Miracle-dz/UniFAR-FTUs huggingface dataset:
The Extinction and Distance of Maffei 1

We have obtained low- and high-resolution spectra of the core of the highly reddened elliptical galaxy Maffei 1. From these data, we have obtained the first measurement of the Mg2 index and have measured the velocity dispersion and radial velocity with improved accuracy. To evaluate the extinction, a correlation between the Mg2 index and effective V-I color has been established for elliptical galaxies. Using a new method for correcting for effective wavelength shifts, the V-I color excess reveals that the optical depth of Galactic dust at 1 μm is 1.69 ± 0.07. Thus, AV = 4.67 ± 0.19 mag, which is lower by 0.4 mag than previously thought. To establish the distance, the fundamental plane for elliptical galaxies has been constructed in I. The velocity dispersion of Maffei 1, measured to be 186.8 ± 7.4 km s-1, in combination with modern wide-field photometry in I, leads to a distance of 2.92 ± 0.37 Mpc. The Dn-σ relation, which is independently calibrated, gives 3.08 ± 0.85 and 3.23 ± 0.67 Mpc from photometry in B and K′, respectively. The weighted mean of the three estimates is 3.01 ± 0.30 Mpc, which is lower than distances judged with reference to M32 and the bulge of M31 from the brightest stars seen at K′. Since the luminosity of asymptotic giant branch stars at K′ is strongly dependent on age, the lower distance suggests that the last epoch of star formation in Maffei 1 occurred farther in the past than in these other systems. The distance and luminosity make Maffei 1 the nearest giant elliptical galaxy. In the absence of extinction, the galaxy would be among the brightest in the sky and would have an apparent size that of the full Moon. The radial velocity of Maffei 1 is +66.4 ± 5.0 km s-1, significantly higher than the accepted value of -10 km s-1. The Hubble distance corresponding to the mean velocity of Maffei 1, Maffei 2, and IC 342 is 3.5 Mpc. Thus, it is unlikely that Maffei 1 has had any influence on Local Group dynamics.
Clinical characteristics of major depression that predict risk of depression in relatives.

BACKGROUND Major depression (MD) is both clinically and etiologically heterogeneous. We attempt to relate clinical and etiologic heterogeneity by determining those features of MD that reflect a high familial liability to depressive illness. METHODS Our sample, 3786 personally interviewed twin pairs from a population-based registry, contained 1765 people with a lifetime history of MD by DSM-III-R criteria, of whom 639 (36.2%) had affected co-twins. We examine, using Cox proportional hazard models, the clinical features of MD in affected twins that predicted the risk for MD in the co-twin. Control variables were zygosity, age at interview, and sex of the twin and co-twin. RESULTS The best-fitting model contained 4 significant predictors: number of episodes, duration of longest episode, recurrent thoughts of death or suicide, and level of distress or impairment. These 4 clinical features were similarly predictive of the risk for MD in the co-twins of male and female twins and predicted risk of illness more strongly in monozygotic than in dizygotic twins. Variables that did not uniquely predict risk of MD in the co-twin included age at onset and number of depressive symptoms. For number of episodes, the best-fitting model indicated an inverted U-shaped function with greatest co-twin risk for MD with 7 to 9 lifetime episodes. CONCLUSIONS The clinical features of MD in epidemiologic samples can be meaningfully related to the familial vulnerability to illness. Familial MD is best characterized by intermediate levels of recurrence, long duration of episodes, high levels of impairment, and recurrent thoughts of death or suicide. These clinical features probably reflect a high genetic liability to depressive illness.
Strong evidence in favor of the existence of S-matrix for strings in plane waves

Field theories on the plane wave background are considered. We discuss that for such field theories one can only form 1+1 dimensional freely propagating wave packets. We analyze tree level four point functions of scalar field theory as well as scalars coupled to gauge fields in detail and show that these four point functions are well-behaved so that they can be interpreted as S-matrix elements for 2 particle → 2 particle scattering amplitudes. Therefore, at least classically, field theories on the plane wave background do have S-matrix formulation.
A Unification of Extensive-Form Games and Markov Decision Processes

We describe a generalization of extensive-form games that greatly increases representational power while still allowing efficient computation in the zero-sum setting. A principal feature of our generalization is that it places arbitrary convex optimization problems at decision nodes, in place of the finite action sets typically considered. The possibly-infinite action sets mean we must "forget" the exact action taken (feasible solution to the optimization problem), remembering instead only some statistic sufficient for playing the rest of the game optimally. Our new model provides an exponentially smaller representation for some games; in particular, we show how to compactly represent (and solve) extensive-form games with outcome uncertainty and a generalization of Markov decision processes to multi-stage adversarial planning games.
Comparison of rectification topologies with Matlab/Simulink model simulations

