Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of interatomic contact areas to quantify discrepancies between RNA 3D models and reference structures

Growing interest in computational prediction of ribonucleic acid (RNA) three-dimensional structure has highlighted the need for reliable and meaningful methods to compare models and experimental structures. We present a structure superposition-free method to quantify both the local and global accuracy of RNA structural models with respect to the reference structure. The method, initially developed for proteins and here extended to RNA, closely reflects physical interactions, has a simple definition, a fixed range of values and no arbitrary parameters. It is based on the correspondence of respective contact areas between nucleotides or their components (base or backbone). The better is the agreement between respective contact areas in a model and the reference structure, the more accurate the model is considered to be. Since RNA bases account for the largest contact areas, we further distinguish stacking and non-stacking contacts. We have extensively tested the contact area-based evaluation method and found it effective in both revealing local discrepancies and ranking models by their overall quality. Compared to other reference-based RNA model evaluation methods, the new method shows a stronger emphasis on stereochemical quality of models. In addition, it takes into account model completeness, enabling a meaningful evaluation of full models and those missing some residues.
Finding optimum abstractions in parametric dataflow analysis

We propose a technique to efficiently search a large family of abstractions in order to prove a query using a parametric dataflow analysis. Our technique either finds the cheapest such abstraction or shows that none exists. It is based on counterexample-guided abstraction refinement but applies a novel meta-analysis on abstract counterexample traces to efficiently find abstractions that are incapable of proving the query. We formalize the technique in a generic framework and apply it to two analyses: a type-state analysis and a thread-escape analysis. We demonstrate the effectiveness of the technique on a suite of Java benchmark programs.
A construct of the instrument for measuring junior high school mathematics teacher's self-efficacy

The aim of this study was to develop a construct of the instrument for junior high school mathematics teacher self-efficacy and its mapping in the Special Region of Yogyakarta. The population was 816 junior high mathematics teacher and a sample of 274 teachers was selected through proportionate random sampling technique. The data were analyzed using Confirmatory Factor Analysis (CFA), using software Lisrel 8.80 through the first order and the second order stages. The result of data analysis toward four dimensions obtained: 11 fit items for dimension of personal efficacy (PE), 12 fit items for dimension of general teaching efficacy (GTE), 13 fit items for dimension of subject matter teaching efficacy (STE), and 8 fit items for dimension of outcome efficacy (OE). Afterward, the result of selecting 54 items in the first order stage was examined for the second order CFA which obtained Chi-Square = 297.58; df = 271; p-value = 0.12824; RMSEA = 0.019, which showed the model was fit to the data and obtained 25 fit items. The loading factors for each dimension PE, GTE, STE, and OE consecutively were: 0.46; 0.84; 0.89, and 0.92, and mapping of mathematics teacher self-efficacy level, which showed: 43.07% in low category, 55.47% in medium category (55.47%) and 1.46% in the high category.
Context Aware Handover Algorithms for Mobile Positioning Systems

Abstract: This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution which consists of the algorithms is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95% of cases studied while two other applications performed correct handover in only 50% of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters.
What Moves the Bond Market?

In an examination of the U.S. Treasury securities market, the authors attempt to explain the sharpest price changes and most active trading episodes. They find that each of the twenty-five largest price shocks and twenty-five greatest trading surges can be attributed to just-released macroeconomic announcements. They also measure the market's average reactions to theses announcements and analyze the extent to which the reactions depend on the degree of announcement surprise and on prevailing market conditions. The market's price and trading reactions are found to reflect differences of informational content in and among the varying announcements under changing market conditions.
Comparison between Chinese Herbal Medicines and Conventional Therapy in the Treatment of Severe Hand, Foot, and Mouth Disease: A Randomized Controlled Trial

Background. This study was made to evaluate the efficacy of Chinese herbal medicines, Reduning injection, and a traditional Chinese medicine (TCM) granule, in patients with severe hand, foot, and mouth disease (HFMD) by conducting a prospective, controlled, and randomized trial. Methods. 355 severe HFMD patients were randomly assigned to receive conventional therapy alone, Reduning injection plus conventional therapy, or TCM enema plus conventional therapy for 7–10 days. Results. There was no significant difference in the incidence of major complications between the groups. Median time to fever clearance was 20 hours (95% CI: 6.0–25.0) for conventional therapy recipients, 18 hours (95% CI: 4.0–24.0) for Reduning combination-treated patients, and 6 hours (95% CI: 4.0–16.0) for TCM combination-treated patients. Only the difference in time to fever clearance between TCM combination group and conventional group reached statistical significance (P = 0.048). Reduning combination group showed a significant reduction in sedative administration compared with conventional therapy group (P = 0.008). No HFMD-related death and no important adverse events were observed. Conclusions. Reduning injection plus conventional therapy significantly reduced the concomitant use of sedatives, which may help decrease HFMD-related neurologic complications in children. TCM effectively reduced time to fever clearance and may become a complementary therapy for relieving the symptoms of severe HFMD.
ESTIMATION OF THE MAGNITUDE OF VICTORY IN ONE-DAY CRICKET

