Miracle-dz/UniFAR-FTUs huggingface dataset:
Cutaneous melanoma: hints from occupational risks by anatomic site in Swedish men

Aims: To improve knowledge of the epidemiology of melanoma by comparing occupational risks of cutaneous melanoma (CM) by anatomic site in Swedish workers. Methods: Male workers employed in 1970 and living in the country in 1960 were followed up from 1971 to 1989 using the Swedish Registers of Death and Cancer. A more specifically exposed subcohort included men reporting the same occupation in 1960 and 1970. For each location, occupational risk ratios (RRs) were extracted from Poisson regression models adjusted by age, period, town size, and geographical area. To diminish the influence of socioeconomic factors, intrasector analyses, comparing only jobs belonging to the same occupational sector, were performed. Risk patterns for different locations were compared. Results: High RRs for different sites were found among workers exposed to UV sources (dentists, physiotherapists, and lithographers), and sun exposed workers (harbour masters, and lighthouse/related work). Risk excesses were seen in fur tailors, tanners/fur dressers, patternmakers/cutters, electrical fitters/wiremen, telephone/telegraph installers/repairmen, and some glass/pottery/tile workers. Results for lower and upper limbs were significantly correlated but somewhat independent of those found in thorax, the most frequent location. Correlation between head/neck and thorax was moderate. Specific risk excesses were found for rolling mill workers in head/neck, for chimney sweeps in upper limbs, and for aircraft pilots/navigators/flight engineers in lower limbs. Conclusions: High RRs in the trunk among occupations with UV exposure from artificial sources suggest an effect not restricted to exposed sites. An unusual distribution of cases and RRs in chimney sweeps, rolling-mill, or glass/pottery/tile workers suggests local effects of exposures. The not previously reported risk excess in this job and in fur related processes, and the RR in electrical fitters and telephone/telegraph installers deserve further investigation. Disparities between locations, as RRs in thorax and limbs, may reflect differences in aetiological mechanisms.
Branes in the 2 D black hole

We present a comprehensive analysis of branes in the euclidean 2D black hole (cigar). In particular, exact boundary states and annulus amplitudes are provided for D0branes which are localized at the tip of the cigar as well as for two families of extended D1 and D2-branes. Our results are based on closely related studies for the euclidean AdS3 model [1] and, as predicted by the conjectured duality between the 2D black hole and the sine-Liouville model, they share many features with branes in Liouville theory. New features arise here due to the presence of closed string modes which are localized near the tip of the cigar. The paper concludes with some remarks on possible applications to exact tachyon condensation and matrix models.
Single-beat estimation of end-diastolic pressure-volume relationship: a novel method with potential for noninvasive application.

Whereas end-systolic and end-diastolic pressure-volume relations (ESPVR, EDPVR) characterize left ventricular (LV) pump properties, clinical utility of these relations has been hampered by the need for invasive measurements over a range of pressure and volumes. We propose a single-beat approach to estimate the whole EDPVR from one measured volume-pressure (Vm and Pm) point. Ex vivo EDPVRs were measured from 80 human hearts of different etiologies (normal, congestive heart failure, left ventricular assist device support). Independent of etiology, when EDPVRs were normalized (EDPVRn) by appropriate scaling of LV volumes, EDPVRns were nearly identical and were optimally described by the relation EDP = An.EDV (Bn), with An = 28.2 mmHg and Bn = 2.79. V0 (the volume at the pressure of approximately 0 mmHg) was predicted by using the relation V0 = Vm. (0.6 - 0.006.Pm) and V30 by V30 = V0 + (Vm,n - V0)/(Pm/An) (1/Bn). The entire EDPVR of an individual heart was then predicted by forcing the curve through Vm, Pm, and the predicted V0 and V30. This technique was applied prospectively to the ex vivo human EDPVRs not used in determining optimal An and Bn values and to 36 in vivo human, 12 acute and 14 chronic canine, and 80 in vivo and ex vivo rat studies. The root-mean-square error (RMSE) in pressure between measured and predicted EDPVRs over the range of 0-40 mmHg was < 3 mmHg of measured EDPVR in all settings, indicating a good predictive value of this approach. Volume-normalized EDPVRs have a common shape, despite different etiology and species. This allows the entire curve to be predicted by a new method with a potential for noninvasive application. The results are most accurate when applied to groups of hearts rather than to individual hearts.
On the influence of input noise on a generalization error estimator

