nils-herrmann/clean_scirepeval huggingface dataset:
Attributed Graph Grammar for floor plan analysis

In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations.
Visual Thinking Design Patterns

In visual analytics, interactive data visualizations provide a bridge between analytic computations, often involving “big data”, and computations in the brain of the user. Visualization provides a high bandwidth channel from the computer to the user by means of the visual display, with interactions including brushing, dynamic queries, and generalized fisheye views designed to select and control what is shown. In this paper we introduce Visual Thinking Design Patterns (VTDPs) as part of a methodology for producing cognitively efficient designs. We describe their main components, including epistemic actions (actions to seek knowledge) and visual queries (pattern searches that provide a whole or partial solution to a problem). We summarize the set of 20 VTDPs we have identified so far and show how they can be used in a design methodology. design patterns, visual thinking, data visualization, visual analytics.
Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland. ∗Corresponding author Email addresses: juho.kokkala@aalto.fi (Juho Kokkala), simo.sarkka@aalto.fi (Simo Särkkä) URL: http://becs.aalto.fi/~jkokkala (Juho Kokkala), http://becs.aalto.fi/~ssarkka (Simo Särkkä) Preprint submitted to Digital Signal Processing February 20, 2015
Genetic Algorithm Optimization of a High-Directivity Microstrip Patch Antenna Having a Rectangular Profile

A single high-directivity microstrip patch antenna (MPA) having a rectangular profile, which can substitute a linear array is proposed. It is designed by using genetic algorithms with the advantage of not requiring a feeding network. The patch fits inside an area of 2.54 x 0.25, resulting in a broadside pattern with a directivity of 12 dBi and a fractional impedance bandwidth of 4 %. The antenna is fabricated and the measurements are in good agreement with the simulated results. The genetic MPA provides a similar directivity as linear arrays using a corporate or series feeding, with the advantage that the genetic MPA results in more bandwidth.
Event-Triggered Fault Detection of Nonlinear Networked Systems

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.
Hierarchical LSTMs with Adaptive Attention for Visual Captioning

Recent progress has been made in using attention based encoder-decoder framework for image and video captioning. Most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of visual captioning. Furthermore, the hierarchy of LSTMs enables more complex representation of visual data, capturing information at different scales. Considering these issues, we propose a hierarchical LSTM with adaptive attention  approach for image and video captioning. Specifically, the proposed framework utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the caption generation. We design the  model as a general framework, and we firstly instantiate it for the task of video captioning. Then, we further instantiate our hLSTMarefine it and apply it to the imioning task. To demonstrate the effectiveness of our proposed framework, we test our method on both video and image captioning tasks. Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks. The effect of important components is also well exploited in the ablation study.
Controlling Target Features in Neural Machine Translation via Prefix Constraints

We propose prefix constraints, a novel method to enforce constraints on target sentences in neural machine translation. It places a sequence of special tokens at the beginning of target sentence (target prefix), while side constraints (Sennrich et al., 2016) places a special token at the end of source sentence (source suffix). Prefix constraints can be predicted from source sentence jointly with target sentence, while side constraints must be provided by the user or predicted by some other methods. In both methods, special tokens are designed to encode arbitrary features on target-side or metatextual information. We show that prefix constraints are more flexible than side constraints and can be used to control the behavior of neural machine translation, in terms of output length, bidirectional decoding, domain adaptation, and unaligned target word generation.
A proximity language model for information retrieval

The proximity of query terms in a document is a very important information to enable ranking models go beyond the "bag of word" assumption in information retrieval. This paper studies the integration of term proximity information into the unigram language modeling. A new proximity language model (PLM) is proposed which views query terms' proximity centrality as the Dirichlet hyper-parameter that weights the parameters of the unigram document language model. Several forms of proximity measure are developed to be used in PLM which could compute a query term's proximate centrality in a specific document. In experiments, the proximity language model is compared with the basic language model and previous works that combine the proximity information with language model using linear score combination. The experiment results show that the proposed model performs better in both top precision and average precision.
When and how to update systematic reviews.

Systematic reviews are most helpful if they are up-to-date. We did a systematic review of strategies and methods describing when and how to update systematic reviews.



To identify, describe and assess strategies and methods addressing: 1) when to update systematic reviews and 2) how to update systematic reviews.


SEARCH STRATEGY
We searched MEDLINE (1966 to December 2005), PsycINFO, the Cochrane Methodology Register (Issue 1, 2006), and hand searched the 2005 Cochrane Colloquium proceedings.



We included methodology reports, updated systematic reviews, commentaries, editorials, or other short reports describing the development, use, or comparison of strategies and methods for determining the need for updating or updating systematic reviews in healthcare.



We abstracted information from each included report using a 15-item questionnaire. The strategies and methods for updating systematic reviews were assessed and compared descriptively with respect to their usefulness, comprehensiveness, advantages, and disadvantages.



Four updating strategies, one technique, and two statistical methods were identified. Three strategies addressed steps for updating and one strategy presented a model for assessing the need to update. One technique discussed the use of the "entry date" field in bibliographic searching. Statistical methods were cumulative meta-analysis and predicting when meta-analyses are outdated.



