nils-herrmann/clean_scirepeval huggingface dataset:
Interpolation-based classifier generation in XCSF

XCSF is a rule-based on-line learning system that makes use of local learning concepts in conjunction with gradient-based approximation techniques. It is mainly used to learn functions, or rather regression problems, by means of dividing the problem space into smaller subspaces and approximate the function values linearly therein. In this paper, we show how local interpolation can be incorporated to improve the approximation speed and thus to decrease the system error. We describe how a novel interpolation component integrates into the algorithmic structure of XCSF and thereby augments the well-established separation into the performance, discovery and reinforcement component. To underpin the validity of our approach, we present and discuss results from experiments on three test functions of different complexity, i.e. we show that by means of the proposed strategies for integrating the locally interpolated values, the overall performance of XCSF can be improved.
Learning to generate images with perceptual similarity metrics

Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation. Supervised training of image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the mismatch between a generated image and its corresponding target image. We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM) . Because MS-SSIM is differentiable, it is easily incorporated into gradient-descent learning. We compare the consequences of using MS-SSIM versus PL loss on training autoencoders. Human observers reliably prefer images synthesized by MS-SSIM-optimized models over those synthesized by PL-optimized models, for two distinct PL measures (L1 and L2 distances). We also explore the effect of training objective on image encoding and analyze conditions under which perceptually-optimized representations yield better performance on image classification. Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. We argue that significant advances can be made in modeling images through the use of training objectives that are well aligned to characteristics of human perception.
Customer preferences versus managerial decision-making in open innovation communities: the case of Starbucks

Customers can participate in open innovation communities posting innovation ideas, which in turn can receive comments and votes from the rest of the community, highlighting user preferences. However, the final decision about implementing innovations corresponds to the company. This paper is focused on the customers’ activity in open innovation communities. The aim is to identify the main topics of customers’ interests in order to compare these topics with managerial decision-making. The results obtained reveal first that both votes and comments can be used to predict user preferences; and second, that customers tend to promote those innovations by reporting more comfort and benefits. In contrast, managerial decisions are more focused on the distinctive features associated with the brand image.
A meta-analysis of the efficacy of second-generation antipsychotics

Consensus panel recommendations regarding choice of an antipsychotic agent for schizophrenia differ markedly, but most consider second-generation antipsychotics (SGAs) as a homogeneous group. It has been suggested that SGAs seem falsely more efficacious than first-generation antipsychotics (FGAs) as a result of reduced efficacy due to use of a high-dose comparator, haloperidol. We performed (1) a meta-analysis of randomized efficacy trials comparing SGAs and FGAs, (2) comparisons between SGAs, (3) a dose-response analysis of FGAs and SGAs, and (4) an analysis of the effect on efficacy of an overly high dose of an FGA comparator.



Literature search of clinical trials between January 1953 and May 2002 of patients with schizophrenia from electronic databases, reference lists, posters, the Food and Drug Administration, and other unpublished data. We included 124 randomized controlled trials with efficacy data on 10 SGAs vs FGAs and 18 studies of comparisons between SGAs. Two of us independently extracted the sample sizes, means, and standard deviation of the efficacy data.



Using the Hedges-Olkin algorithm, the effect sizes of clozapine, amisulpride, risperidone, and olanzapine were 0.49, 0.29, 0.25, and 0.21 greater than those of FGAs, with P values of 2 x 10-8, 3 x 10-7, 2 x 10-12, and 3 x 10-9, respectively. The remaining 6 SGAs were not significantly different from FGAs, although zotepine was marginally different. No efficacy difference was detected among amisulpride, risperidone, and olanzapine. We found no evidence that the haloperidol dose (or all FGA comparators converted to haloperidol-equivalent doses) affected these results when we examined its effect by drug or in a 2-way analysis of variance model in which SGA effectiveness is entered as a second factor.



Some SGAs are more efficacious than FGAs, and, therefore, SGAs are not a homogeneous group.
Structured Aspect Extraction

Aspect extraction identifies relevant features of an entity from a textual description and is typically targeted to product reviews, and other types of short text, as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms, often neglecting associated modifiers or embedding them in the aspect terms without proper distinction. Moreover, flat syntactic structures are often assumed, resulting in inaccurate extractions of complex aspects. This paper studies the problem of structured aspect extraction, a variant of traditional aspect extraction aiming at a fine-grained extraction of complex (i.e., hierarchical) aspects. We propose an unsupervised and scalable method for structured aspect extraction consisting of statistical noun phrase clustering, cPMI-based noun phrase segmentation, and hierarchical pattern induction. Our evaluation shows a substantial improvement over existing methods in terms of both quality and computational efficiency.
Biofiltration vs conventional activated sludge plants: what about priority and emerging pollutants removal?

