nils-herrmann/clean_scirepeval huggingface dataset:
Music Style Transfer Issues: A Position Paper

Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, “music style” is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of “music style”, current studies under the category of “music style transfer”, are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we see a vital necessity to re-define music style transfer more precisely and scientifically based on the uniqueness of music representation, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. Otherwise, an accumulated upcoming literature (all named after music style transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer music before the age of deep learning. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.
ABC transporters in Arthropods : genomic comparison and role in insecticide transport and resistance

About a 100 years ago, the Drosophila white mutant marked the birth of Drosophila genetics. The white gene turned out to encode the first well studied ABC transporter in arthropods. The ABC gene family is now recognized as one of the largest transporter families in all kingdoms of life. The majority of ABC proteins function as primary-active transporters that bind and hydrolyze ATP while transporting a large diversity of substrates across lipid membranes. Although extremely well studied in vertebrates for their role in drug resistance, less is known about the role of this family in the transport of endogenous and exogenous substances in arthropods. The ABC families of five insect species, a crustacean and a chelicerate have been annotated in some detail. We conducted a thorough phylogenetic analysis of the seven arthropod and human ABC protein subfamilies, to infer orthologous relationships that might suggest conserved function. Most orthologous relationships were found in the ABCB half transporter, ABCD, ABCE and ABCF subfamilies, but specific expansions within species and lineages are frequently observed and discussed. We next surveyed the role of ABC transporters in the transport of xenobiotics/plant allelochemicals and their involvement in insecticide resistance. The involvement of ABC transporters in xenobiotic resistance in arthropods is historically not well documented, but an increasing number of studies using unbiased differential gene expression analysis now points to their importance. We give an overview of methods that can be used to link ABC transporters to resistance. ABC proteins have also recently been implicated in the mode of action and resistance to Bt toxins in Lepidoptera. Given the enormous interest in Bt toxicology in transgenic crops, such findings will provide an impetus to further reveal the role of ABC transporters in arthropods.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection

Emotion recognition in conversations is crucial for building empathetic machines. Current work in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the selfand interspeaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.
Development and Validation of a Social Capital Questionnaire for Adolescent Students (SCQ-AS)

Social capital has been studied due to its contextual influence on health. However, no specific assessment tool has been developed and validated for the measurement of social capital among 12-year-old adolescent students. The aim of the present study was to develop and validate a quick, simple assessment tool to measure social capital among adolescent students.



A questionnaire was developed based on a review of relevant literature. For such, searches were made of the Scientific Electronic Library Online, Latin American and Caribbean Health Sciences, The Cochrane Library, ISI Web of Knowledge, International Database for Medical Literature and PubMed Central bibliographical databases from September 2011 to January 2014 for papers addressing assessment tools for the evaluation of social capital. Focus groups were also formed by adolescent students as well as health, educational and social professionals. The final assessment tool was administered to a convenience sample from two public schools (79 students) and one private school (22 students), comprising a final sample of 101 students. Reliability and internal consistency were evaluated using the Kappa coefficient and Cronbach's alpha coefficient, respectively. Content validity was determined by expert consensus as well as exploratory and confirmatory factor analysis.



The final version of the questionnaire was made up of 12 items. The total scale demonstrated very good internal consistency (Cronbach's alpha: 0.71). Reproducibility was also very good, as the Kappa coefficient was higher than 0.72 for the majority of items (range: 0.63 to 0.97). Factor analysis grouped the 12 items into four subscales: School Social Cohesion, School Friendships, Neighborhood Social Cohesion and Trust (school and neighborhood).



The present findings indicate the validity and reliability of the Social Capital Questionnaire for Adolescent Students.
An evaluation of reading comprehension of expository text in adults with traumatic brain injury.

This project was conducted to obtain information about reading problems of adults with traumatic brain injury (TBI) with mild-to-moderate cognitive impairments and to investigate how these readers respond to reading comprehension strategy prompts integrated into digital versions of text.



Participants from 2 groups, adults with TBI (n = 15) and matched controls (n = 15), read 4 different 500-word expository science passages linked to either a strategy prompt condition or a no-strategy prompt condition. The participants' reading comprehension was evaluated using sentence verification and free recall tasks.



The TBI and control groups exhibited significant differences on 2 of the 5 reading comprehension measures: paraphrase statements on a sentence verification task and communication units on a free recall task. Unexpected group differences were noted on the participants' prerequisite reading skills. For the within-group comparison, participants showed significantly higher reading comprehension scores on 2 free recall measures: words per communication unit and type-token ratio. There were no significant interactions.