Little research has been conducted on when and how to update systematic reviews and the feasibility and efficiency of the identified approaches is uncertain. These shortcomings should be addressed in future research.
Bounds on Multiprocessing Timing Anomalies

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A Randomized Controlled Trail Comparing the Efficacy of 0.5% Centbucridine to 2% Lignocaine as Local Anesthetics in Dental Extractions

The development of local anesthesia in dentistry has marked the beginning of a new era in terms of pain control. Lignocaine is the most commonly used local anesthetic (LA) agent even though it has a vasodilative effect and needs to be combined with adrenaline. Centbucridine is a non-ester, non amide group LA and has not been comprehensively studied in the dental setting and the objective was to compare it to Lignocaine. This was a randomized study comparing the onset time, duration, depth and cardiovascular parameters between Centbucridine (0.5%) and Lignocaine (2%). The study was conducted in the dental outpatient department at the Government Dental College in India on patients attending for the extraction of lower molars. A total of 198 patients were included and there were no significant differences between the LAs except those who received Centbucridine reported a significantly longer duration of anesthesia compared to those who received Lignocaine. None of the patients reported any side effects. Centbucridine was well tolerated and its substantial duration of anesthesia could be attributed to its chemical compound. Centbucridine can be used for dental procedures and can confidently be used in patients who cannot tolerate Lignocaine or where adrenaline is contraindicated.
Conducting Realistic Experiments in Software Engineering

An important goal of most empirical software engineering research is the transfer of research results to industrial applications. Two important obstacles for this transfer are the lack of control of variables of case studies, i.e., the lack of explanatory power, and the lack of realism of controlled experiments. While it may be difficult to increase the explanatory power of case studies, there is a large potential for increasing the realism of controlled software engineering experiments. To convince industry about the validity and applicability of the experimental results, the tasks, subjects and the environments of the experiments should be as realistic as practically possible. Such experiments are, however, more expensive than experiments involving students, small tasks and pen-andpaper environments. Consequently, a change towards more realistic experiments requires a change in the amount of resources spent on software engineering
Predictive risk stratification model: a progressive cluster-randomised trial in chronic conditions management (PRISMATIC) research protocol

An ageing population increases demand on health and social care. New approaches are needed to shift care from hospital to community and general practice. A predictive risk stratification tool (Prism) has been developed for general practice that estimates risk of an emergency hospital admission in the following year. We present a protocol for the evaluation of Prism.



We will undertake a mixed methods progressive cluster-randomised trial. Practices begin as controls, delivering usual care without Prism. Practices will receive Prism and training randomly, and thereafter be able to use Prism with clinical and technical support. We will compare costs, processes of care, satisfaction and patient outcomes at baseline, 6 and 18 months, using routine data and postal questionnaires. We will assess technical performance by comparing predicted against actual emergency admissions. Focus groups and interviews will be undertaken to understand how Prism is perceived and adopted by practitioners and policy makers. We will model data using generalised linear models and survival analysis techniques to determine whether any differences exist between intervention and control groups. We will take account of covariates and explanatory factors. In the economic evaluation we will carry out a cost-effectiveness analysis to examine incremental cost per emergency admission to hospital avoided and will examine costs versus changes in primary and secondary outcomes in a cost-consequence analysis. We will also examine changes in quality of life of patients across the risk spectrum. We will record and transcribe focus groups and interviews and analyse them thematically. We have received full ethical and R and D approvals for the study and Information Governance Review Panel (IGRP) permission for the use of routine data. We will comply with the CONSORT guidelines and will disseminate the findings at national and international conferences and in peer-reviewed journals.



The proposed study will provide information on costs and effects of Prism; how it is used in practice, barriers and facilitators to its implementation; and its perceived value in supporting the management of patients with and at risk of developing chronic conditions.
Serotonin transporter binding after recovery from eating disorders

Several lines of evidence suggest that altered serotonin (5-HT) function persists after recovery from anorexia nervosa (AN) and bulimia nervosa (BN). We compared 11 subjects who recovered (>1 year normal weight, regular menstrual cycles, no bingeing or purging) from restricting-type AN (REC RAN), 7 who recovered from bulimia-type AN (REC BAN), 9 who recovered from BN (REC BN), and 10 healthy control women (CW). Positron emission tomography (PET) imaging with [11C]McN5652 was used to assess the 5-HT transporter (5-HTT). For [11C]McN5652, distribution volume (DV) values were determined using a two-compartment, three-parameter tracer kinetic model, and specific binding was assessed using the binding potential (BP, BP = DVregion of interest/DVcerebellum − 1). After correction for multiple comparisons, the four groups showed significant (p < 0.05) differences for [11C]McN5652 BP values for the dorsal raphe and antero-ventral striatum (AVS). Post-hoc analysis revealed that REC RAN had significantly increased [11C]McN5652 BP compared to REC BAN in these regions. Divergent 5-HTT activity in subtypes of eating disorder subjects may provide important insights as to why these groups have differences in affective regulation and impulse control.
Point set silhouettes via local reconstruction