In current era, efficient control of electrical machines is highly desirable. For better control, one cannot go ahead without power electronics. There are various machines currently serving in industries, e.g., AC, DC, etc. To make available the desired power supply for the machines, rectifiers and inverters, etc. are used. There is large number of rectification techniques and some new techniques are still being developed. In this paper, the author compares various topologies for power supply conditioning, specially focusing on rectification process, with help of MATLAB/SIMULINK model simulations.
OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains

Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDS) to encode a planning domain as a non-deterministic finite automaton and then apply fast algorithms from model checking to search for a solution. OBDDS can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying UMOP to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.
Effects of negative ratings on personalized recommendation

In most recommender systems, the active user's preferences can be denoted by multi-graded rating data (1 to 5 in MovieLens etc.). When using the available ratings, some recommendation algorithms transfer multi-graded rating data into binary rating data ignoring the actual value of ratings, while some others just use positive ratings (no smaller than 3 in 1–5 rating structure e.g.) to recommend items for users. In the former case, positive ratings and negative ratings are treated equally while in the latter case the negative ratings are not used at all. In this paper, we use a tunable parameter to combine the positive ratings and the negative ratings, and a diffusion-based process in a weighted bipartite networks to make personal recommendation. We test the proposed method with three data-sets. The results demonstrate that the negative ratings can help to improve the accuracy of recommendation algorithm.
Trust and Betrayal from a Husserlian Standpoint

Abstract This paper provides an interpretation of trust and betrayal within political communities from the perspective of Husserl’s concept of social communities. I situate the paper amidst Margaret Gilbert’s theory of political obligations, arguing that at least one outside conception of trust fills a gap left in her theory. More specifically, I argue for the supplementary fit that Karen Jones’s conception of trust understood as ‘basal security’ provides for Gilbert. From there, I tie this conception of trust and betrayal to Husserl’s notions of ‘original belief’ and socio-cultural crisis. There is thereby a phenomenological elucidation of features within the social world that allow such crises to occur in the first place.
Word sense disambiguation in information retrieval using query expansion

There are two problems in using words to represent document contents and query in information retrieval: ambiguity and different words which represent the same concept. These problems can be addressed by using query expansion. We focused on analysing the implementation of query expansion, word sense disambiguation (WSD), iterated relevance feedback, and some retrieval variations to retrieval performance. In this paper, WSD is implemented in Lucene using query expansion with thesaurus and relevance feedback. Extended Lesk algorithm was re-implemented to disambiguate the query using WordNet. Expansion terms were limited up to 20 words chosen from expansion term candidates from disambiguated query's senses information, co-occurrence terms, and most frequent terms using Kullback-Leibler Distance. We iterated the process to find the best number of expansion iteration. We found that the method using WSD to query can extend search process time to 161 times longer at worst. Query expansion using disambiguated sense information did not affect the performance much while using information from relevance feedback did. This experiment provides better understanding of WSD in information retrieval system performance.
Integrated detection of anomalous behavior of computer infrastructures

Our research concentrates on anomaly detection techniques, which have both industrial applications such as network monitoring and protection, as well as research applications such as software behavioral analysis or malware classification. During our doctoral research, we worked on anomaly detection from three different perspective, as a complex computer infrastructure has several weak spots that must be protected. We first focused on the operating system, central to any computer, to avoid malicious code to subvert its normal activity. Secondly, we concentrated on web applications, which are the main interface to modern computing: Because of their immense popularity, they have indeed become the most targeted entry point of intrusions. Last, we developed novel techniques with the aim of identifying related events (e.g., alerts reported by intrusion detection systems) to build new and more compact knowledge to detect malicious activity on large-scale systems. During our research we enhanced existing anomaly detection tools and also contributed with new ones. Such tools have been tested over different datasets, both synthetic data and real network traffic, and lead to interesting results that were accepted for publication at main security venues.
An Efficient Context Model for Fast Responsiveness of Context-Aware Services in Mobile Networks

