2003 and 2004 papers, the models have been presented to several organizations including the State Data Centers (SDC) and the Federal and State Cooperative Program for Population Estimates (FSCPE). These groups had several suggestions, mostly for new covariates. We have also been considering the interpretability of the covariates and their coefficients and have concluded that a different set is indicated.
SILICATE EVOLUTION IN BROWN DWARF DISKS

We present a compositional analysis of the 10 μm silicate spectra for brown dwarf disks in the Taurus and Upper Scorpius (UppSco) star-forming regions, using archival Spitzer/Infrared Spectrograph observations. A variety in the silicate features is observed, ranging from a narrow profile with a peak at 9.8 μm, to nearly flat, low-contrast features. For most objects, we find nearly equal fractions for the large-grain and crystalline mass fractions, indicating both processes to be active in these disks. The median crystalline mass fraction for the Taurus brown dwarfs is found to be 20%, a factor of ∼2 higher than the median reported for the higher mass stars in Taurus. The large-grain mass fractions are found to increase with an increasing strength in the X-ray emission, while the opposite trend is observed for the crystalline mass fractions. A small 5% of the Taurus brown dwarfs are still found to be dominated by pristine interstellar medium-like dust, with an amorphous submicron grain mass fraction of ∼87%. For 15% of the objects, we find a negligible large-grain mass fraction, but a >60% small amorphous silicate fraction. These may be the cases where substantial grain growth and dust sedimentation have occurred in the disks, resulting in a high fraction of amorphous submicron grains in the disk surface. Among the UppSco brown dwarfs, only usd161939 has a signal-to-noise ratio high enough to properly model its silicate spectrum. We find a 74% small amorphous grain and a ∼26% crystalline mass fraction for this object.
Unified Constraint Propagation on Multi-View Data

This paper presents a unified framework for intra-view and inter-view constraint propagation on multi-view data. Pairwise constraint propagation has been studied extensively, where each pairwise constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to inter-view constraint propagation, which is more challenging since each pairwise constraint is now defined over a pair of data points from different views. Although both intra-view and inter-view constraint propagation are crucial for multi-view tasks, most previous methods can not handle them simultaneously. To address this challenging issue, we propose to decompose these two types of constraint propagation into semi-supervised learning subproblems so that they can be uniformly solved based on the traditional label propagation techniques. To further integrate them into a unified framework, we utilize the results of intra-view constraint propagation to adjust the similarity matrix of each view and then perform inter-view constraint propagation with the adjusted similarity matrices. The experimental results in cross-view retrieval have shown the superior performance of our unified constraint propagation.
Role reversal within the mentoring dyad: collaborative mentoring on the effective instruction of English language learners

Two purposes guided this mixed‐methods investigation of the collaborative mentoring of teachers in a large school system in the south‐eastern United States. The first was to examine collaborative mentoring as unstructured peer‐to‐peer coaching that emerged spontaneously as teachers shared experiences about effectively teaching English language learners (ELLs). The second was to examine how licensure courses contributed to the emergence of collaborative mentoring. Data collection consisted of questionnaires, mentoring stories, phone interviews, and electronic surveys. After completing courses, 84 teachers reported significant increases in frequency and duration of interactions for sharing best practices with colleagues. The data analysts identified course components, causal links, and catalysts as having created conditions for mentoring. Of 33 novice teachers recently trained in teaching ELLs, most found themselves mentoring veteran teachers yet untrained in teaching this student group. This unexpected reversal of the traditional novice/veteran roles within mentoring dyads addresses a gap in the educational literature.
Hec1p, an Evolutionarily Conserved Coiled-Coil Protein, Modulates Chromosome Segregation through Interaction with SMC Proteins

