Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Galactic Distribution of Planets. I. Methodology and Planet Sensitivities of the 2015 High-cadence Spitzer Microlens Sample

We analyze an ensemble of microlensing events from the 2015 Spitzer microlensing campaign, all of which were densely monitored by ground-based high-cadence survey teams. The simultaneous observations from Spitzer and the ground yield measurements of the microlensing parallax vector , from which compact constraints on the microlens properties are derived, including ≲25% uncertainties on the lens mass and distance. With the current sample, we demonstrate that the majority of microlenses are indeed in the mass range of M dwarfs. The planet sensitivities of all 41 events in the sample are calculated, from which we provide constraints on the planet distribution function. In particular, assuming a planet distribution function that is uniform in , where q is the planet-to-star mass ratio, we find a 95% upper limit on the fraction of stars that host typical microlensing planets of 49%, which is consistent with previous studies. Based on this planet-free sample, we develop the methodology to statistically study the Galactic distribution of planets using microlensing parallax measurements. Under the assumption that the planet distributions are the same in the bulge as in the disk, we predict that ∼1/3 of all planet detections from the microlensing campaigns with Spitzer should be in the bulge. This prediction will be tested with a much larger sample, and deviations from it can be used to constrain the abundance of planets in the bulge relative to the disk.
Genomic and proteomic analysis of multiple sclerosis

Multiple sclerosis (MS) and other autoimmune diseases result from the dysregulation of genetic and proteomic programs. In MS, the loss of immune homeostasis leads to aberrant targeting and destruction of the myelin sheath, which manifests as the clinical syndrome of MS. The advent of technologies to perform large-scale analysis of mRNA transcript and protein expression will transform our understanding of the mechanisms underlying the initiation and progression of MS, and will yield new targets for therapeutic intervention.
Influence of Intracerebral Hemorrhage Location on Incidence, Characteristics, and Outcome: Population-Based Study

Background and Purpose— The characteristics of intracerebral hemorrhage (ICH) may vary by ICH location because of differences in the distribution of underlying cerebral small vessel diseases. Therefore, we investigated the incidence, characteristics, and outcome of lobar and nonlobar ICH. Methods— In a population-based, prospective inception cohort study of ICH, we used multiple overlapping sources of case ascertainment and follow-up to identify and validate ICH diagnoses in 2010 to 2011 in an adult population of 695 335. Results— There were 128 participants with first-ever primary ICH. The overall incidence of lobar ICH was similar to nonlobar ICH (9.8 [95% confidence interval, 7.7–12.4] versus 8.6 [95% confidence interval, 6.7–11.1] per 100 000 adults/y). At baseline, adults with lobar ICH were more likely to have preceding dementia (21% versus 5%; P=0.01), lower Glasgow Coma Scale scores (median, 13 versus 14; P=0.03), larger ICHs (median, 38 versus 11 mL; P<0.001), subarachnoid extension (57% versus 5%; P<0.001), and subdural extension (15% versus 3%; P=0.02) than those with nonlobar ICH. One-year case fatality was lower after lobar ICH than after nonlobar ICH (adjusted odds ratio for death at 1 year: lobar versus nonlobar ICH 0.21; 95% confidence interval, 0.07–0.63; P=0.006, after adjustment for known predictors of outcome). There were 4 recurrent ICHs, which occurred exclusively in survivors of lobar ICH (annual risk of recurrent ICH after lobar ICH, 11.8%; 95% confidence interval, 4.6%–28.5% versus 0% after nonlobar ICH; log-rank P=0.04). Conclusions— The baseline characteristics and outcome of lobar ICH differ from other locations.
Minima in branching random walks

Given a branching random walk, let $M_n$ be the minimum position of any member of the $n$th generation. We calculate $\\mathbfEM_n$ to within O(1) and prove exponential tail bounds for $\\mathbfP{|M_n-\\mathbfEM_n|>x}$, under quite general conditions on the branching random walk. In particular, together with work by Bramson [Z. Wahrsch. Verw. Gebiete 45 (1978) 89―108], our results fully characterize the possible behavior of $\\mathbf EM_n$ when the branching random walk has bounded branching and step size.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Business capability models are widely used in enterprise architecture management to generate an abstract overview of an organization’s business activities for reaching its business objectives. The creation and maintenance of these models are associated with a huge manual workload. Research provides insights into opportunities for automated modeling of enterprise architecture models. However, most papers address the application and technology layer and leave the business layer largely unexplored. Particularly, no research has been conducted on the automated generation of business capability models. This research uses 19 semi-structured interviews to identify possible automated modeling opportunities of business capabilities and related challenges. Based on this a business capability mining approach is developed. This research benefits both, practice and theory, by describing a situation-based business capability mining approach and identifying implementation scenarios.
Obtaining the Equation of State for Multiphase Iron under Earth’s Core Conditions using Bayesian Statistics