The results help to elucidate the nature of reading comprehension in adults with TBI with mild-to-moderate cognitive impairments and endorse further evaluation of reading comprehension strategies as a potential intervention option for these individuals. Future research is needed to better understand how individual differences influence a person's reading and response to intervention.
Diabetic retinopathy detection using deep convolutional neural networks

Diabetic retinopathy is when damage occurs to the retina due to diabetes, which affects up to 80 percent of all patients who have had diabetes for 10 years or more. The expertise and equipment required are often lacking in areas where diabetic retinopathy detection is most needed. Most of the work in the field of diabetic retinopathy has been based on disease detection or manual extraction of features, but this paper aims at automatic diagnosis of the disease into its different stages using deep learning. This paper presents the design and implementation of GPU accelerated deep convolutional neural networks to automatically diagnose and thereby classify high-resolution retinal images into 5 stages of the disease based on severity. The single model accuracy of the convolutional neural networks presented in this paper is 0.386 on a quadratic weighted kappa metric and ensembling of three such similar models resulted in a score of 0.3996.
The Performance of Multiprogrammed Multiprocessor Scheduling Policies

Scheduling policies for general purpose multiprogrammed multiprocessors are not well understood. This paper examines various policies to determine which properties of a scheduling policy are the most significant determinants of performance. We compare a more comprehensive set of policies than previous work, including one important scheduling policy that has not previously been examined. We also compare the policies under workloads that we feel are more realistic than previous studies have used. Using these new workloads, we arrive at different conclusions than reported in earlier work. In particular, we find that the “smallest number of processes first” (SNPF) scheduling discipline performs poorly, even when the number of processes in a job is positively correlated with the total service demand of the job. We also find that policies that allocate an equal fraction of the processing power to each job in the system perform better, on the whole, than policies that allocate processing power unequally. Finally, we find that for lock access synchronization, dividing processing power equally among all jobs in the system is a more effective property of a scheduling policy than the property of minimizing synchronization spin-waiting, unless demand for synchronization is extremely high. (The latter property is implemented by coscheduling processes within a job, or by using a thread management package that avoids preemption of processes that hold spinlocks.) Our studies are done by simulating abstract models of the system and the workloads.
Dynamic asset allocation for varied financial markets under regime switching framework

Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008. Portfolio optimization is the most developed method to examine the optimal decision for asset allocation. We employ the hidden Markov model to identify regimes in varied financial markets; a regime switching model gives multiple distributions and this information can convert the static mean–variance model into an optimization problem under uncertainty, which is the case for unobservable market regimes. We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem. In addition, we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling-horizon simulation. We conclude that the regime information helps portfolios avoid risk during left-tail events. Portfolio optimization is the most researched and practiced method to examine the optimal decision for asset allocation. The mean–variance model introduced by Markowitz (1952) is the basis for portfolio selection, which finds the optimal portfolio by computing the risk-return tradeoff using the estimated mean vector and the covariance matrix of asset returns. One of the advantages of the Markowitz model is that there are no restrictions on the type of assets that can be included in the model. For example, commodity futures, which has become drastically popular among investors as a major asset class, like stocks and bonds can be easily included in the model to solve the portfolio selection problem. However, the Markowitz model is a single-period model without stochastic characteristics and also assumes that the multi-dimensional return series of assets have constant mean vector and covariance matrix. In this paper, we extend the traditional Markowitz portfolio model to address the changing nature of the covariance matrix under differing market conditions. Certainly, one of the severe issues arising during 2008 crash was the increase in correlation (contagion) that occurred and the ensuing lack of diversification by many investors (even those applying Markowitz models). The regime detection methodology provides an intuitive and practical way to anticipate changing correlation conditions. As such, the research is on the pathway of the original Markowitz tradition. There have been many studies indicating the existence of multiple regimes in financial markets especially the stock market. The hidden Markov model (HMM) is a popular method for regime identification, which has been widely used in engineering and …
Brain age predicts mortality

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.
Progress in Circulating Tumor Cell Research Using Microfluidic Devices

Circulating tumor cells (CTCs) are a popular topic in cancer research because they can be obtained by liquid biopsy, a minimally invasive procedure with more sample accessibility than tissue biopsy, to monitor a patient's condition. Over the past decades, CTC research has covered a wide variety of topics such as enumeration, profiling, and correlation between CTC number and patient overall survival. It is important to isolate and enrich CTCs before performing CTC analysis because CTCs in the blood stream are very rare (0⁻10 CTCs/mL of blood). Among the various approaches to separating CTCs, here, we review the research trends in the isolation and analysis of CTCs using microfluidics. Microfluidics provides many attractive advantages for CTC studies such as continuous sample processing to reduce target cell loss and easy integration of various functions into a chip, making "do-everything-on-a-chip" possible. However, tumor cells obtained from different sites within a tumor exhibit heterogenetic features. Thus, heterogeneous CTC profiling should be conducted at a single-cell level after isolation to guide the optimal therapeutic path. We describe the studies on single-CTC analysis based on microfluidic devices. Additionally, as a critical concern in CTC studies, we explain the use of CTCs in cancer research, despite their rarity and heterogeneity, compared with other currently emerging circulating biomarkers, including exosomes and cell-free DNA (cfDNA). Finally, the commercialization of products for CTC separation and analysis is discussed.
Power system blackouts - literature review

Increasing electrical energy demand, modern lifestyles and energy usage patterns have made the world fully dependant on power systems. This instigated mandatory requirements for the operators to maintain high reliability and stability of the power system grid. However, the power system is a highly nonlinear system, which changes its operations continuously. Therefore, it is very challenging and uneconomical to make the system be stable for all disturbances. The system is usually designed to handle a single outage at a time. However, during the last decade several major blackouts were reported and all of them started with single outages. Each major blackout was mandatorily and transparently reported to the public. The properly written blackout reports help to minimize the operational risk, by strengthening the system and its operations based on selected high risk contingencies. In the last decade, several major blackouts were reported separately in many research papers. This paper lists a good collection of properly reported literatures on power system stability and reliability including history of blackouts. Some critical comments on root causes, lessons learnt from the blackouts and solutions are addressed while briefly discussing the blackout events presented in published literatures.
Postpartum Hypertension: Etiology, Diagnosis, and Management.

