LLukas22/scidocs huggingface dataset:
Climate change. Is weather event attribution necessary for adaptation funding?

I nternational funds created largely for funding climate adaptation programs and projects in developing countries were fi rst legally established through the seventh session of the Conference of the Parties (COP-7) to the United Nations Framework Convention on Climate Change (FCCC) held in 2001 at Marrakesh. In 2009, at COP-15 in Copenhagen, delegates “took note” of a pledge from developed countries to commit U.S. $30 billion for the period 2010–2012, ramping up to $100 billion per annum by 2020, to support a mixture of climate adaptation and mitigation activities in developing countries. International adaptation fi nance has therefore been, and continues to be, a signifi cant political issue for the FCCC and for international institutions, such as the World Bank, the Global Environment Facility, and regional development banks ( 1). Yet governance arrangements and allocation principles for these climate adaptation funds remain both underdeveloped and politically contested ( 2, 3). A Green Climate Fund for disbursing such funds was established at COP-16 in Cancún, and a Transitional Committee is currently developing operational documents for the fund to be adopted at COP-17 in Durban, South Africa, later this year. In this Policy Forum, we challenge claims made by proponents of the new science of weather event attribution that the capability of calculating the odds that specifi c weather extremes are caused by humans could assist in the allocation of international climate adaptation funds. By claiming such a new guiding principle, for example, in the context of the emerging loss and damage agenda ( 4), these proponents may unwittingly be providing convenient cover for those who would rather continue debating adaptation allocation principles than securing and investing new funds (see the photo). Instead, we argue that adaptation funding should continue to be distributed according to development needs and adaptive capacity, as negotiated by the FCCC over many years and as is currently practiced.
Active learning for class imbalance problem

The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.
Detecting group review spam

It is well-known that many online reviews are not written by genuine users of products, but by spammers who write fake reviews to promote or demote some target products. Although some existing works have been done to detect fake reviews and individual spammers, to our knowledge, no work has been done on detecting spammer groups. This paper focuses on this task and proposes an effective technique to detect such groups.
AAS 04-297 UNIFORMLY DISTRIBUTED FLOWER CONSTELLATION DESIGN STUDY FOR GLOBAL NAVIGATION SYSTEM

The recently developed Flower Constellations have the satellites following the same relative trajectory with respect to an Earth rotating reference frame. This allows you to design an approximate uniform relative trajectory over the region of interest (which can be global) and uniformly distribute in time the satellites along the relative trajectory. Based on this idea, a new constellation of 30 satellites is here proposed, designed, and compared with the existing GPS and GLONASS constellation and the proposed European Galileo constellation. The proposed solution presents better characteristics in terms of attitude and position errors.
The nature and determinants of neuropsychological functioning in late-life depression.

CONTEXT
Cognitive impairment in late-life depression (LLD) is highly prevalent, disabling, poorly understood, and likely related to long-term outcome.


OBJECTIVES
To determine the characteristics and determinants of neuropsychological functioning LLD.


DESIGN
Cross-sectional study of groups of LLD patients and control subjects.


SETTING
Outpatient, university-based depression research clinic.


PARTICIPANTS
One hundred patients without dementia 60 years and older who met DSM-IV criteria for current episode of unipolar major depression (nonpsychotic) and 40 nondepressed, age- and education-equated control subjects.


MAIN OUTCOME MEASURES
A comprehensive neuropsychological battery.


RESULTS
Relative to control subjects, LLD patients performed poorer in all cognitive domains. More than half exhibited significant impairment (performance below the 10th percentile of the control group). Information processing speed and visuospatial and executive abilities were the most broadly and frequently impaired. The neuropsychological impairments were mediated almost entirely by slowed information processing (beta =.45-.80). Education (beta =.32) and ventricular atrophy (beta =.28) made additional modest contributions to variance in measures of language ability. Medical and vascular disease burden, apolipoprotein E genotype, and serum anticholinergicity did not contribute to variance in any cognitive domain.


CONCLUSIONS
Late-life depression is characterized by slowed information processing, which affects all realms of cognition. This supports the concept that frontostriatal dysfunction plays a key role in LLD. The putative role of some risk factors was validated (eg, advanced age, low education, depression severity), whereas others were not (eg, medical burden, age at onset of first depressive episode). Further studies of neuropsychological functioning in remitted LLD patients are needed to parse episode-related and persistent factors and to relate them to underlying neural dysfunction.
Do nondiabetic patients undergoing coronary artery bypass grafting surgery require intraoperative management of hyperglycemia?