ABSTRACT hsHec1p, a Homo sapiens coiled-coil-enriched protein, plays an important role in M-phase progression in mammalian cells. ASaccharomyces cerevisiae protein, identical to Tid3p/Ndc80p and here designated scHec1p, has similarities in structure and biological function to hsHec1p. Budding yeast cells deleted in the scHEC1/NDC80 allele are not viable, but this lethal phenotype can be rescued by hsHEC1 under control of the endogenous scHEC1 promoter. At the nonpermissive temperature, significant mitotic delay, chromosomal missegregation, and decreased viability were observed in yeast cells with temperature-sensitive (ts) alleles of hsHEC1. In the hshec1-113 ts mutant, we found a single-point mutation changing Trp395 to a stop codon, which resulted in the expression of a C-terminally truncated 45-kDa protein. The binding of this mutated protein, hshec1-113p, to five identified hsHec1p-associated proteins was unchanged, while its binding to human SMC1 protein and yeast Smc1p was ts. Hec1p also interacts with Smc2p, and the binding of the mutated hshec1-113p to Smc2p was not ts. Overexpression of either hsHEC1 or scHEC1 suppressed the lethal phenotype of smc1-2 and smc2-6 at nonpermissive temperatures, suggesting that the interactions between Hec1p and Smc1p and -2p are biologically significant. These results suggest that Hec1 proteins play a critical role in modulating chromosomal segregation, in part, through their interactions with SMC proteins.
LOOKING FOR MINIMUM CONDITIONS FOR FF-TLOM CUTTING , BASED ON EQUAL BLADE TANGENCIES AT BOTH LAYER CONTOURS

The Freeform Thick Layered Object Manufacturing (FF-TLOM) process is very suitable for the production of very large objects. The technology is based on flexible blade cutting of thick foam layers, which are stacked to create a physical model. Positioning the tool under accurate cutting constraints is a complex and difficult task, since the tool setting is controlled by 6 degrees of freedom. The tool setting in combination with the blade matching with the required shape can be performed in many ways. In this paper the tool path is explored for convex and concave layer contours. Circle arcs approximate the contours, the circle arcs are combined into cone surfaces, the cone conditions are applied for feasible orientations of a straight tool. The orientation of the tool is fixed, next the flexible blade curve is matched with the local nominal shape curve and finally the tool position needs a correction. The manufacturing preparation is easy to perform for these cases. It is a method, which supports optimal flexible blade cutting, however, this method is not applicable in all cases.
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering

This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team’s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.
Hypertree Decomposition for Solving Constraint Satisfaction Problems

This paper deals with the structural decomposition methods and their use for solving Constraint Satisfaction problems (CSP). mong the numerous structural decomposition methods, hypertree decomposition has been shown to be the most general CSP decomposition. However so far the exact methods are not able to find optimal decomposition of realistic instances in a reasonable CPU time. We present Alea, a new heuristic to compute hypertree decomposition. Some experiments on a serial of benchmarks coming from the literature or the industry permit us to observe that Alea is in general better or comparable to BE (Bucket Elimination), the best well known heuristic, while it generally outperforms DBE (Dual Bucket Elimination), another successful heuristic. We also experiment an algorithm (acyclic solving algorithm) for solving an acyclic CSP obtained by using the heuristic Alea. The experimental results we obtain are promising comparing to those obtained by solving CSP using an enumerative search algorithm.
An fMRI study of face encoding and recognition in first-episode schizophrenia

Background: Schizophrenia has been associated with limited abilities to interact effectively in social situations. Face perception and ability to recognise familiar faces are critical for social interaction. Patients with chronic schizophrenia are known to show impaired face recognition. Studying first-episode (FE) patients allows the exclusion of confounding effects of chronicity, medication and institutionalisation in this deficit. Objective: To determine brain (dys)functions during a face encoding and recognition paradigm in FE schizophrenia. Methods: Thirteen antipsychotic-naïve FE schizophrenia patients and 13 age- and sex-matched healthy controls underwent functional magnetic resonance imaging during a face encoding and recognition paradigm. Behavioural responses were recorded on line. Results: Patients recognised significantly fewer of previously presented faces than the controls (p = 0.008). At the neural level, both groups activated a network of regions including the fusiform area, occipital, temporal and frontal regions. In brain activity, the two groups did not differ in any region during encoding or recognition conditions (p > 0.05, corrected or uncorrected). Conclusions: Our findings show impaired face recognition without a significant alteration of related brain activity in FE schizophrenia patients. It is possible that neural changes become more strongly evident with progression of the illness, and manifest themselves as behavioural impairments during the early course.
The Evolution of X-Ray Clusters in a Cold Plus Hot Dark Matter Universe