This paper compares the removal performances of two complete wastewater treatment plants (WWTPs) for all priority substances listed in the Water Framework Directive and additional compounds of interest including flame retardants, surfactants, pesticides, and personal care products (PCPs) (n = 104). First, primary treatments such as physicochemical lamellar settling (PCLS) and primary settling (PS) are compared. Similarly, biofiltration (BF) and conventional activated sludge (CAS) are then examined. Finally, the removal efficiency per unit of nitrogen removed of both WWTPs for micropollutants is discussed, as nitrogenous pollution treatment results in a special design of processes and operational conditions. For primary treatments, hydrophobic pollutants (log K ow > 4) are well removed (>70 %) for both systems despite high variations of removal. PCLS allows an obvious gain of about 20 % regarding pollutant removals, as a result of better suspended solids elimination and possible coagulant impact on soluble compounds. For biological treatments, variations of removal are much weaker, and the majority of pollutants are comparably removed within both systems. Hydrophobic and volatile compounds are well (>60 %) or very well removed (>80 %) by sorption and volatilization. Some readily biodegradable molecules are better removed by CAS, indicating a better biodegradation. A better sorption of pollutants on activated sludge could be also expected considering the differences of characteristics between a biofilm and flocs. Finally, comparison of global processes efficiency using removals of micropollutants load normalized to nitrogen shows that PCLS + BF is as efficient as PS + CAS despite a higher compactness and a shorter hydraulic retention time (HRT). Only some groups of pollutants seem better removed by PS + CAS like alkylphenols, flame retardants, or di-2-ethylhexyl phthalate (DEHP), thanks to better biodegradation and sorption resulting from HRT and biomass characteristics. For both processes, and out of the 68 molecules found in raw water, only half of them are still detected in the water discharged, most of the time close to their detection limit. However, some of them are detected at higher concentrations (>1 μg/L and/or lower than environmental quality standards), which is problematic as they represent a threat for aquatic environment.
Analysis of HLA class II genes in Hashimoto's thyroiditis reveals differences compared to Graves’ disease

Graves’ disease (GD) and Hashimoto's thyroiditis (HT) represent the commonest forms of autoimmune thyroid disease (AITD) each presenting with distinct clinical features. Progress has been made in determining association of HLA class II DRB1, DQB1 and DQA1 loci with GD demonstrating a predisposing effect for DR3 (DRB1*03-DQB1*02-DQA1*05) and a protective effect for DR7 (DRB1*07-DQB1*02-DQA1*02). Small data sets have hindered progress in determining HLA class II associations with HT. The aim of this study was to investigate DRB1-DQB1-DQA1 in the largest UK Caucasian HT case control cohort to date comprising 640 HT patients and 621 controls. A strong association between HT and DR4 (DRB1*04-DQB1*03-DQA1*03) was detected (P=6.79 × 10−7, OR=1.98 (95% CI=1.51–2.59)); however, only borderline association of DR3 was found (P=0.050). Protective effects were also detected for DR13 (DRB1*13-DQB1*06-DQA1*01) (P=0.001, OR=0.61 (95% CI=0.45–0.83)) and DR7 (P=0.013, OR=0.70 (95% CI=0.53–0.93)). Analysis of our unique cohort of subjects with well characterized AITD has demonstrated clear differences in association within the HLA class II region between HT and GD. Although HT and GD share a number of common genetic markers this study supports the suggestion that differences in HLA class II genotype may, in part, contribute to the different immunopathological processes and clinical presentation of these related diseases.
Compliance with homework assignments in cognitive-behavioral psychotherapy for depression: Relation to outcome and methods of enhancement

Despite the importance attached to homework in cognitive-behavioral therapy for depression, quantitative studies of its impact on outcome have been limited. One aim of the present study was to replicate a previous finding suggesting that improvement can be predicted from the quality of the client's compliance early in treatment. If homework is indeed an effective ingredient in this form of treatment, it is important to know how compliance can be influenced. The second aim of the present study was to examine the effectiveness of several methods of enhancing compliance that have frequently been recommended to therapists. The data were drawn from 235 sessions received by 25 clients. Therapists' ratings of compliance following the first two sessions of treatment contributed significantly to the prediction of improvement at termination (though not at followup). However, compliance itself could not be predicted from any of the clients' ratings of therapist behavior in recommending the assignments.
The sucrose–trehalose 6-phosphate (Tre6P) nexus: specificity and mechanisms of sucrose signalling by Tre6P