OBJECTIVE
To study the effect of blood glucose (BG) control with insulin in preventing hyperglycemia during and after coronary artery bypass grafting (CABG) surgery in nondiabetic patients.


METHODS
In a randomized clinical trial, 120 nondiabetic patients who underwent elective CABG surgery were enrolled for study of whether the control of hyperglycemia was a need in such a surgery in a teaching heart hospital. The patients were randomly divided into study (n=60) and control (n=60) groups. In the study group, insulin was infused to maintain BG level between 110 mg/dL and 126 mg/dL (a modified insulin therapy protocol, and in the control group, the patients were excepted). Insulin therapy was limited to intraoperative period. BG levels during surgery and up to 48 hours after surgery and early postoperative complications were compared between the study and control groups.


RESULTS
One hundred seventeen patients completed the study (59 patients in study group and 58 in control group). Peak intraoperative BG level in the study group was 126.4±17.9 mg/dL and in the control group was 137.3±17.6 mg/dL (p=0.024). The frequencies of severe hyperglycemia (BG≥180 mg/dL) were 6 of 59 (10.1%) in the study group and 19 of 58 (32.7%) in the control group during operation (p=0.002). Peak postoperative BG level in the study group was 194.8±41.2 mg/dL and was 199.8±43.2 mg/dL in the control group (p=0.571). There was no hypoglycemic event in either group. The frequencies of early postoperative complications were 10 of 59 (16.9%) in the study group and 19 of 58 (32.7%) in the control group (p=0.047).


CONCLUSIONS
Hyperglycemia (BG≥126 mg/dL) is common in nondiabetic patients undergoing CABG surgery. A modified insulin therapy to maintain BG level between 110 mg/dL and 126 mg/dL may be acceptable for avoiding hypoglycemia and keeping intraoperative BG levels in acceptable range in nondiabetics.
Optimal combination forecasts for hierarchical time series

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these ‘‘hierarchical time series’’. They are commonly forecast using either a ‘‘bottom-up’’ or a ‘‘top-down’’ method. In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottomup approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions. We show in a simulation study that our method performs well compared to the topdown approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region. © 2011 Elsevier B.V. All rights reserved.
Effects of escitalopram on plasma concentrations of aripiprazole and its active metabolite, dehydroaripiprazole, in Japanese patients.

INTRODUCTION
The effects of escitalopram (10 mg/d) coadministration on plasma concentrations of aripiprazole and its active metabolite, dehydroaripiprazole, were studied in 13 Japanese psychiatric patients and compared with those of paroxetine (10 mg/d) coadministration.


METHODS
The patients had received 6-24 mg/d of aripiprazole for at least 2 weeks. Patients were randomly allocated to one of 2 treatment sequences: paroxetine-escitalopram (n=6) or escitalopram-paroxetine (n=7). Each sequence consisted of two 2-week phases. Plasma concentrations of aripiprazole and dehydroaripiprazole were measured using liquid chromatography with mass spectrometric detection.


RESULTS
Plasma concentrations of aripiprazole and the sum of aripiprazole and dehydroaripiprazole during paroxetine coadministration were 1.7-fold (95% confidence intervals [CI], 1.3-2.1, p<0.001) and 1.5-fold (95% CI 1.2-1.9, p<0.01) higher than those values before the coadministration. These values were not influenced by escitalopram coadministration (1.3-fold, 95% CI 1.1-1.5 and 1.3-fold, 95% CI 1.0-1.5). Plasma dehydroaripiprazole concentrations remained constant during the study.


CONCLUSION
The present study suggests that low doses of escitalopram can be safely coadministered with aripiprazole, at least from a pharmacokinetic point of view.
End-to-end attention-based large vocabulary speech recognition

Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems are hybrids of neural networks and Hidden Markov Models (HMMs). Recently, more direct end-to-end methods have been investigated, in which neural architectures were trained to model sequences of characters [1,2]. To our knowledge, all these approaches relied on Connectionist Temporal Classification [3] modules. We investigate an alternative method for sequence modelling based on an attention mechanism that allows a Recurrent Neural Network (RNN) to learn alignments between sequences of input frames and output labels. We show how this setup can be applied to LVCSR by integrating the decoding RNN with an n-gram language model and by speeding up its operation by constraining selections made by the attention mechanism and by reducing the source sequence lengths by pooling information over time. Recognition accuracies similar to other HMM-free RNN-based approaches are reported for the Wall Street Journal corpus.
Attitudes of Malaysian general hospital staff towards patients with mental illness and diabetes

BACKGROUND
The context of the study is the increased assessment and treatment of persons with mental illness in general hospital settings by general health staff, as the move away from mental hospitals gathers pace in low and middle income countries. The purpose of the study was to examine whether general attitudes of hospital staff towards persons with mental illness, and extent of mental health training and clinical experience, are associated with different attitudes and behaviours towards a patient with mental illness than towards a patients with a general health problem - diabetes.