We present the first self-consistently computed results on the evolution of X-ray properties of galaxy clusters in a Cold + Hot Dark Matter (CHDM) model. We have performed a hydrodynamic plus N-body simulation for the COBE-compatible CHDM model with standard mass components: Omega(hot) = 0.3, Omega(cold) = 0.6 and Omega(baryon) = 0.1 (h = 0.5). In contrast with the CDM model, which fails to reproduce the observed temperature distribution function dN/dT (Bryan et al. 1994b), the CHDM model fits the observational dN/dT quite well. Our results on X-ray luminosity are less firm but even more intriguing. We find that the resulting X-ray luminosity functions at redshifts z = 0.0, 0.2, 0.4, 0.7 are well fit by observations, where they overlap. The fact that both temperatures and luminosities provide a reasonable fit to the available observational data indicates that, unless we are missing some essential physics, there is neither room nor need for a large fraction of gas in rich clusters: 10% (or less) in baryons is sufficient to explain their X-ray properties. We also see a tight correlation between X-ray luminosity and gas temperature.
Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning

Combinatorial chemistry and high-throughput screening are revolutionizing the process of lead discovery in the pharmaceutical industry. Large numbers of structures and vast quantities of biological assay data are quickly being accumulated, overwhelming traditional structure/activity relationship (SAR) analysis technologies. Recursive partitioning is a method for statistically determining rules that classify objects into similar categories or, in this case, structures into groups of molecules with similar potencies. SCAM is a computer program implemented to make extremely efficient use of this methodology. Depending on the size of the data set, rules explaining biological data can be determined interactively. An example data set of 1650 monoamine oxidase inhibitors exemplifies the method, yielding substructural rules and leading to general classifications of these inhibitors. The method scales linearly with the number of descriptors, so hundreds of thousands of structures can be analyzed utilizing thousands to millions of molecular descriptors. There are currently no methods to deal with statistical analysis problems of this size. An important aspect of this analysis is the ability to deal with mixtures, i.e., identify SAR rules for classes of compounds in the same data set that might be binding in different ways. Most current quantitative structure/activity relationship methods require that the compounds follow a single mechanism. Advantages and limitations of this methodology are presented.
Robust mixed-size placement under tight white-space constraints

A novel and very simple correct-by-construction top-down methodology for high-utilization mixed-size placement is presented. The Polarbear algorithm combines recursive cut-size-driven partitioning with fast and scalable legalization of every placement subproblem generated by every partitioning. The feedback provided by the legalizer at all stages of partitioning improves final placement quality significantly on standard IBM benchmarks and dramatically on low-white-space adaptations of them. Compared to Feng Shui 5.1 and Capo 9.3, Polarbear is the only tool that can consistently find high-quality placements for benchmarks with less than 5% white space. With white space at 5%, Polarbear beats Capo 9.3 by 10% in average total wirelength while Feng Shui 5.1 frequently fails to find legal placements altogether. With 20% white space, POLARBEAR still beats Capo 9.3 by 1% and Feng Shui 5.1 by 1% in average total wirelength, in comparable run times.
Real Effects of Financial Reporting Quality and Credibility : Evidence from the PCAOB Regulatory Regime

I examine whether financial reporting quality and credibility affect a company’s financing and investment decisions. I use PCAOB inspections of non-U.S. auditors as exogenous shocks to the reporting quality of non-U.S. companies audited by PCAOB inspected auditors. I then use the subsequent public revelation of the inspection as exogenous shocks to the reporting credibility of non-U.S. companies that employ PCAOB inspected auditors. Using a difference-indifferences design, I find that although PCAOB inspections improve accrual quality for non-U.S. companies audited by the inspected auditors, there is no evidence that these improvements in accrual quality lead to changes in investment, investment efficiency or debt financing. However, I find that when PCAOB inspection reports are subsequently made public, non-U.S. companies audited by PCAOB inspected auditors increase their long-term debt (investment) by 11.5% (10.9%) and become more responsive to their investment opportunities. These effects are stronger for financially constrained companies and companies with non-big four auditors. Overall, the evidence in this paper suggests that regulatory oversight of the auditor helps improve reporting credibility, which in turn facilitates corporate investment by increasing companies’ external financing capacity. I thank Daniel Aobdia, Beth Blankespoor, Lisa De Simone, Michelle Hanlon, Jonas Heese (discussant), Becky Lester, Rodrigo Verdi, and seminar participants at the Dartmouth Accounting Research Conference, PCAOB Center for Economic Analysis, Stanford University, and University of North Carolina for many helpful comments and suggestions. I thank Niketa Shroff for help with data collection. I gratefully acknowledge financial support from the MIT Junior Faculty Research Assistance Program. All errors are my own.
Detecting laterally transferred genes: use of entropic clustering methods and genome position