Iron, a principal element of Earth’s core, is vital for understanding the thermodynamic properties of this region. The accuracy of iron’s equation of state (EOS) is crucial, yet experimental uncertainties signiﬁcantly impact the EOS parameters. By employing Bayesian statistics and Markov Chain Monte Carlo (MCMC) simulations, we have quantiﬁed these uncertainties. Our approach introduced a straightforward yet eﬀective method to calculate the probability of phase boundary data. The resulting EOS reliably reproduces a variety of experimental datasets, including phase boundary experiments, static pressure measurements under various conditions, shock wave data, and sound velocity under diﬀerent states. Using 100 sets of posterior parameter samples, our predictions indicate that the density deﬁcit in Earth’s outer core ranges approximately from 8.7% to 9.7%. Additionally, the inferred geodynamo power output from latent heat release during the cooling and solidiﬁcation process of Earth’s inner core is estimated to be between 0.458 and 6.002 terawatts.
Statistical methods and Bayesian interpretation of evidence in forensic automatic speaker recognition

The goal of this paper is to establish a robust methodology for forensic automatic speaker recognition (FASR) based on sound statistical and probabilistic methods, and validated using databases recorded in real-life conditions. The interpretation of recorded speech as evidence in the forensic context presents particular challenges. The means proposed for dealing with them is through Bayesian inference and corpus based methodology. A probabilistic model – the odds form of Bayes’ theorem and likelihood ratio – seems to be an adequate tool for assisting forensic experts in the speaker recognition domain to interpret this evidence. In forensic speaker recognition, statistical modelling techniques are based on the distribution of various features pertaining to the suspect's speech and its comparison to the distribution of the same features in a reference population with respect to the questioned recording. In this paper, the state-of-the-art automatic, text-independent speaker recognition system, using Gaussian mixture model (GMM), is adapted to the Bayesian interpretation (BI) framework to estimate the within-source variability of the suspected speaker and the between-sources variability, given the questioned recording. This doublestatistical approach (BI-GMM) gives an adequate solution for the interpretation of the recorded speech as evidence in the judicial process.
Performance evaluation of handwritten signature recognition in mobile environments

The utilisation of biometrics in mobile scenarios is increasing remarkably. At the same time, handwritten signature recognition is one of the modalities with highest potential of use for those applications where customers are used to sign in those traditional processes. However, several improvements have to be made in order to reach acceptable levels of performance, reliability and interoperability. The evaluation carried out in this study contributes with multiple results obtained from 43 users signing 60 times, divided in three sessions, in eight different capture devices, being six of them mobile devices and the other two digitisers specially made for signing and used as a baseline. At each session, a total of 20 signatures per user are captured by each device, so that the evaluation here reported a total of 20 640 signatures, stored in ISO/IEC 19794–7 format. The algorithm applied is a DTW-based one, particularly modified for mobile environments. The results analysed include inter-operability, visual feedback and modality tests. One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing. Many relevant conclusions have been collected and, over all, multiple improvements have been reached contributing to future deployments of biometrics in mobile environments.
Nitrogen Retranslocation Response of Young Picea mariana to Nitrogen-15 Supply