In order to provide context-aware services, context modeling is one of the most important tasks. Ontology has been widely accepted for the context modeling but it brings some overheads in the process of reasoning large-scale and dynamic information. In mobile networks, especially, ontology should be verified to be applicable for Context-aware Services since real-time responsiveness for large scale moving users is highly required to be guaranteed. This paper proposes an efficient context model to overcome the limitation of ontology. For the purpose, we design a conceptual context model and compare the processing time of the context-aware mobile service system implying the three different context modeling approaches - ontology, database, and proposed hybrid modeling. As the result of performance evaluations, the proposed hybrid model shows faster processing time among them and lesser affected by the major metrics of mobile services - changeability and number of users - than the two existing models. Based on the experimental results, we expect that the proposed hybrid model is very useful and efficient for fast responsiveness to support Context-aware Services for large scale users in mobile networks.
Common Variant rs9939609 in Gene FTO Confers Risk to Polycystic Ovary Syndrome

Background Fat mass and obesity-associated gene (FTO) has been associated with obesity, especially the common variant rs9939609. Polycystic ovary syndrome (PCOS) is a complex endocrine-metabolic disorder and over 50% of patients are overweight/obese. Thus FTO is a potential candidate gene for PCOS but their relationship is confusing and remains to be clarified in different population with a large sample size. Method This study was performed adopting a two-stage design by genotyping SNP rs9939609. The first set comprise of 741 PCOS and 704 control subjects, with data from our previous GWAS. The second phase of replication study was performed among another independent group of 2858 PCOS and 2358 control subjects using TaqMan-MGB probe assay. All subjects are from Han Chinese. Results The less meaningful association of FTO rs9939609 and PCOS discovered in GWAS (P = 2.47E-03), was further confirmed in the replication study (P = 1.86E-09). Using meta-analysis, the P-meta value has reached 6.89E-12, over-exceeding the genome-wide association level of 5.00E-8. By combination, the P value was 1.26E-11 and after BMI adjustment it remained significant(P = 1.82E-06). To further elucidate whether this association is resulted from obesity or PCOS per se, the samples were divided into two groups–obese and non-obese PCOS, and the results were still positive in obese group (P obese = 5.81E-05, OR = 1.55), as well as in non-obese PCOS group (P non-obese = 7.06E-04, OR = 1.28). Conclusion Variant rs9939609 in FTO is associated with PCOS in Chinese women, not only in obese PCOS subjects, but also in non-obese cases.
Increased S-Nitrosylation and Proteasomal Degradation of Caspase-3 during Infection Contribute to the Persistence of Adherent Invasive Escherichia coli (AIEC) in Immune Cells

Adherent invasive Escherichia coli (AIEC) have been implicated as a causative agent of Crohn’s disease (CD) due to their isolation from the intestines of CD sufferers and their ability to persist in macrophages inducing granulomas. The rapid intracellular multiplication of AIEC sets it apart from other enteric pathogens such as Salmonella Typhimurium which after limited replication induce programmed cell death (PCD). Understanding the response of infected cells to the increased AIEC bacterial load and associated metabolic stress may offer insights into AIEC pathogenesis and its association with CD. Here we show that AIEC persistence within macrophages and dendritic cells is facilitated by increased proteasomal degradation of caspase-3. In addition S-nitrosylation of pro- and active forms of caspase-3, which can inhibit the enzymes activity, is increased in AIEC infected macrophages. This S-nitrosylated caspase-3 was seen to accumulate upon inhibition of the proteasome indicating an additional role for S-nitrosylation in inducing caspase-3 degradation in a manner independent of ubiquitination. In addition to the autophagic genetic defects that are linked to CD, this delay in apoptosis mediated in AIEC infected cells through increased degradation of caspase-3, may be an essential factor in its prolonged persistence in CD patients.
Learning the Caesar and Vigenere Cipher by hierarchical evolutionary re-combination

We describe a new programming language called HERCL, designed for evolutionary computation with the specific aim of allowing new programs to be created by combining patches of code from different parts of other programs, at multiple scales. Large-scale patches are followed up by smaller-scale patches or mutations, recursively, to produce a global random search strategy known as hierarchical evolutionary re-combination. We demonstrate the proposed system on the task of learning to encode with the Caesar or Vigenere Cipher, and show how the evolution of one task may fruitfully be cross-pollinated with evolved solutions from other related tasks.
MCMC Methods for Drawing Random Samples From the Discrete-Grains Model of a Magnetic Medium