METHODS
General hospital health professionals in Malaysia were randomly allocated one of two vignettes, one describing a patient with mental illness and the other a patient with diabetes, and invited to complete a questionnaire examining attitudes and health care practices in relation to the case. The questionnaires completed by respondents included questions on demographics, training in mental health, exposure in clinical practice to people with mental illness, attitudes and expected health care behaviour towards the patient in the vignette, and a general questionnaire exploring negative attitudes towards people with mental illness. Questionnaires with complete responses were received from 654 study participants.


RESULTS
Stigmatising attitudes towards persons with mental illness were common. Those responding to the mental illness vignette (N = 356) gave significantly lower ratings on care and support and higher ratings on avoidance and negative stereotype expectations compared with those responding the diabetes vignette (N = 298).


CONCLUSIONS
Results support the view that, in the Malaysian setting, patients with mental illness may receive differential care from general hospital staff and that general stigmatising attitudes among professionals may influence their care practices. More direct measurement of clinician behaviours than able to be implemented through survey method is required to support these conclusions.
Towards systems engineering-a personal view of progress

The article is written from the standpoint of an industrial consumer of tools, methods, and theories as they concern the building of software-intensive IT (information technology) systems. A personal view is given of the contribution that advances in software engineering and related disciplines have made or might make to the job of improving the way in which systems might be constructed in a predictable, cost-effective manner that meets the customer's requirements.<<ETX>>
Improving Multi-class Text Classification with Naive Bayes

There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification. Thesis Supervisor: Tommi Jaakkola Title: Assistant Professor of Electrical Engineering and Computer Science
Randomized phase II study of docetaxel and prednisone with or without OGX-011 in patients with metastatic castration-resistant prostate cancer.

PURPOSE
To determine the clinical activity of OGX-011, an antisense inhibitor of clusterin, in combination with docetaxel/prednisone in patients with metastatic castration-resistant prostate cancer.


PATIENTS AND METHODS
Patients were randomly assigned 1:1 to receive docetaxel/prednisone either with (arm A) or without (arm B) OGX-011 640 mg intravenously weekly. The primary end point was the proportion of patients with a prostate-specific antigen (PSA) decline of ≥ 50% from baseline, with the experimental therapy being considered of interest if the proportion of patients with a PSA decline was more than 60%. Secondary end points were objective response rate, progression-free survival (PFS), overall survival (OS), and changes in serum clusterin.


RESULTS
Eighty-two patients were accrued, 41 to each arm. OGX-011 adverse effects included rigors and fevers. After cycle 1, median serum clusterin decreased by 26% in arm A and increased by 0.9% in arm B (P < .001). PSA declined by ≥ 50% in 58% of patients in arm A and 54% in arm B. Partial response occurred in 19% and 25% of patients in arms A and B, respectively. Median PFS and OS times were 7.3 months (95% CI, 5.3 to 8.8 months) and 23.8 months (95% CI, 16.2 months to not reached), respectively, in arm A and 6.1 months (95% CI, 3.7 to 8.6 months) and 16.9 months (95% CI, 12.8 to 25.8 months), respectively, in arm B. Baseline factors associated with improved OS on exploratory multivariate analysis were an Eastern Cooperative Oncology Group performance status of 0 (hazard ratio [HR], 0.27; 95% CI, 0.14 to 0.51), presence of bone or lymph node metastases only (HR, 0.45; 95% CI, 0.25 to 0.79), and treatment assignment to OGX-011 (HR, 0.50; 95% CI, 0.29 to 0.87).