Nutrient loading stimulates N retranslocation, an important mechanism of N reuse in plants to support new growth. We quantified N retranslocation in young black spruce [Picea mariana (Mill.) BSP] using tracer and nontracer techniques to examine enhanced field performance after nutrient loading. Nursery reared seedlings were transplanted to sand-filled pots fertilized with 15 NH 4 15 NO 3 at rates equivalent to 0 and 200 kg N ha -1 simulating poor and rich soils. After one growing season (120 d), biomass increased (118%) on the poor soil without N gain demonstrating the significance of internal N reserves for retranslocation to new growth. Nutrient loading improved retranslocation (218%) and new biomass (156%) after planting confirming the advantage of higher preplant N reserves (175%) for later nutrient demand. Enhanced N availability in the rich soil accelerated growth (236%), N uptake (258%), and retranslocation (23%) in seedlings. Retranslocation increased with time reflecting higher N demand as seedlings become larger and suggest the process is driven by sink strength. Nontracer estimates of N retranslocation in seedlings fell short of isotopic determinations because of inability to discriminate between soil and plant derived N in tree components. Although fertilization promoted N uptake (125-258%), 15 N recovery in plants averaged 12 to 19% indicating low fertilizer efficiency in young trees. Total reliance of unfertilized plants on internal N reserves for growth on the poor soil affirms the importance of retranslocation to meet plant N demands, and also exemplifies initial short-term independence on soil N for newly planted seedlings that can be prolonged by nutrient loading.
Economics Working Paper Series 2015 / 007 Incomplete Information , Proportional Representation and Strategic Voting

We introduce incomplete information to a multiparty election under proportional representation: each voter knows her preferences and votes strategically to maximize her payoffs, but is uncertain about the number and the preferences of the other voters. Parties are assumed to be purely office motivated and, hence, the resulting governments are always minimum winning. In this framework we prove a) generic existence of equilibria where only two parties receive a positive fraction of the votes and therefore lead to single party governments and b) generic inexistence of equilibria that lead to coalition governments. That is, contrary to common wisdom, a proportional rule is found not to promote sincere voting and to be favorable towards single party governments. The existence of two-party equilibria that lead to single party governments is robust to parties having ideological concerns.
Neural respiratory drive as a physiological biomarker to monitor change during acute exacerbations of COPD

Background Acute exacerbations of chronic obstructive pulmonary disease have a significant negative impact on both patients and healthcare systems. Currently, there are no physiological biomarkers that effectively monitor clinical change or predict respiratory readmission. Acute exacerbations impose a change in the respiratory muscle load-capacity-drive relationship. It was hypothesised that lack of a fall in neural respiratory drive would identify patients at risk of treatment failure and early hospital readmission. Methods An observational study was performed at two UK teaching hospitals. Routine clinical physiological parameters and neural respiratory drive index (NRDI), calculated as the product of second intercostal space parasternal electromyography (EMG) activity normalised to the peak EMG activity during a maximum inspiratory sniff manoeuvre and respiratory rate, were recorded daily from admission to discharge. Results 30 acutely unwell patients of mean (SD) age 72 (10) years, forced expiratory volume in 1 s 0.60 (1.65) l, NRDI 455 (263) AU and median length of stay 6 days were studied. Changes in NRDI correlated with changes in Borg score (r=+0.60; p<0.001), discriminated between patients deemed to have clinically improved rather than deteriorated (mean difference 339 AU; 95% CI 234 to 444; p<0.001) and identified those patients readmitted within 14 days (mean difference 203 AU; 95% CI 39 to 366; p=0.017). Conclusions NRDI is a feasible clinical physiological parameter in patients with an acute exacerbation of chronic obstructive pulmonary disease and can provide useful information on treatment response and risk of readmission.
Principal Component Networks: Utilizing Low-Rank Activation Structure to Reduce Parameters Early in Training

Recent works show that overparameterized neural networks contain small subnetworks that exhibit comparable accuracy to the full model when trained in isolation. These results highlight the potential to reduce the computational costs of deep neural network training without sacrificing generalization performance. Initial approaches for finding these small networks relied on expensive multi-round train-and-prune procedures, limiting their practical potential, but more recent work identifies subnetworks using structured pruning techniques early in training. In this paper, we study network activations, rather than network weights, and find that hidden layer activations in overparameterized networks exist primarily in subspaces smaller than the actual model width. We further notice that these subspaces can be identified early in training. Based on these observations, we show how to efficiently find small networks that exhibit similar accuracy to their overparameterized counterparts after only a few training epochs. We term these network architectures Principal Component Networks (PCNs). PCNs compress individual layers by retaining only the high variance linear combinations of channels—defined by the principal components of the layer inputs—a key difference from structured pruning techniques which focus on individual channel pruning using localized channel measurements. We evaluate PCNs on CIFAR-10 and ImageNet for VGG and ResNet style architectures and compare against existing methods for subnetwork identification during early training. We find that PCNs consistently reduce parameter counts with little accuracy loss, thus providing the potential to reduce the computational costs of deep neural network training. Beyond model compression, we also connect our observation regarding hidden layer activations to the feature representations learned by neural networks and discuss areas for future work.
KINESIOLOGY & COACHING