Postpartum hypertension complicates approximately 2% of pregnancies and, similar to antepartum severe hypertension, can have devastating consequences including maternal death.



This review aims to increase the knowledge and skills of women's health care providers in understanding, diagnosing, and managing hypertension in the postpartum period.



Hypertension complicating pregnancy, including postpartum, is defined as systolic blood pressure 140 mm Hg or greater and/or diastolic blood pressure 90 mm Hg or greater on 2 or more occasions at least 4 hours apart. Severe hypertension is defined as systolic blood pressure 160 mm Hg or greater and/or diastolic blood pressure 110 mm Hg or greater on 2 or more occasions repeated at a short interval (minutes). Workup for secondary causes of hypertension should be pursued, especially in patients with severe or resistant hypertension, hypokalemia, abnormal creatinine, or a strong family history of renal disease. Because severe hypertension is known to cause maternal stroke, women with severe hypertension sustained over 15 minutes during pregnancy or in the postpartum period should be treated with fast-acting antihypertension medication. Labetalol, hydralazine, and nifedipine are all effective for acute management, although nifedipine may work the fastest. For persistent postpartum hypertension, a long-acting antihypertensive agent should be started. Labetalol and nifedipine are also both effective, but labetalol may achieve control at a lower dose with fewer adverse effects.



Providers must be aware of the risks associated with postpartum hypertension and educate women about the symptoms of postpartum preeclampsia. Severe acute hypertension should be treated in a timely fashion to avoid morbidity and mortality. Women with persistent postpartum hypertension should be administered a long-acting antihypertensive agent.


Target Audience
Obstetricians and gynecologists, family physicians.



After completing this activity, the learner should be better able to assist patients and providers in identifying postpartum hypertension; provide a framework for the evaluation of new-onset postpartum hypertension; and provide instructions for the management of acute severe and persistent postpartum hypertension.
The impact of motor development on typical and atypical social cognition and language : A systematic review

Motor development allows infants to gain knowledge of the world but its vital role in social development is often ignored.  A systematic search for papers investigating the relationship between motor and social skills was conducted , including research in typical development and in Developmental Coordination Disorder, Autism Spectrum Disorders and Specific Language Impairment.  The search identified 42 studies, many of which highlighted a significant relationship between motor skills and the development of social cognition, language and social interactions.  This complex relationship requires more attention from researchers and practitioners, allowing the development of more tailored intervention techniques for those at risk of motor, social and language difficulties.  Practitioner Message  Significant relationships exist between the development of motor skills, social cognition, language and social interactions in typical and atypical development  Practitioners should be aware of the relationships between these aspects of development and understand the impact that early motor difficulties may have on later social skills  Complex relationships between motor and social skills are evident in children with ASD, DCD and SLI  Early screening and more targeted interventions may be appropriate
Teacher-Student Framework : A Reinforcement Learning Approach

We propose a reinforcement learning approach to learning to teach. Following Torrey and Taylor’s framework [18], an agent (the “teacher”) advises another one (the “student”) by suggesting actions the latter should take while learning a specific task in a sequential decision problem; the teacher is limited by a “budget” (the number of times such advice can be given). Our approach assumes a principled decision-theoretic setting; both the student and the teacher are modeled as reinforcement learning agents. We provide experimental results with the Mountain car domain, showing how our approach outperforms the heuristics proposed by Torrey and Taylor [18]. Moreover, we propose a technique for a student to take into account advice more efficiently and we experimentally show that performances are improved in Torrey and Taylor’s setting.
Evaluation of Inter-Process Communication Mechanisms

The abstraction of a process enables certain primitive forms of communication during process creation and destruction such as wait(). However, the operating system provides more general mechanisms for flexible inter-process communication. In this paper, we have studied and evaluated three commonly-used inter-process communication devices pipes, sockets and shared memory. We have identified the various factors that could affect their performance such as message size, hardware caches and process scheduling, and constructed experiments to reliably measure the latency and transfer rate of each device. We identified the most reliable timer APIs available for our measurements. Our experiments reveal that shared memory provides the lowest latency and highest throughput, followed by kernel pipes and lastly, TCP/IP sockets. However, the latency trends provide interesting insights into the construction of each mechanism. We also make certain observations on the pros and cons of each mechanism, highlighting its usefulness for different kinds of applications.