Trehalose 6-phosphate (Tre6P), the intermediate of trehalose biosynthesis, has a profound influence on plant metabolism, growth, and development. It has been proposed that Tre6P acts as a signal of sugar availability and is possibly specific for sucrose status. Short-term sugar-feeding experiments were carried out with carbon-starved Arabidopsis thaliana seedlings grown in axenic shaking liquid cultures. Tre6P increased when seedlings were exogenously supplied with sucrose, or with hexoses that can be metabolized to sucrose, such as glucose and fructose. Conditional correlation analysis and inhibitor experiments indicated that the hexose-induced increase in Tre6P was an indirect response dependent on conversion of the hexose sugars to sucrose. Tre6P content was affected by changes in nitrogen status, but this response was also attributable to parallel changes in sucrose. The sucrose-induced rise in Tre6P was unaffected by cordycepin but almost completely blocked by cycloheximide, indicating that de novo protein synthesis is necessary for the response. There was a strong correlation between Tre6P and sucrose even in lines that constitutively express heterologous trehalose-phosphate synthase or trehalose-phosphate phosphatase, although the Tre6P:sucrose ratio was shifted higher or lower, respectively. It is proposed that the Tre6P:sucrose ratio is a critical parameter for the plant and forms part of a homeostatic mechanism to maintain sucrose levels within a range that is appropriate for the cell type and developmental stage of the plant.
Event Digest: A Holistic View on Past Events

For a general user, easy access to vast amounts of online information available on past events has made retrospection much harder. We propose a problem of automatic event digest generation to aid effective and efficient retrospection. For this, in addition to text, a digest should maximize the reportage of time, geolocations, and entities to present a holistic view on the past event of interest.
 We propose a novel divergence-based framework that selects excerpts from an initial set of pseudo-relevant documents, such that the overall relevance is maximized, while avoiding redundancy in text, time, geolocations, and named entities, by treating them as independent dimensions of an event. Our method formulates the problem as an Integer Linear Program (ILP) for global inference to diversify across the event dimensions. Relevance and redundancy measures are defined based on JS-divergence between independent query and excerpt models estimated for each event dimension. Elaborate experiments on three real-world datasets are conducted to compare our methods against the state-of-the-art from the literature. Using Wikipedia articles as gold standard summaries in our evaluation, we find that the most holistic digest of an event is generated with our method that integrates all event dimensions. We compare all methods using standard Rouge-1, -2, and -SU4 along with Rouge-NP, and a novel weighted variant of Rouge.
Optimal and stable gait planning of a quadruped robot for trotting over uneven terrains

To obtain satisfactory performance for legged robots, they should be able to move on natural environment stably. In this article, the path planning of a quadruped robot in the motion over uneven terrains will be investigated. One of major problems for traversing uneven terrains is the robot stability. Thus, a condition to guarantee the robot stability over such terrains will be proposed. This condition defines a point with zero moment in 3D space. This condition makes it possible to vary of the height of main body stably during trotting which is a main issue for traversing uneven terrains. Based on this condition, a linear ZMP equation will be defined so that the main body acceleration acts as control input and the ZMP acts as output. A linear quadratic regulator with stability constraint and fixed final states will be proposed to obtain the COG path which results in minimum acceleration. The tip of swing foot path planning will be fulfilled so that the generated path leads to avoid any collision with environment. Furthermore, the path of tip of swing legs is designed with minimum acceleration. The simulation results confirm the validity of proposed methods in terms of stability and the improvement of the consumption energy over uneven terrains.
Endogenous Technological Change

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Strategies for Handling Missing Data in Electronic Health Record Derived Data