Estimating the generalization capability is one of the most important problems in supervised learning. Therefore, various generalization error estimators have been proposed so far, in the presence of noise in output values. On the other hand, noise often exists in input values as well as output values. In this paper, we therefore investigate the influence of input noise on a generalization error estimator. We focus on a particular generalization error estimator called the subspace information criterion (SIC), which is shown to be unbiased in the absence of input noise. Intuitively, small input noise does not seem to affect the unbiasedness of SIC severely because small input noise varies the output values only slightly if the learning target function is continuous. On the contrary to this intuition, we show that even small input noise can totally corrupt the unbiasedness of SIC. This fact casts doubt on the use of SIC in the presence of input noise. To cope with this problem, we provide a sufficient condition to guarantee that SIC is unbiased in the limit of small input noise. We finally show that this condition is always fulfilled when the standard ridge estimation is used for learning, which allows us to use SIC without concern even in the presence of small input noise.
HoneyC - The low-interaction client honeypot

Client honeypots crawl the Internet to find and identify servers that exploit client-side vulnerabilities. Traditionally, these servers are identified by the client honeypot monitoring state changes that result from a server interaction. These, so called high-interaction, client honeypots are slow and expensive to operate because they require an entire operating system to be hosted. We have developed a componentbased low-interaction client honeypot that emulates only the essential features of our target clients and that applies signature matching to allow fast static analysis of server responses. Performance measurements of a prototype implementation targeting clients using the HTTP 1.1 protocol indicate that low-interaction client honeypots are faster and cheaper than high-interaction client honeypots. The difference in false negatives suggests that these technologies may be complementary rather than competitive in nature.
Type Ia supernovae and the formation history of early‐type galaxies

Using the standard prescription for the rates of supernovae Type II and Type Ia, we compare the predictions of a simple model of star formation in galaxies with the observed radial gradients of abundance ratios in a sample of early-type galaxies to infer the relative contribution of each type of supernova. The data suggest a correlation between the fractional contribution of Type Ia to the chemical enrichment of the stellar populations (1 - ξ) and central velocity dispersion of order 1 - ξ ∼ 0.16 log σ 0 + 0.40, so that the Type Ia contribution in stars ranges from a negligible amount in massive (σ 0 ∼ 300 km s - 1 ) galaxies up to 10 per cent in low-mass (∼100 km s - 1 ) elliptical galaxies. Our model is parametrized by a star-formation time-scale (t S F ) that controls the duration of the starburst. A correlation with galaxy radius as a power law (t S F r β ) translates into a radial gradient of the abundance ratios. The data imply a wide range of formation scenarios for a simple model that fixes the luminosity profile, ranging from inside out (β = 2), to outside in formation (β = -1), as is consistent with numerical simulations of elliptical galaxy formation. An alternative scenario that links t S F to the dynamical time-scale favours inside-out formation over a smaller range 0.4 < β < 0.6. In both cases, massive galaxies are predicted to have undergone a more extended period of star formation in the outer regions with respect to their low-mass counterparts.
Relevance-Based Ranking of Video Comments on YouTube