Most parametric methods for detecting foreign genes in bacterial genomes use a scoring function that measures the atypicality of a gene with respect to the bulk of the genome. Genes whose features are sufficiently atypical—lying beyond a threshold value—are deemed foreign. Yet these methods fail when the range of features of donor genomes overlaps with that of the recipient genome, leading to misclassification of foreign and native genes; existing parametric methods choose threshold parameters to balance these error rates. To circumvent this problem, we have developed a two-pronged approach to minimize the misclassification of genes. First, beyond classifying genes as merely atypical, a gene clustering method based on Jensen–Shannon entropic divergence identifies classes of foreign genes that are also similar to each other. Second, genome position is used to reassign genes among classes whose composition features overlap. This process minimizes the misclassification of either native or foreign genes that are weakly atypical. The performance of this approach was assessed using artificial chimeric genomes and then applied to the well-characterized Escherichia coli K12 genome. Not only were foreign genes identified with a high degree of accuracy, but genes originating from the same donor organism were effectively grouped.
Mandarin Consonant Contrast Recognition Among Children With Cochlear Implants or Hearing Aids and Normal-Hearing Children

Hypothesis The purpose of the present study was to investigate the consonant recognition of Mandarin-speaking children with cochlear implants (CIs) and hearing aids (HAs) and to determine if they reach a level of consonant recognition similar to that of normal-hearing (NH) children. Background Little information is available in the literature regarding the consonant perception abilities of prelingually deafened young children with either CIs or HAs. No studies have compared Mandarin-Chinese consonant contrast recognition in CI and HA children. Methods Forty-one prelingually deafened children with CIs, 26 prelingually deafened children with HAs, and 30 NH children participated in this study. The 3 groups were matched for chronologic age (3–5 yr). The hearing-impaired groups were matched for age at fitting of the devices, duration of device use, and aided hearing threshold. All subjects completed a computerized Mandarin consonant phonetic contrast perception test. Results CI and HA children scored, on average, approximately 8 percentage points below the mean NH group performance on the consonant contrast recognition. Approximately 40% of the CI and HA children had not reached a performance level of the NH group. No significant differences in the consonant recognition scores were found between the CI and HA groups. Age of implantation was correlated with consonant contrast recognition in the CI group. Conclusion When age at fitting of the devices, duration of device use, and aided thresholds are matched at the group level, consonant recognition is similar between the CI and HA children after 2 years of device use. Early implantation tends to yield better consonant contrast recognition in the young children with CIs. However, a large amount of variance in performance was not accounted for by the demographic variables studied.
Complete Genome Sequence of Francisella endociliophora Strain FSC1006, Isolated from a Laboratory Culture of the Marine Ciliate Euplotes raikovi

ABSTRACT A strain of Francisella endociliophora was isolated from a laboratory culture of the marine ciliate Euplotes raikovi. Here, we report the complete genome sequence of the bacterial strain FSC1006 (Francisella Strain Collection, Swedish Defence Research Agency, Umeå, Sweden).
Analytical Protein Microarrays: Advancements Towards Clinical Applications

Protein microarrays represent a powerful technology with the potential to serve as tools for the detection of a broad range of analytes in numerous applications such as diagnostics, drug development, food safety, and environmental monitoring. Key features of analytical protein microarrays include high throughput and relatively low costs due to minimal reagent consumption, multiplexing, fast kinetics and hence measurements, and the possibility of functional integration. So far, especially fundamental studies in molecular and cell biology have been conducted using protein microarrays, while the potential for clinical, notably point-of-care applications is not yet fully utilized. The question arises what features have to be implemented and what improvements have to be made in order to fully exploit the technology. In the past we have identified various obstacles that have to be overcome in order to promote protein microarray technology in the diagnostic field. Issues that need significant improvement to make the technology more attractive for the diagnostic market are for instance: too low sensitivity and deficiency in reproducibility, inadequate analysis time, lack of high-quality antibodies and validated reagents, lack of automation and portable instruments, and cost of instruments necessary for chip production and read-out. The scope of the paper at hand is to review approaches to solve these problems.