CONCLUSION
Treatment with OGX-011 and docetaxel was well tolerated with evidence of biologic effect and was associated with improved survival. Further evaluation is warranted.
Classification on ADHD with Deep Learning

Effective discrimination of attention deficit hyperactivity disorder (ADHD) using imaging and functional biomarkers would have fundamental influence on public health. In usual, the discrimination is based on the standards of American Psychiatric Association. In this paper, we modified one of the deep learning method on structure and parameters according to the properties of ADHD data, to discriminate ADHD on the unique public dataset of ADHD-200. We predicted the subjects as control, combined, inattentive or hyperactive through their frequency features. The results achieved improvement greatly compared to the performance released by the competition. Besides, the imbalance in datasets of deep learning model influenced the results of classification. As far as we know, it is the first time that the deep learning method has been used for the discrimination of ADHD with fMRI data.
Predictors of outcome from computer-based treatment for substance use disorders: Results from a randomized clinical trial.

BACKGROUND
Although empirical evidence for the effectiveness of technology-mediated interventions for substance use disorders is rapidly growing, the role of baseline characteristics of patients in predicting treatment outcomes of a technology-based therapy is largely unknown.


METHOD
Participants were randomly assigned to either standard methadone maintenance treatment or reduced standard treatment combined with the computer-based therapeutic education system (TES). An array of demographic and behavioral characteristics of participants (N=160) was measured at baseline. Opioid abstinence and treatment retention were measured weekly for a 52-week intervention period. Generalized linear model and Cox-regression were used to estimate the predictive roles of baseline characteristics in predicting treatment outcomes.


RESULTS
We found significant predictors of opioid abstinence and treatment retention within and across conditions. Among 21 baseline characteristics of participants, employment status, anxiety, and ambivalent attitudes toward substance use predicted better opioid abstinence in the reduced-standard-plus-TES condition compared to standard treatment. Participants who had used cocaine/crack in the past 30 days at baseline showed lower dropout rates in standard treatment, whereas those who had not used exhibited lower dropout rates in the reduced-standard-plus-TES condition.


CONCLUSIONS
This study is the first randomized controlled trial, evaluating over a 12-month period, how various aspects of participant characteristics impact outcomes for treatments that do or do not include technology-based therapy. Compared to standard alone treatment, including TES as part of the care was preferable for patients who were employed, highly anxious, and ambivalent about substance use and did not produce worse outcomes for any subgroups of participants.
Mechanisms of self-incompatibility in flowering plants

Self-incompatibility is a widespread mechanism in flowering plants that prevents inbreeding and promotes outcrossing. The self-incompatibility response is genetically controlled by one or more multi-allelic loci, and relies on a series of complex cellular interactions between the self-incompatible pollen and pistil. Although self-incompatibility functions ultimately to prevent self-fertilization, flowering plants have evolved several unique mechanisms for rejecting the self-incompatible pollen. The self-incompatibility system in the Solanaceae makes use of a multi-allelic RNase in the pistil to block incompatible pollen tube growth. In contrast, the Papaveraceae system appears to have complex cellular responses such as calcium fluxes, actin rearrangements, and programmed cell death occurring in the incompatible pollen tube. Finally, the Brassicaceae system has a receptor kinase signalling pathway activated in the pistil leading to pollen rejection. This review highlights the recent advances made towards understanding the cellular mechanisms involved in these self-incompatibility systems and discusses the striking differences between these systems.
A Survey of Automatic Indexing Techniques for Thai Text Documents

* Faculty of Information Technology, Rangsit University. Abstract With the rapidly increasing number of Thai text documents available in digital media and websites, it is important to find an efficient text indexing technique to facilitate search and retrieval. An efficient index would speed up the response time and improve the accessibility of the documents. Up to now, not much research in Thai text indexing has been conducted as compared to more commonly used languages like English or other European languages. In Thai text indexing, the extraction of indexing terms becomes a main issue because they cannot be specified automatically from text documents, due to the nature of Thai texts being non-segmented. As a result, there are many challenges for indexing Thai text documents. The ma-jority of Thai text indexing techniques can be divided into two main categories: a language-dependent technique and a lan-guage-independent technique as will be described in this paper.
Deep Abstract Q-Networks

Weexamine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma’s Revenge and Venture, remain challenging for existing methods. Methods using abstraction [5, 13] have shown to be useful in tackling long-horizon problems. We combine recent techniques of deep reinforcement learning with existing model-based approaches using an expert-provided state abstraction. We construct toy domains that elucidate the problem of long horizons, sparse rewards and high-dimensional inputs, and show that our algorithm significantly outperforms previous methods on these domains. Our abstraction-based approach outperforms Deep QNetworks [11] on Montezuma’s Revenge and Venture, and exhibits backtracking behavior that is absent from previous methods.