The Duckworth–Lewis method is steadily becoming the standard approach for resetting targets in interrupted one-day cricket matches. In this paper we show that a modiﬁcation of the Duckworth–Lewis resource table can be used to quantify the magnitude of a victory in one-day matches. This simple and direct application is particularly useful in breaking ties in tournament standings and in quantifying team strength.
Extensive Sex-Specific Nonadditivity of Gene Expression in Drosophila melanogaster

Assessment of the degree to which gene expression is additive and heritable has important implications for understanding the maintenance of variation, adaptation, phenotypic divergence, and the mapping of genotype onto phenotype. We used whole-genome transcript profiling using Agilent long-oligonucleotide microarrays representing 12,017 genes to demonstrate that gene transcription is pervasively nonadditive in Drosophila melanogaster. Comparison of adults of two isogenic lines and their reciprocal F1 hybrids revealed 5820 genes as significantly different between at least two of the four genotypes in either males or females or across both sexes. Strikingly, while 25% of all genes differ between the two parents, 33% differ between both F1's and the parents, averaged across sexes. However, only 5% of genes show overdominance, suggesting that heterosis for expression is rare.
Extracellular proteases are key mediators of Staphylococcus aureus virulence via the global modulation of virulence-determinant stability

Staphylococcus aureus is a highly virulent and successful pathogen that causes a diverse array of diseases. Recently, an increase of severe infections in healthy subjects has been observed, caused by community‐associated methicillin‐resistant S. aureus (CA‐MRSA). The reason for enhanced CA‐MRSA virulence is unclear; however, work suggests that it results from hypersecretion of agr‐regulated toxins, including secreted proteases. In this study, we explore the contribution of exo‐proteases to CA‐MRSA pathogenesis using a mutant lacking all 10 enzymes. We show that they are required for growth in peptide‐rich environments, serum, in the presence of antimicrobial peptides (AMPs), and in human blood. We also reveal that extracellular proteases are important for resisting phagocytosis by human leukocytes. Using murine infection models, we reveal contrasting roles for the proteases in morbidity and mortality. Upon exo‐protease deletion, we observed decreases in abscess formation, and impairment during organ invasion. In contrast, we observed hypervirulence of the protease‐null strain in the context of mortality. This dichotomy is explained by proteomic analyses, which demonstrates exo‐proteases to be key mediators of virulence‐determinant stability. Specifically, increased abundance of both secreted (e.g. α‐toxin, Psms, LukAB, LukE, PVL, Sbi, γ‐hemolysin) and surface‐associated (e.g. ClfA+B, FnbA+B, IsdA, Spa) proteins was observed upon protease deletion. Collectively, our findings provide a unique insight into the progression of CA‐MRSA infections, and the role of secreted proteolytic enzymes.
A Comparison Between Volumetric Data Generated by Voxel-Based Morphometry and Manual Parcellation of Multimodal Regions of the Frontal Lobe

BACKGROUND AND PURPOSE: Volumetric measurements on structural MR images are an established method to investigate pathology-related volume changes in cortex. Manual volumetric methods have sometimes been referred to as the reference standard for quality control of automatic volumetric methods. While some automatic methods, like VBM, may rely on a template, manual methods use sulci as indirect landmarks for the subdivision of cortex. The purpose of this study was to compare volumetric data generated by MM and VBM on 4 multimodal regions in the frontal lobe. MATERIALS AND METHODS: We investigated 4 multimodal frontocortical regions by MM and VBM in patients with frontotemporal lobar degeneration and Alzheimer disease and controls. RESULTS: MM and VBM results were highly correlated for dorsolateral prefrontal cortex, orbitofrontal cortex, and hippocampus, but not for the dorsal and rostral anterior cingulate. VBM results were more consistent with results from previous studies on cingulate in frontotemporal lobar degeneration. Our results may potentially be explained by 2 factors. First, the volume of small cortical regions may be more affected by anatomic variability than large regions in the MM. Second, it has been shown that the location of multimodal cytoarchitectonic areas, such as the cingulate cortex, may be difficult to predict by the appearance of sulci and gyri. CONCLUSIONS: While both VBM and the MM may do equally poorly in predicting cytoarchitecture, the MM may add additional unrelated variance caused by anatomic variability. Thus, paradoxically, the higher anatomic precision of the MM may potentially cause a weaker relation to cytoarchitecture.
Rethinking class analysis: some reflections on current issues and possible new forms of empirical research

This paper examines the current state of social stratification research. Its focus is mainly upon the British tradition of research, but its reflections also apply more broadly to wider European and North American literature. The paper explores the classical tradition of class analysis in Britain and probes how this became superseded by newer forms of sociological analysis which are rooted primarily in occupational differences. The paper argues that there is a need for a double shift in approach. This would involve a renewal of interest in class-based relations of structured inequality and also a shift of focus away from highly quantitative approaches in favour of different styles of empirical research.
Modeling of cache access behavior based on Zipf's law

Recently, chip multiprocessors (CMPs) that can simultaneously execute multiple workloads using multiple cores have become a key to achieve high-performance processing. To improve CMP performance, various shared resource management mechanisms have been proposed. In particular, cache partitioning is significantly effective to avoid resource conflicts at a shared cache memory. As most cache partitioning methods need to predict the changes in cache access characteristics of each workload when the cache partition moves, it is important for cache partitioning to establish an accurate prediction model. In this paper, we first analyze the cache access locality of various applications using stack distance profiling. We figure out that stack distance distributions incline to obey socalled Zipf's law. To achieve effective cache partitioning, then, we propose a model based on Zipf's law that predicts the changes in the stack distance distributions. Using the model, we also show the validity of a measure, which has been proposed in our previous work to quantify how much a workload demands the cache capacity.
Evidence-Driven Image Interpretation by Combining Implicit and Explicit Knowledge in a Bayesian Network

Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, in contrast to human perception that makes extensive use of logic-based rules, these models fail to benefit from knowledge that is explicitly provided. In this paper, we propose a framework that can perform knowledge-assisted analysis of visual content. We use ontologies to model the domain knowledge and a set of conditional probabilities to model the application context. Then, a Bayesian network is used for integrating statistical and explicit knowledge and performing hypothesis testing using evidence-driven probabilistic inference. In addition, we propose the use of a focus-of-attention (FoA) mechanism that is based on the mutual information between concepts. This mechanism selects the most prominent hypotheses to be verified/tested by the BN, hence removing the need to exhaustively test all possible combinations of the hypotheses set. We experimentally evaluate our framework using content from three domains and for the following three tasks: 1) image categorization; 2) localized region labeling; and 3) weak annotation of video shot keyframes. The results obtained demonstrate the improvement in performance compared to a set of baseline concept classifiers that are not aware of any context or domain knowledge. Finally, we also demonstrate the ability of the proposed FoA mechanism to significantly reduce the computational cost of visual inference while obtaining results comparable to the exhaustive case.
Sarcasm Detection in Chinese Using a Crowdsourced Corpus

Based on the assumption that comment with positive sentimental polarity to a negative issue has high probability to be a sarcasm, we propose a simple yet efficient method to collect sarcastic textual data by crowdsourcing with social media and merging game with a purpose approach. Taking advantage of Facebook's reaction button, posts triggering strong negative emotion are collected. Next, by using PTT's search engine, we successfully connect PTT's comments to the collected posts in Facebook and build the sarcasm corpus. Based on the corpus data, the performance comparison of sarcasm detection between SVM with naïve features and Convolutional Neural Network models is conducted. An impressive accuracy rate and great potentials of the corpus are demonstrated.
OFDM-based Medium Access with Rate Constraints

— For wireless local area networks based on OFDM we study the sum rate of time and frequency division multiple access (TDMA, OFDMA) as well as superimposed transmission (SUP). In the analysis trafﬁc-imposed constraints on the maximum exploitable user rates apply. For a large number of subcarriers the effective sum rate is low with TDMA but much higher with OFDMA and SUP. The spatial properties of the channel determine which one of OFDMA and SUP performs best.
The effects of education and income on consumers' motivation to read online hotel reviews

An increasing number of consumers are using the Internet for online advice and recommendations. Consumers read these electronic word-of-mouth (eWOM) comments for various reasons. The current study is an extension to that of Kim, Mattila, and Baloglu (2011)1, with the aim of measuring the effects of income and education on eight motives for reading online reviews as described by Hennig-Thurau and Walsh (2003)2. A 20-item survey was administered to a sample of 56 Dutch respondents. The results indicate that income and education do have an effect on some of the motives. It has been found that more highly educated individuals tend to be less influenced by eWOM, and people with a lower income tend to use eWOM particularly for dissonance reduction. However, results should be interpreted with caution as not all educational levels and income categories were equally represented. 1 Kim EEK, Mattila AS, Baloglu, S. 2011. Effects of gender and expertise on consumers' motivation to read online hotel reviews. Cornell Hospitality Quarterly 52: 399–406.2 Hennig-Thurau T, Walsh G. 2003. Electronic word-of-mouth: motives for and consequences of reading customer articulations on the Internet. International Journal of Electronic Commerce 8: 51–74.
Analysis of DCO-OFDM and flip-OFDM for IM/DD optical-wireless system