The discrete-grains model is a simple model for the distribution of “grains” on a magnetic medium. In this model, grains on the medium are taken to be one of four basic rectangular shapes (“tiles”)-1 × 1, 1 × 2, 2 × 1, and 2 × 2. The magnetic medium is then modeled as an N × N square tiled by these four basic tiles. In this paper, we present Markov chain Monte Carlo (MCMC) methods for generating random tilings of an N × N square consisting only of the four basic tiles. To be precise, given a target probability distribution, e.g., the uniform distribution, on the space, SN, of all possible such tilings, we make use of the Metropolis-Hastings algorithm to design an MCMC method for sampling from a probability distribution arbitrarily close, in total variation distance, to the target distribution. We further extend this approach to enable sampling from probability distributions on certain subsets of SN, namely, those consisting of tilings, in which each kind of tile occurs a fixed number of times. We finally present some bounds and conjectures on the mixing times of the underlying Markov chains, which provide estimates of the amount of time taken by the MCMC methods to generate a random tiling sampled from the target probability distribution.
Double Threshold Energy Detection of Cooperative Spectrum Sensing in Cognitive Radio

Cognitive radio has become an effective theory to solve the inefficiency of the spectrum usage, and cooperative spectrum sensing among the secondary users to detect the primary user accurately is broadly studied before. In this paper, we employ a double threshold method in energy detector to perform spectrum sensing, while a fusion center in the cognitive radio network collects the local decisions and observational values of the secondary users, and then makes the final decision to determine whether the primary user is absence or not. Simulation results will show that the spectrum sensing performance in AWGN channels is improved significantly under the proposed scheme as opposed to the conventional method.
Efficient Algorithm for Distance Metric Learning

Distance metric learning provides an approach to transfer knowledge from sparse labeled data to unlabeled data. The learned metric is more proper to measure the similarity of semantics among instances. The main idea of the algorithm is to create an objective function using the equivalence constraints and in-equivalence constraints and pose the problem as an optimization problem. In this paper, we proposed to unify different metric learning algorithms into semidefinite programming (SDP) framework. The classical semidefinite programming algorithms are extremely expensive on larger problem. So we discuss efficient algorithms for large-scale metric learning. We investigated a recent proposed algorithm arise from Frank-Wolfe algorithm and proposed novel strategies for acceleration based on the special structure of the problem. We compared different algorithms on 3 UCI dataset in clustering problem.
Hepatocyte transplantation in bile salt export pump-deficient mice: selective growth advantage of donor hepatocytes under bile acid stress

The bile salt export pump (Bsep) mediates the hepatic excretion of bile acids, and its deficiency causes progressive familial intrahepatic cholestasis. The current study aimed to induce bile acid stress in Bsep−/− mice and to test the efficacy of hepatocyte transplantation in this disease model. We fed Bsep−/− and wild‐type mice cholic acid (CA) or ursodeoxycholic acid (UDCA). Both CA and UDCA caused cholestasis and apoptosis in the Bsep−/− mouse liver. Wild‐type mice had minimal liver injury and apoptosis when fed CA or UDCA, yet had increased proliferative activity. On the basis of the differential cytotoxicity of bile acids on the livers of wild‐type and Bsep−/− mice, we transplanted wild‐type hepatocytes into the liver of Bsep−/− mice fed CA or CA + UDCA. After 1–6 weeks, the donor cell repopulation and canalicular Bsep distribution were documented. An improved repopulation efficiency in the CA + UDCA‐supplemented group was found at 2 weeks (4.76 ± 5.93% vs. 1.32 ± 1.48%, P = 0.0026) and at 4–6 weeks (12.09 ± 14.67% vs. 1.55 ± 1.28%, P < 0.001) compared with the CA‐supplemented group. Normal‐appearing hepatocytes with prominent nuclear staining for FXR were noted in the repopulated donor nodules. After hepatocyte transplantation, biliary total bile acids increased from 24% to 82% of the wild‐type levels, among which trihydroxylated bile acids increased from 41% to 79% in the Bsep−/− mice. We conclude that bile acid stress triggers differential injury responses in the Bsep−/− and wild‐type hepatocytes. This strategy changed the balance of the donor–recipient growth capacities and was critical for successful donor repopulation.
Rule-based User Characteristics Acquisition from Logs with Semantics for Personalized Web-Based Systems

Personalization of web-based information systems based on specialized user models has become more important in order to preserve the effectiveness of their use as the amount of available content increases. We describe a user mo- deling approach based on automated acquisition of user behaviour and its suc- cessive rule-based evaluation and transformation into an ontological user model. We stress reusability and flexibility by introducing a novel approach to logging, which preserves the semantics of logged events. The successive analysis is driven by specialized rules, which map usage patterns to knowledge about users, stored in an ontology-based user model. We evaluate our approach via a case study using an enhanced faceted browser, which provides personalized navigation support and recommendation.