There are many web platforms that are used to share non-textual content, such as videos, images, animations, etc. and that allow users to add comments for each item. YouTube is probably the most popular of them, with billions of videos uploaded by its users, but also with billions of comments for all these videos. While most of the videos only have a couple of comments, the most debated ones have tens of million comments. However, the platform does not provide any mechanism to filter the comments in order to get the most relevant ones, as the only provided ordering is in descending order of the publishing date. We propose a ranking mechanism for these comments that tries to determine the relevance of each comment to the individual video by automatically linking it to relevant web pages with textual information. Three different ranking methods are discussed and their results are presented in order to offer a comparison among them.
Optimal Wireless Sensor Network Design for Efficient Energy Utilization

This paper presents a study of the optimal network design for efficient energy utilization in continuous data-gathering Wireless Sensor Networks (WSNs). We examine the problem of minimizing the network cost through the minimum number of relay-station installation. We model the network design problem as a mixed integer linear programming. Our key contribution is that the proposed model not only guarantee the network lifetime but also ensure the radio communication between the energy-limited sensor nodes so that the network can guarantee packet delivery from sensor nodes to the base station. Numerical experiments were conducted to evaluate and demonstrate the effectiveness of the proposed method.
Bioinformatics Original Paper Sequence Features of Dna Binding Sites Reveal Structural Class of Associated Transcription Factor

Motivation: A key goal in molecular biology is to understand the mechanisms by which a cell regulates the transcription of its genes. One important aspect of this transcriptional regulation is the binding of transcription factors (TFs) to their specific cis-regulatory counterparts on the DNA. TFs recognize and bind their DNA counterparts according to the structure of their DNA-binding domains (e.g. zinc finger, leucine zipper, homeodomain). The structure of these domains can be used as a basis for grouping TFs into classes. Although the structure of DNA-binding domains varies widely across TFs generally, the TFs within a particular class bind to DNA in a similar fashion, suggesting the existence of class-specific features in the DNA sequences bound by each class of TFs. Results: In this paper, we apply a sparse Bayesian learning algorithm to identify a small set of class-specific features in the DNA sequences bound by different classes of TFs; the algorithm simultaneously learns a true multi-class classifier that uses these features to predict the DNA-binding domain of the TF that recognizes a particular set of DNA sequences. We train our algorithm on the six largest classes in TRANSFAC, comprising a total of 587 TFs. We learn a six-class classifier for this training set that achieves 87% leave-one-out cross-validation accuracy. We also identify features within cis-regulatory sequences that are highly specific to each class of TF, which has significant implications for how TF binding sites should be modeled for the purpose of motif discovery.
Electrocardiogram synthesis using Gaussian and fourier models

Electrocardiogram (ECG) is an important tool for investigation of cardiac functions. ECG synthesis or modeling can be useful for biomedical applications involving data compressions, signal analysis and testing of medical systems. In this paper, we present a morphological modeling method of single lead ECG by two different approaches, viz., Fourier and Gaussian models. Single lead ECG data was preprocessed to remove unwanted noise and segmented in three zones, P-R, Q-R-S and S-T. The individual segments were then modeled to extract model coefficients. The residual of each segment, computed as difference between original and reconstructed samples were also modeled using Fourier model. The algorithms were validated with lead V2 from normal and Anterior Myocardial Infarction (AMI) data from Physionet. With 25 AMI datasets, the average PRDN and SNR were found to be 9.33 and 20.71 respectively with Gaussian model and 5.43 and 22.16 respectively with Fourier model. The Fourier model showed better reconstruction performance, but less memory efficient compared to the Gaussian model.
Segmentation of lung CT images with random walk algorithm

Lung segmentation operation is handling a very important role for determining unhealthy tissues and thus diagnosis and treatment of lung cancer. In this work Lung CT images are segmented by using Random Walk algorithm. First 25 DICOM images belong to 4 patients are read for processing and 8-bit gray level images obtained. For interactive segmentation, the seed points are set and applied as input parameters to the algorithm. Using the output values, the segmentation finished with an accuracy of average %95.
Group Practice of the Transcendental Meditation® and TM-Sidhi® Program and Reductions in Infant Mortality and Drug-Related Death

These two studies tested the prediction that the group practice of a procedure for the development of consciousness, the Transcendental Meditation and TM-Sidhi program, by a sufficiently large group of individuals would be sufficient to reduce collective stress in the larger population, reflected in two stress-related health indicators, infant mortality rate and drug-related fatality rate. Based on theoretical prediction and prior research, from January 2007 through 2010 (intervention period), this effect should have been measurable. Change in the rates of these two indicators during the intervention period were estimated from 2002 through 2010 data using a broken-trend (or segmented trend) intervention model with time series regression methods. Significant changes in trend for both the infant mortality rate and drug-related fatality rate were evident at the predicted time and in the predicted direction, controlling for preintervention trends, seasonality, and autocorrelation. The changes in trend were both statistically and practically significant, indicating an average annual decline of 3.12% in infant mortality rate and 7.61% in drug-related fatality rate. Diagnostic tests are satisfactory and indicate that it is unlikely that the statistical results are attributable to spurious regression. The mechanism for these collective effects is discussed in view of possible alternative hypotheses.
Contract bridge as a Micro-world for reasoning about communication agents

We argue that bidding in the game of Contract Bridge can profitably be regarded as a micro-world suitable for experimenting with pragmatics. We sketch an analysis in which a "bidding system" is treated as the semantics of an artificial language, and show how this "language", despite its apparent simplicity, is capable of supporting a wide variety of common speech acts parallel to those in natural languages; we also argue that the reason for the relatively unsuccessful nature of previous attempts to write strong Bridge playing programs has been their failure to address the need to reason explicitly about knowledge, pragmatics, probabilities and plans. We give an overview of Pragma, a system currently under development at SICS, which embodies these ideas in concrete form, using a combination of rule-based inference, stochastic simulation, and "neural-net" learning. Examples are given illustrating the functionality of the system in its current form.
Correction to: A Bifactor Approach to the Factor Structure Study of the CES-D Scale

Background: The purpose of this study was to examine the factor structure of the Korean version of CES-D scale by applying the bifactor model. Many studies for validating the CES-D scale have supported the four-factor structure. But some studies found that the three factor structure provided a more appropriate solution. Such inconsistency in the number and structure of factors led us to conduct two studies to clarify the structure of dimensionality of the CES-D-K scale. Methods: In study 1, we factor-analyzed the response patterns to the abbreviated CES-D-K scale (11-item, N=12,309) included the Korean Welfare Panel Study. In study 2, we factor-analyzed the data obtained from 223 college and graduate students who responded to the 20-item CES-D-K scale. Correlational analyses were also conducted to investigate criterion validity of the CES-D-K scale with external variables that are theoretically related to depression. The fit indexes of a single-factor model, a four-factors model, and a bifactor model were compared. The Omega coefficients and Explained Common Variance (ECV) were also computed to evaluate the psychometric properties of the CES – D-K scale more accurately. Results: The results showed that the CES-D-K scale has a high value of Omega-Hierarchical for the total score, low values of Omega-Hierarchical for the subscale scores, and a high ECV value. Conclusions: Therefore, we concluded that, as the scale with a strong general factor, the use of the scale score can accomplish the goal of measuring individual differences on the target construct of depression with little to no gain from constructing subscale scores.
Building a clustered semantic network for an Entire Large Dictionary of Japanese

In this paper, we develop a new method of solving the cluster-size imbalance problem observed when documents and corpora are processed with the Markov Clustering (MCL) Algorithm (Van Dongen, 2000), which is widely recognized as an efficient approach to graph clustering. The Branching MCL (BMCL) and its variant, Reverse Branching MCL (RBMCL) can resize overly inclusive Markov clusters (core clusters) into appropriate subsets. The method, together with the Recurrent MCL (RMCL) that integrates concepts of similar words (Jung, 2006), is applied to a semantic network built from the large-scale corpus of Gakken's Large Dictionary of Japanese (GLDJ), covering 100,000 words, definitions, examples, and grammatical explanations. The effectiveness of these techniques is currently being tested by creating a clustered semantic network for the GLDJ.
The Anisotropic Spatial Distribution of CDM Subhalos

