snu-aidas/HVMap-dataset huggingface dataset:
Clustering of contingency table and mixture model

Basing cluster analysis on mixture models has become a classical and powerful approach. It enables some classical criteria such as the well-known k-means criterion to be explained. To classify the rows or the columns of a contingency table, an adapted version of k-means known as Mndki2, which uses the chi-square distance, can be used. Unfortunately, this simple, effective method which can be used jointly with correspondence analysis based on the same representation of the data, cannot be associated with a mixture model in the same way as the classical k-means algorithm. In this paper we show that the Mndki2 algorithm can be viewed as an approximation of a classifying version of the EM algorithm for a mixture of multinomial distributions. A comparison of the algorithms belonging in this context are experimentally investigated using Monte Carlo simulations. 2006 Elsevier B.V. All rights reserved.
Multicriteria classification and sorting methods: A literature review

The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as real-world applications of MCDA classification/sorting techniques and their software implementations. 2002 Elsevier Science B.V. All rights reserved.
Modeling Driver Behavior in a Cognitive Architecture

OBJECTIVE
This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system.


BACKGROUND
Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior.


METHOD
An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment.


RESULTS
This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing.


CONCLUSION
The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks.


APPLICATION
The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.
A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average

Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
Group Buying on the Web: A Comparison of Price-Discovery Mechanisms

Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production. 1 Email: anandk@wharton.upenn.edu; Tel: (215) 898 1175. 2 Email: raviaron@wharton.upenn.edu; Tel:(215) 573 5677.
DESIGNING A LAYOUT USING THE MODIFIED TRIANGLE METHOD , AND GENETIC ALGORITHMS

This paper describes the use of genetic algorithms (GA) for solving the facility layout problem (FLP) within manufacturing systems’ design. The paper considers a specific heuristic layout planning method, known as the modified triangle method. This method seeks for an optimal layout solution based on the degrees of flows between workstations. The search for an optimal solution is extremely time-consuming and not suitable for larger systems; therefore we have developed a system based on evolutionary computation. Our paper presents a system, based on GA, and the results obtained regarding several numerical cases from the literature. We propose the usage of this system by presenting numerous advantages over other methods of solving FLP for problem representation and evolutionary computation for solution search. (Received in September 2012, accepted in June 2013. This paper was with the authors 2 months for 2 revisions.)
DRAFT: COMMENTS WELCOME BUT PLEASE DO NOT QUOTE

The Experimental Study of Adaptive User Interfaces Pat Langley (langley@isle.org) Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, CA 94306 Michael Fehling (fehling@odc.stanford.edu) Organizational Dynamics Center, EES/OR Department Stanford University, Stanford, CA 94305 Abstract In this paper we examine some issues that arise in the experimental evaluation of adaptive user interfaces, which are computational decision aids that use machine learning to improve their interaction with users. We begin by reviewing the notion of an adaptive interface and presenting examples for a number of application areas. After this, we discuss plausible dependent measures of their behavior, including solution speed, solution quality, user satisfaction, and predictive accuracy. Next we turn to independent variables that are likely to in uence these measures, including the number of user interactions and characteristics of the system, the user, and the task at hand. In closing, we comment on the role that experimentation plays in the larger scienti c process.
Convolutional auto-encoder for image denoising of ultra-low-dose CT

OBJECTIVES
The purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom.


MATERIALS AND METHODS
Standard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality.


RESULTS
For the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values < 0.05). On the other hand, the difference in the overall image quality between our proposed method and block-matching and 3D filtering was not statistically significant (p-value = 0.07272). The p-values obtained between our proposed method and large-scale nonlocal mean were all less than 0.05.


CONCLUSION
Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.
Emergence of Scaling in Random Networks

www.sciencemag.org (this information is current as of September 11, 2008 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/286/5439/509 version of this article at: including high-resolution figures, can be found in the online Updated information and services, found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/286/5439/509#related-content http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles , 3 of which can be accessed for free: cites 11 articles This article 2718 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles 95 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/physics Physics : subject collections This article appears in the following http://www.sciencemag.org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining
On Measures of Entropy and Information

2 Mutual information 3 Mutual information . . . . . . . . . . . . . . . . . 3 Multivariate mutual information . . . . . . . . . 4 Interaction information . . . . . . . . . . . . . . . 5 Conditional mutual information . . . . . . . . . 5 Binding information . . . . . . . . . . . . . . . . 6 Residual entropy . . . . . . . . . . . . . . . . . . 6 Total correlation . . . . . . . . . . . . . . . . . . . 6 Lautum information . . . . . . . . . . . . . . . . 6 Uncertainty coefficient . . . . . . . . . . . . . . . 7
Efficacy of psychotherapeutic interventions to promote forgiveness: a meta-analysis.

OBJECTIVE
This meta-analysis addressed the efficacy of psychotherapeutic interventions to help people forgive others and to examine moderators of treatment effects.


METHOD
Eligible studies reported quantitative data on forgiveness of a specific hurt following treatment by a professional with an intervention designed explicitly to promote forgiveness. Random effects meta-analyses were conducted using k = 53 posttreatment effect sizes (N = 2,323) and k = 41 follow-up effect sizes (N = 1,716) from a total of 54 published and unpublished research reports.


RESULTS
Participants receiving explicit forgiveness treatments reported significantly greater forgiveness than participants not receiving treatment (Δ+ = 0.56 [0.43, 0.68]) and participants, receiving alternative treatments (Δ+ = 0.45 [0.21, 0.69]). Also, forgiveness treatments resulted in greater changes in depression, anxiety, and hope than no-treatment conditions. Moderators of treatment efficacy included treatment dosage, offense severity, treatment model, and treatment modality. Multimoderator analyses indicated that treatment dosage (i.e., longer interventions) and modality (individual > group) uniquely predicted change in forgiveness compared with no-treatment controls. Compared with alternative treatment conditions, both modality (individual > group) and offense severity were marginally predictive (ps < .10) of treatment effects.


CONCLUSIONS
It appears that using theoretically grounded forgiveness interventions is a sound choice for helping clients to deal with past offenses and helping them achieve resolution in the form of forgiveness. Differences between treatment approaches disappeared when controlling for other significant moderators; the advantage for individual interventions was most clearly demonstrated for Enright-model interventions, as there have been no studies of individual interventions using the Worthington model.
Peginterferon alfa-2a plus ribavirin for chronic hepatitis C virus infection.

BACKGROUND
Treatment with peginterferon alfa-2a alone produces significantly higher sustained virologic responses than treatment with interferon alfa-2a alone in patients with chronic hepatitis C virus (HCV) infection. We compared the efficacy and safety of peginterferon alfa-2a plus ribavirin, interferon alfa-2b plus ribavirin, and peginterferon alfa-2a alone in the initial treatment of chronic hepatitis C.


METHODS
A total of 1121 patients were randomly assigned to treatment and received at least one dose of study medication, consisting of 180 microg of peginterferon alfa-2a once weekly plus daily ribavirin (1000 or 1200 mg, depending on body weight), weekly peginterferon alfa-2a plus daily placebo, or 3 million units of interferon alfa-2b thrice weekly plus daily ribavirin for 48 weeks.


RESULTS
A significantly higher proportion of patients who received peginterferon alfa-2a plus ribavirin had a sustained virologic response (defined as the absence of detectable HCV RNA 24 weeks after cessation of therapy) than of patients who received interferon alfa-2b plus ribavirin (56 percent vs. 44 percent, P<0.001) or peginterferon alfa-2a alone (56 percent vs. 29 percent, P<0.001). The proportions of patients with HCV genotype 1 who had sustained virologic responses were 46 percent, 36 percent, and 21 percent, respectively, for the three regimens. Among patients with HCV genotype 1 and high base-line levels of HCV RNA, the proportions of those with sustained virologic responses were 41 percent, 33 percent, and 13 percent, respectively. The overall safety profiles of the three treatment regimens were similar; the incidence of influenza-like symptoms and depression was lower in the groups receiving peginterferon alfa-2a than in the group receiving interferon alfa-2b plus ribavirin.


CONCLUSIONS
In patients with chronic hepatitis C, once-weekly peginterferon alfa-2a plus ribavirin was tolerated as well as interferon alfa-2b plus ribavirin and produced significant improvements in the rate of sustained virologic response, as compared with interferon alfa-2b plus ribavirin or peginterferon alfa-2a alone.
Probabilistic neural networks

-By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware "'neurons" that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation. Keywords--Neural network, Probability density function, Parallel processor, "Neuron", Pattern recognition, Parzen window, Bayes strategy, Associative memory.
Further Optimal Regret Bounds for Thompson Sampling

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have comparable or better empirical performance compared to the state of the art methods. In this paper, we provide a novel regret analysis for Thompson Sampling that proves the first near-optimal problem-independent bound of O( √ NT lnT ) on the expected regret of this algorithm. Our novel martingalebased analysis techniques are conceptually simple, and easily extend to distributions other than the Beta distribution. For the version of Thompson Sampling that uses Gaussian priors, we prove a problemindependent bound of O( √ NT lnN) on the expected regret, and demonstrate the optimality of this bound by providing a matching lower bound. This lower bound of Ω( √ NT lnN) is the first lower bound on the performance of a natural version of Thompson Sampling that is away from the optimal bound (O( √ NT )) achievable for the multi-armed bandit problem by another algorithm [4]. Our near-optimal problem-independent bounds for Thompson Sampling solve a COLT 2012 open problem of Chapelle and Li. Additionally, our techniques simultaneously provide the optimal problem-dependent bound of (1 + ) ∑ i lnT d(μi,μ1) +O( 2 ) on the expected regret. The optimal problem-dependent regret bound for this problem was first Preliminary work. Under review by AISTATS 2013. Do not distribute. proven recently by Kaufmann et al. [15].
Total Anomalous Pulmonary Venous Connection: From Embryology to a Prenatal Ultrasound Diagnostic Update

.08.002 C and the Chinese Taipei Society o mons.org/licenses/by-nc-nd/4.0/ also as a part of complex heart diseases or genetic syndromes. Among cyanotic heart diseases, total anomalous pulmonary venous connection (TAPVC) is the only condition involving a venous system malformation [1] and is easily misdiagnosed. The key characteristic of this uncommon congenital heart disease (CHD) is that all four pulmonary veins (PVs) fail to form a direct connection to the left atrium (LA); instead, they drain into the right heart through different routes of systemic venous return. The incidence of this condition is approximately 7e9 per 100,000 live births [2,3], and it accounts for 0.7e1.5% of all CHDs [4]. Patients with CHD who were born from 2000 to 2006 in Taiwan also exhibited a similar prevalence of TAPVC (0.11/ 1000) [5].
[Sensitization to acrylates caused by artificial acrylic nails: review of 15 cases].

BACKGROUND
Allergic contact dermatitis due to acrylates present in the workplace is a disease frequently reported among dentists, printers, and fiberglass workers. Recently, the number of cases of contact allergic dermatitis among beauticians specialized in sculpting artificial nails has increased.


OBJECTIVE
Our objective was to study the clinical characteristics and allergens implicated in allergic contact dermatitis due to acrylates in beauticians and users of sculpted nails.


MATERIAL AND METHODS
This was an observational, retrospective study of patients diagnosed with allergic contact dermatitis due to acrylates used in sculpting artificial nails over the last 26 years in the Hospital General Universitario, Valencia, Spain.


RESULTS
In total, 15 patients were diagnosed: 14 beauticians and 1 client. Most cases were diagnosed in the past 2 years. All were women, their mean age was 32.2 years, and 26.7 % had a personal or family history of atopy. The sensitization time varied between 1 month and 15 years. The most frequently affected areas were the fleshy parts of the fingers and hands. Three patients - 2 beauticians and 1 client - presented allergic asthma due to acrylates. All patients underwent patch testing with a standard battery of allergens and a battery of acrylates. The most frequent allergens were ethylene glycol dimethacrylate (13/15, 86.7 %), hydroxyethyl methacrylate (13/15, 86.7 %), triethylene glycol dimethacrylate (7/15, 46.7 %), 2-hydroxypropyl methacrylate (5/15, 33.3 %), and methyl methacrylate (5/15, 33.3 %).


CONCLUSIONS
Acrylate monomers used for sculpting artificial nails are important sensitizers for contact and occupational dermatitis. The most important consideration is primary and secondary prevention.