Background. A year and a half before karate was scheduled to make its debut appearance in the Olympics in 2020, the World Karate Federation (WKF) elected to change its judging system for kata to a point-based system reminiscent of other Olympic sports. Problem and aim. This paper analyzes data collected from WKF tournaments over the first year of the adoption of the new karate judging system to evaluate their implementation and factors that affected scores and inter-judge agreement. Methods. The result books from the WKF’s 7 Karate1 Premier League and 4 Karate1 Series A League tournament were analyzed (3445 total kata performances). The distribution of scores and the standard deviation of judge scores per kata performance were calculated. Technical and athletic scores of each judge were plotted and Pearson’s correlation coefficient was found. Singular value decomposition (SVD) was used to cluster kata that were often run together by competitors and ANOVA and Tukey’s post hoc test were run on scores from the kata clusters. Results. The frequency of scores follows a normal distribution with a slight negative skew. Judge agreement was affected by the quality of performance where better kata performances had a higher agreement. While judges give two independent scores for each technique and athleticism, there is a strong correlation between the two scores (r=0.932). Clustering and analysis of scores based on kata chosen to run showed that certain kata styles score higher. Conclusions. This paper is the first attempt to evaluate the new kata scoring system and to identify extrinsic factors that affect the scoring.
Genetic variation in Pneumocystis carinii isolates from different geographic regions: implications for transmission.

To study transmission patterns of Pneumocystis carinii pneumonia (PCP) in persons with AIDS, we evaluated P. carinii isolates from patients in five U.S. cities for variation at two independent genetic loci, the mitochondrial large subunit rRNA and dihydropteroate synthase. Fourteen unique multilocus genotypes were observed in 191 isolates that were examined at both loci. Mixed infections, accounting for 17.8% of cases, were associated with primary PCP. Genotype frequency distribution patterns varied by patients' place of diagnosis but not by place of birth. Genetic variation at the two loci suggests three probable characteristics of transmission: that most cases of PCP do not result from infections acquired early in life, that infections are actively acquired from a relatively common source (humans or the environment), and that humans, while not necessarily involved in direct infection of other humans, are nevertheless important in the transmission cycle of P. carinii f. sp. hominis.
Prospective Study of Lipoprotein(a) as a Risk Factor for Atherosclerotic Cardiovascular Disease in Patients With Diabetes

OBJECTIVE To assess whether lipoprotein(a) [Lp(a)] is a risk factor for atherosclerotic cardiovascular disease in diabetes. RESEARCH DESIGN AND METHODS We studied 221 patients with non-insulin-dependent diabetes mellitus (NIDDM) without diabetic complications who were followed for 2.2–3.1 years. Their serum Lp(a) levels were semiquantified by a rapid electrophoretic method that accurately discriminates high from low serum Lp(a) at the 20 mg/dl level. RESULTS Seven of 105 diabetic patients with a high serum Lp(a) experienced a clinical event related to atherosclerotic cardiovascular disease. This incidence was significantly higher than that of the 110 diabetic patients with a low serum Lp(a). The logistic regression analysis revealed that Lp(a) was an independent risk factor for the event. CONCLUSIONS Lp(a) is a significant risk factor for atherosclerotic cardiovascular disease in NIDDM.
Mining complex genotypic features for predicting HIV-1 drug resistance

MOTIVATION Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly. RESULTS Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch-and-bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors (NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature. AVAILABILITY http://www.kyb. mpg.de/bs/people/hiroto/iboost/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Estimation with random linear mixing, belief propagation and compressed sensing

We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilistic measurement channel. The relaxed BP method is a Gaussian approximation of standard BP that offers significant computational savings for dense measurement matrices. The main contribution of this paper is to extend Guo and Wang's relaxed BP method and analysis to general (non-AWGN) output channels. Specifically, we present detailed equations for implementing relaxed BP for general channels and show that the relaxed BP has an identical asymptotic large sparse limit behavior as standard BP as predicted by the Guo and Wang's state evolution (SE) equations. Applications are presented to compressed sensing and estimation with bounded noise.
A methodological framework for data warehouse design

Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.