The paper analyzes the performance of two forms of orthogonal frequency division multiplexing (OFDM) techniques called as DC biased Optical OFDM (DCO-OFDM) and Flip- OFDM for intensity modulated direct detection (IM/DD) system. The aforementioned OFDM schemes are compared in terms of peak-to-average power ratio (PAPR) and bit error rate (BER) as a function of electrical energy per bit to noise power ratio (Eb(elec)/N0) when the signal is affected by additive white Gaussian noise (AWGN). In particular, 7-dB and 13-dB DC bias are used for DCO-OFDM in BER analysis and finally compared with flip-OFDM for the different data formats such as 4-,16-,64-,256- QAMs. The results show that flip-OFDM performs better in BER analysis with reduced value of (Eb(elec)/N0) and thus, it can be considered as a power efficient unipolar modulation format. However, Flip-OFDM signal provides a high PAPR value that can deteriorate the overall system performance. Finally, OFDM signal performance tradeoff is measured as a function of transmitted optical power, QAM size, and spectral efficiency.
Relation Prediction in Multi-Relational Domains using Matrix Factorization

The paper is concerned with relation prediction in multi-relational domains using matrix factorization. While most past predictive models focussed on one single relation type between two entity types, in the paper a generalized model is presented that is able to deal with an arbitrary number of relation types and entity types in a domain of interest. The novel multi-relational matrix factorization is domain independent and highly scalable. We validate the performance of our approach using two real-world data sets, i.e. user-movie recommendations and gene function prediction.
A Functional Genomics Approach to Tanshinone Biosynthesis Provides Stereochemical Insights

Tanshinones are abietane-type norditerpenoid quinone natural products that are the bioactive components of the Chinese medicinal herb Salvia miltiorrhiza Bunge. The initial results from a functional genomics-based investigation of tanshinone biosynthesis, specifically the functional identification of the relevant diterpene synthases from S. miltiorrhiza, are reported. The cyclohexa-1,4-diene arrangement of the distal ring poises the resulting miltiradiene for the ensuing aromatization and hydroxylation to ferruginol suggested for tanshinone biosynthesis.
Doubts, Asymmetries, and Insurance

This paper studies an insurance problem where agents doubt their forecasting models. Using Hansen-Sargent multiplier preferences, these doubts are represented as sets of probability distributions that are statistically hard to discriminate. Agents differ endogenously in two aspects their forecasting models and the extent of doubts surrounding these models. This heterogeneity gives rise to a new insurance channel that depends on sensitivity of marginal utilities to changes in consumption. We show how introducing a small amount of doubts can keep long run inequality bounded in settings when agents have exogenous heterogeneous beliefs or heterogeneous information about private insurance needs. The transient wealth dynamics delivers countercyclical prices of risk and generates motives for trading on news shocks. ∗I am especially indebted to Thomas Sargent, Jaroslav Borovicka and Mikhail Golosov for their advice on this project. I also thank Tim Cogley, Lars Peter Hansen, Andrew Atkeson, Pierre Olivier Weill, Christopher Phelan, V.V. Chari, Larry Jones, Aleh Tsyvinski, Victor Tsyrennikov, Max Croce, David Evans, Jesse Perla, Chris Tonneti, Gaston Navarro, Isaac Baley, Andres Blanco, Anusha Nath and the participants at the Becker Friedman Institute, SED (Seoul) for helpful comments and suggestions. †Economics Department; University of Minnesota bhandari@umn.edu
Single Exponential FPT Algorithm for Interval Vertex Deletion and Interval Completion Problem

Let G be an input graph with n vertices and m edges and let k be a fixed parameter. We provide a single exponential FPT algorithm with running time O(c^kn(n+m)), c= min {18,k} that turns graph G into an interval graph by deleting at most k vertices from G. This solves an open problem posed by D.Marx [19]. We also provide a single exponential FPT algorithm with running time O(c^kn(n+m)), c= min {17,k} that turns G into an interval graph by adding at most$k edges. The first FPT algorithm with run time O(k^{2k}n^3m) appeared in STOC 2007 [24]. Our algorithm is the the first single exponential FPT algorithm that improves the running time of the previous algorithm. The algorithms are based on a structural decomposition of G into smaller subgraphs when G is free from small interval graph obstructions. The decomposition allows us to manage the search tree more efficiently.
Towards a text analysis system for political debates

Social scientists and journalists nowadays have to deal with an increasingly large amount of data. It usually requires expensive searching and annotation effort to ﬁnd insight in a sea of information. Our goal is to build a discourse analysis sys-tem which can be applied to large text collections. This system can help social scientists and journalists to analyze data and validate their research theories by providing them with tailored machine learning methods to alleviate the annotation effort and exploratory facilities and visualization tools. We report initial experimental re-sults in a case study related to discourse analysis in political debates.