We present an algorithm to compute the silhouette set of a point cloud. Previous methods extract point set silhouettes by thresholding point normals, which can lead to simultaneous overand under-detection of silhouettes. We argue that additional information such as surface curvature is necessary to resolve these issues. To this end, we develop a local reconstruction scheme using Gabriel and intrinsic Delaunay criteria and define point set silhouettes based on the notion of a silhouette generating set. The mesh umbrellas, or local reconstructions of one-ring triangles surrounding each point sample, generated by our method enable accurate silhouette identification near sharp features and close-by surface sheets, and provide the information necessary to detect other characteristic curves such as creases and boundaries. We show that these curves collectively provide a sparse and intuitive visualization of point cloud data.
Lymphatic drainage of lung segments in the visceral pleura: a cadaveric study

Although peribronchial lymphatic drainage of the lung has been well characterized, lymphatic drainage in the visceral pleura is less well understood. The objective of the present study was to evaluate the lymphatic drainage of lung segments in the visceral pleura. Adult, European cadavers were examined. Cadavers with a history of pleural or pulmonary disease were excluded. The cadavers had been refrigerated but not embalmed. The lungs were surgically removed and re-warmed. Blue dye was injected into the subpleural area and into the first draining visceral pleural lymphatic vessel of each lung segment. Twenty-one cadavers (7 males and 14 females; mean age 80.9 years) were dissected an average of 9.8 day postmortem. A total of 380 dye injections (in 95 lobes) were performed. Lymphatic drainage of the visceral pleura followed a segmental pathway in 44.2% of the injections (n = 168) and an intersegmental pathway in 55.8% (n = 212). Drainage was found to be both intersegmental and interlobar in 2.6% of the injections (n = 10). Lymphatic drainage in the visceral pleura followed an intersegmental pathway in 22.8% (n = 13) of right upper lobe injections, 57.9% (n = 22) of right middle lobe injections, 83.3% (n = 75) of right lower lobe injections, 21% (n = 21) of left upper lobe injections, and 85.3% (n = 81) of left lower lobe injections. In the lung, lymphatic drainage in the visceral pleura appears to be more intersegmental than the peribronchial pathway is—especially in the lower lobes. The involvement of intersegmental lymphatic drainage in the visceral pleura should now be evaluated during pulmonary resections (and especially sub-lobar resections) for lung cancer.
The antecedents and consequences of racial/ethnic discrimination during adolescence: does the source of discrimination matter?

In the current study, we examined the precursors and consequences of discrimination for 876 Latino, African American, and Asian American adolescents (Mage = 16.9 years, SD = 0.43). The race/ethnic characteristics of schools and neighborhoods influenced adolescents' perceptions of the race/ethnic climates of these contexts. In turn, adolescents who viewed climates more negatively were more likely to perceive discriminatory treatment by school personnel, peers, and societal institutions. Discrimination from these 3 sources exerted differential influence on developmental outcomes: Greater discrimination from school personnel was associated with poorer academic performance, greater discrimination from peers was associated with more psychological maladjustment, and greater societal discrimination was associated with heightened racial awareness. Relations were consistent across race/ethnic groups and gender. Implications for intervening to reduce racial discrimination and other social stigmas are discussed.
A Fast Algorithm for Hierarchical Text Classification

Text classiication is becoming more important with the proliferation of the Internet and the huge amount of data it transfers. We present an eecient algorithm for text classiication using hierarchical classiiers based on a concept hierarchy. The simple TFIDF classiier is chosen to train sample data and to classify other new data. Despite its simplicity, results of experiments on Web pages and TV closed captions demonstrate high classiication accuracy. Application of feature subset selection techniques improves the performance. Our algorithm is compu-tationally eecient being bounded by O(n log n) for n samples.
Resting EEG Discrimination of Early Stage Alzheimer’s Disease from Normal Aging Using Inter-Channel Coherence Network Graphs

Amnestic mild cognitive impairment (MCI) is a degenerative neurological disorder at the early stage of Alzheimer’s disease (AD). This work is a pilot study aimed at developing a simple scalp-EEG-based method for screening and monitoring MCI and AD. Specifically, the use of graphical analysis of inter-channel coherence of resting EEG for the detection of MCI and AD at early stages is explored. Resting EEG records from 48 age-matched subjects (mean age 75.7 years)—15 normal controls (NC), 16 with early-stage MCI, and 17 with early-stage AD—are examined. Network graphs are constructed using pairwise inter-channel coherence measures for delta–theta, alpha, beta, and gamma band frequencies. Network features are computed and used in a support vector machine model to discriminate among the three groups. Leave-one-out cross-validation discrimination accuracies of 93.6% for MCI vs. NC (p < 0.0003), 93.8% for AD vs. NC (p < 0.0003), and 97.0% for MCI vs. AD (p < 0.0003) are achieved. These results suggest the potential for graphical analysis of resting EEG inter-channel coherence as an efficacious method for noninvasive screening for MCI and early AD.
Position estimation and tracking of an autonomous mobile sensor using received signal strength

In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion.