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We consider the parametric learning problem, where the objective of the learner is determined by a parametric loss function. Employing empirical risk minimization with possibly regularization, the inferred parameter vector will be biased toward the training samples. Such bias is measured by the cross validation procedure in practice where the data set is partitioned into a training set used for training and a validation set, which is not used in training and is left to measure the out-of-samplearXiv:1711.05323v1 fatcat:2gs6msra5rcitdaw3aivedbo7e
more »... performance. A classical cross validation strategy is the leave-one-out cross validation (LOOCV) where one sample is left out for validation and training is done on the rest of the samples that are presented to the learner, and this process is repeated on all of the samples. LOOCV is rarely used in practice due to the high computational complexity. In this paper, we first develop a computationally efficient approximate LOOCV (ALOOCV) and provide theoretical guarantees for its performance. Then we use ALOOCV to provide an optimization algorithm for finding the regularizer in the empirical risk minimization framework. In our numerical experiments, we illustrate the accuracy and efficiency of ALOOCV as well as our proposed framework for the optimization of the regularizer.
The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It isdoi:10.3390/computation9020017 fatcat:y4v63bxzybbohei4vhqrvjg4w4
more »... n a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.
All cases were studied between years 2008 and 2012 in Shahin Dezh. Results: In total, 492 cases of brucellosis were reported. ... Objectives: This study investigated the epidemiological features of brucellosis in Shahin Dezh, Western Azarbaijan province, North West of Iran. ... Setting and Participants This study was done in the Shahin Dezh County. Shahin Dezh County is a county in the West Azarbaijan Province of Iran. The capital of the county is Shahin Dezh. ...doi:10.5812/archcid.22279 fatcat:znbvaj4tsrcs7iyw3xgg7vktgy
The randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependent randomization improves the performance in terms of the required number of random features. In this paper, we are concerned with the randomized-feature approach in supervisedarXiv:1712.07102v1 fatcat:bcudauqorbehrjotutio56hsxa
more »... ng for good generalizability. We propose the Energy-based Exploration of Random Features (EERF) algorithm based on a data-dependent score function that explores the set of possible features and exploits the promising regions. We prove that the proposed score function with high probability recovers the spectrum of the best fit within the model class. Our empirical results on several benchmark datasets further verify that our method requires smaller number of random features to achieve a certain generalization error compared to the state-of-the-art while introducing negligible pre-processing overhead. EERF can be implemented in a few lines of code and requires no additional tuning parameters.
Shahin, and F. ... Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:7, No:2, 2013 Influence of Ambiguity Cluster on Quality Improvement in Image Compression Safaa Al-Ali, Ahmad ...doi:10.5281/zenodo.1073446 fatcat:ncfvszjv4rhbjowk7kbj3b37xi
Attention-deficit/hyperactivity disorder is a common neurobehavioral disorder of childhood and adolescence. The etiology of attention-deficit/hyperactivity disorder is not well understood. Neurochemical studies suggest, alterations in catecholaminergic, mainly dopaminergic and noradrenergic, transmitter functions markedly contribute to the symptoms of this disorder. The symptoms of attention-deficit/hyperactivity disorder are significantly ameliorated by the agents that specifically influencepmid:17061613 fatcat:sxdkwxva4fhetaamma2uz5iwbq
more »... ese neurotransmitters. Animal studies implicate areas of the brain in which these neurotransmitters are most dominant. Psychostimulant medications are generally the first choice in the treatment of attention-deficit/hyperactivity disorder. Approximately 70% of the children treated show improvement in the primary attention-deficit/hyperactivity disorder symptoms and in comorbidity such as conduct disorder, although the benefits may not hold beyond two years. Despite the well-established efficacy and safety of stimulants for attention-deficit/hyperactivity disorder, alternative medicines are still needed for several reasons. About 30% of children and adolescents with this disorder may not respond to stimulants or may be unable to tolerate potential adverse events such as decreased appetite, mood lability and sleep disturbances. Although stimulants do not increase the risk for later substance abuse in attention-deficit/hyperactivity disorder, concerns have been raised about special prescription rules and a potential for abuse by persons other than the attention-deficit/hyperactivity disorder subjects. This review focuses on etiology, assessment, and treatment of attention-deficit/hyperactivity disorder.
Low birth weight is a prospective marker of future growth and development and a retrospective marker of mothers nutritional and health status. Methods: A community based prospective study conducted in field practice areas of Urban and Rural Health Training Center Department of Community Medicine, JNMCH, AMU, Aligarh. Participants were registered pregnant women who were in their first trimester and whose expected date of delivery lies within our study period. Study period was of one year. Datadoi:10.18203/2320-1770.ijrcog20160568 fatcat:tg5v5jw7vjhuxhiowiudcvc6im
more »... s analyzed using SPSS version 20. Percentages and chi square test used. Results: Prevalence of LBW was found to be 40%. Occurrence of LBW babies decreased as the nutritional intake of mothers in the form of kilocalories consumed per day increased. The association between dietary calorie intake and birth weight was found to be statistically highly significant. Conclusions: Nutritional status of mother has to be improved not only during pregnancy, but also in her early childhood by undertaking food supplementation programs implemented through National Health Programs that improve the weight gain during delivery and result in improved fetal outcome.
ShahKhan, Haidar Ali, Sher Hayat, Sohail Ahmad, Muhammad Ibrahim, Syed Adnan haider, Ijaz Ahmad, Ihtesham Ul Haq ... GGTTGTGGCAGGAATGTACC 109bp NM_001012284.1 UBCH8 F:AGAATTCAGAAGGAACTTGCAG R:AAGGTGACCTTGGGGGGTTTA 195bp NM_001191190.1 GAPDH F:CTCCCAACGTGTCTGTTGTG R:TGAGCTTGACAAAGTGGTCG 222bp NM_001034034.2 Shahin ...doi:10.2298/bah2002215k fatcat:lmqqgxhctzh53nnrk6av2lymcq
As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiple search results when querying a database, due to the lack of such scenarios in existing public datasets. In this paper, we propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results, whicharXiv:2112.08351v1 fatcat:3iu44wsmeff7dgcpz2xjl23sv4
more »... ances user experience by allowing them to choose from multiple options instead of just one. To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset. We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns. Furthermore, pre-fine tuning and multi-task learning help our model to improve performance on DSR-disambiguation even in the absence of in-domain data, suggesting that it can be learned as a universal dialog skill. Our data and code will be made publicly available.
Estimates of the annual prevalence for Obsessive Compulsive Disorder (OCD) were consistent across the international sites range, 1.9% -2.5%. The nine population surveys, which used Diagnostic Interview Schedule, estimated a six-month prevalence of OCD ranging from 0.7% to 2.1%. This study performed in order to determine the prevalence of OCD in a population-based study among Iranian adults aged 18 and older and to study the association of them with factors such as sex, marital status,doi:10.1186/1471-244x-4-2 pmid:15018627 pmcid:PMC362878 fatcat:c2siegwzyndxhbmem6xlnxssmi
more »... type of occupation and residential area. Methods: A cross-sectional nationwide epidemiological study of the Iranian population aged 18 and older was designed to estimate the prevalence of psychiatric disorders and their association with the above mentioned factors. 25180 individuals were selected and interviewed through a randomized systematic and cluster sampling method from all Iranian households. Schedule for Affective Disorders and Schizophrenia (SADS) and Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria were used in diagnosis of OCD. 250 clinical psychologists interviewed the selected subjects face to face at their homes. Results: The prevalence of OCD in Iran is 1.8% (0.7% and 2.8% in males and females; respectively). 50.3% of the survey sample were men, 49.9% women, 29.1% single, 67.45% married, 0.4% separated or divorced, 2.5% widow/widower and 4% undetermined. All of the above-mentioned factors were examined in the univariate and multivariate logistic regression models. Although the data did not fit the models well, but in univariate models, sex, the category "single" of marital status, age, the categories "business" and "housewife" and residential areas showed significant effect adjusting for the factors, but the models didn't fit the data properly.
The article's abstract is no available.doi:10.18502/ijph.v49i6.3376 fatcat:t2f3val2kndntpzroyfcaxveju
doi:10.34172/aim.2021.82 pmid:34488324 fatcat:kb73ky5kfrerxnldxeuaekarpa
Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders with lifelong impact of the affected individuals. It is usually co-morbid with other psychiatric disorders. This paper aims to review current knowledge about ADHD in imprisoned individuals. The rate of ADHD in prisoners ranges from 10% to 70% and it has been suggested that ADHD, even without co-morbidity with conduct disorder, is a risk factor for imprisonment. Based on these findings, it may bepmid:21685851 fatcat:m6w7v3uuzfad7ntp4wgglauchm
more »... se to include the assessment of ADHD symptoms in all adult and adolescent prisoners. This is while available psychiatric resources for the adequate management of ADHD in prisoners are limited. Most of current knowledge on the topic comes from western countries. There is an urgent need for studies that will explore the effect of other cultures on the interactions between ADHD and imprisonment, especially in developing countries worldwide. At this point, ADHD seems to be an ignored research area in developing countries.
Recent neural models that extend the pretrain-then-finetune paradigm continue to achieve new state-of-the-art results on joint goal accuracy (JGA) for dialogue state tracking (DST) benchmarks. However, we call into question their robustness as they show sharp drops in JGA for conversations containing utterances or dialog flows with realistic perturbations. Inspired by CheckList (Ribeiro et al., 2020), we design a collection of metrics called CheckDST that facilitate comparisons of DST models onarXiv:2112.08321v1 fatcat:aljjduf65zepdcjt3swt4k24ky
more »... comprehensive dimensions of robustness by testing well-known weaknesses with augmented test sets. We evaluate recent DST models with CheckDST and argue that models should be assessed more holistically rather than pursuing state-of-the-art on JGA since a higher JGA does not guarantee better overall robustness. We find that span-based classification models are resilient to unseen named entities but not robust to language variety, whereas those based on autoregressive language models generalize better to language variety but tend to memorize named entities and often hallucinate. Due to their respective weaknesses, neither approach is yet suitable for real-world deployment. We believe CheckDST is a useful guide for future research to develop task-oriented dialogue models that embody the strengths of various methods.
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