I review recent results on the spatial distribution of substructure in CDM halos. I show that the spatial distribution of subhalos is anisotropic and generally prolate with a long axis that is closely aligned with the long axis of the total mass distribution of the host halo. I show that the correlation between the subhalo distribution and the long axis of the host halo is strong both in dissipationless and dissipational gasdynamical simulations. More massive subhalos tend to be more strongly clustered along the major axis of the host halo reflecting filamentary accretion. The anisotropy of subhalos has potential implications for the interpretation of several observations in the Local Group and beyond. For example, I show that while the mean projected mass fraction in substructure in the central regions of CDM halos is fsub \approx 0.4%, fsub is a strong function of projection angle and is ~ 5 to 6 times higher for projections that are nearly collinear with the major axis of the host halo.
Consideration about Applicability of Recommender System Employing Personal-Value-Based User Model

This paper presents consideration about applicability of recommender system based on personal-value-based user model. Existing methods such as collaborative and content-based approaches tend to be less-accurate for new users and items owing to the lack of the relation between items and users' preference. While existing recommender systems usually employ user preference of items to make recommendations, proposed method focuses on users' personal values, which mean value judgments regarding on which attributes users put a high priority. Proposed recommender system employing personal-value-based user model is therefore expected to realize more precise recommendation in cold-start situations. Moreover, the tendency of personal values could be categorized into several types, which would affect the ratings for recommended items. This paper shows experimental results of recommendation for several user types, based on which relation between user type and recommendation is discussed.
Genomic variant calling: Flexible tools and a diagnostic data set

The accurate identification of low-frequency variants in tumors remains an unsolved problem. To support characterization of the issues in a realistic setting, we have developed software tools and a reference dataset for diagnosing variant calling pipelines. The dataset contains millions of variants at frequencies ranging from 0.05 to 1.0. To generate the dataset, we performed whole-genome sequencing of a mixture of two Corriel cell lines, NA19240 and NA12878, the mothers of YRI (Y) and CEU (C) HapMap trios, respectively. The cells were mixed in three different proportions, 10Y/90C, 50Y/50C and 90Y/10C, in an effort to simulate the heterogeneity found in tumor samples. We sequenced three biological replicates for each mixture, yielding approximately 1.4 billion reads per mixture for an average of 64X coverage. Using the published genotypes as our reference, we evaluate the performance of a general variant calling algorithm, constructed as a demonstration of our flexible toolset, and make comparisons to a standard GATK pipeline. We estimate the overall FDR to be 0.028 and the FNR (when coverage exceeds 20X) to be 0.019 in the 50Y/50C mixture. Interestingly, even with these relatively well studied individuals, we predict over 475,000 new variants, validating in well-behaved coding regions at a rate of 0.97, that were not included in the published genotypes.
Multifrequency integrated profiles of pulsars

We have observed a total of 67 pulsars at five frequencies ranging from 243 to 3100 MHz. Observations at the lower frequencies were made at the Giant Metre-Wave Telescope in India and those at higher frequencies at the Parkes Telescope in Australia. We present profiles from 34 of the sample with the best signal-to-noise ratio and the least scattering. The general 'rules' of pulsar profiles are seen in the data; profiles get narrower, the polarization fraction declines and outer components become more prominent as the frequency increases. Many counterexamples to these rules are also observed, and pulsars with complex profiles are especially prone to rule breaking. We hypothesize that the location of pulsar emission within the magnetosphere evolves with time as the pulsar spins down. In highly energetic pulsars, the emission comes from a confined range of high altitudes, in the middle range of spin down energies the emission occurs over a wide range of altitudes whereas in pulsars with low spin-down energies it is confined to low down in the magnetosphere.