Electronic health records (EHRs) present a wealth of data that are vital for improving patient-centered outcomes, although the data can present significant statistical challenges. In particular, EHR data contains substantial missing information that if left unaddressed could reduce the validity of conclusions drawn. Properly addressing the missing data issue in EHR data is complicated by the fact that it is sometimes difficult to differentiate between missing data and a negative value. For example, a patient without a documented history of heart failure may truly not have disease or the clinician may have simply not documented the condition. Approaches for reducing missing data in EHR systems come from multiple angles, including: increasing structured data documentation, reducing data input errors, and utilization of text parsing / natural language processing. This paper focuses on the analytical approaches for handling missing data, primarily multiple imputation. The broad range of variables available in typical EHR systems provide a wealth of information for mitigating potential biases caused by missing data. The probability of missing data may be linked to disease severity and healthcare utilization since unhealthier patients are more likely to have comorbidities and each interaction with the health care system provides an opportunity for documentation. Therefore, any imputation routine should include predictor variables that assess overall health status (e.g. Charlson Comorbidity Index) and healthcare utilization (e.g. number of encounters) even when these comorbidities and patient encounters are unrelated to the disease of interest. Linking the EHR data with other sources of information (e.g. National Death Index and census data) can also provide less biased variables for imputation. Additional methodological research with EHR data and improved epidemiological training of clinical investigators is warranted.
Data Trasfer From MySQL To Hadoop: Implementers' Perspective

Processes use on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, transfer and analysis. Data sets grow in size by gathering logs on servers, cameras and wireless sensor networks etc. Every day approximately 2.5 quintillion bytes of data is created and nearly 90% of the data is created in last two-three years. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It is the core platform for structuring Big Data, and making it useful for solving problems related to analytic purposes. HDFS links together file systems on many local nodes to make them into one big file system. It assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes. This paper proposes a way to make a cluster on low configuration machines and uses it efficiently for data processing and storage. This implementation integrates open source tool named Sqoop for importing data from MySQL server to cluster of Hadoop and store data in distributed file systems across various nodes. The results establish that as the number of Map-tasks increases Hive import time and Loading time decreases by 20% and over 100 % respectively.. The results show that as the number of Map-tasks increase transfer rate improves by over 27% but at the cost of failed/killed tasks. It happens because as the number of tasks increase, resources start getting exhausted and tasks start getting failed or killed.
The multiscalar architecture

The centerpiece of this thesis is a new processing paradigm for exploiting instruction level parallelism. This paradigm, called the multiscalar paradigm, splits the program into many smaller tasks, and exploits fine-grain parallelism by executing multiple, possibly (control and/or data) dependent tasks in parallel using multiple processing elements. Splitting the instruction stream at statically determined boundaries allows the compiler to pass substantial information about the tasks to the hardware. The processing paradigm can be viewed as extensions of the superscalar and multiprocessing paradigms, and shares a number of properties of the sequential processing model and the dataflow processing model. The multiscalar paradigm is easily realizable, and we describe an implementation of the multiscalar paradigm, called the multiscalar processor. The central idea here is to connect multiple sequential processors, in a decoupled and decentralized manner, to achieve overall multiple issue. The multiscalar processor supports speculative execution, allows arbitrary dynamic code motion (facilitated by an efficient hardware memory disambiguation mechanism), exploits communication localities, and does all of these with hardware that is fairly straightforward to build. Other desirable aspects of the implementation include decentralization of the critical resources, absence of wide associative searches, and absence of wide interconnection/data paths.
Genetic Disorders in the United Arab Emirates , Bahrain , and Oman : Lessons Learned

Ever since its initiation, the Catalogue for Transmission Genetics in Arabs (CTGA) database has endeavored to index reports of genetic disorders among Arab patients. This is a daunting task, compounded by the fact that the Arabs not only constitute a huge population of close to 340 million people, but are also spread over 23 countries covering 14 million square km in two continents within their homeland itself. Add to this the Arab diaspora spread around the world, and a picture of truly massive proportions emerges. The CTGA Database Development Team realized earlier on that this enormous undertaking would need to be managed in a systematic manner. To this end, the Team works on the database in a targeted fashion, focusing on a single Arab country at a time. This strategy, initiated with the United Arab Emirates, has enabled the complete coverage of genetic disorders in two more countries (Kingdom of Bahrain and Sultanate of Oman), while maintaining a basal amount of information in the remaining Arab countries.
Self-Adaptive Double Bootstrapped DDPG

Deep Deterministic Policy Gradient (DDPG) algorithm has been successful for state-of-the-art performance in high-dimensional continuous control tasks. However, due to the complexity and randomness of the environment, DDPG tends to suffer from inefficient exploration and unstable training. In this work, we propose Self-Adaptive Double Bootstrapped DDPG (SOUP), an algorithm that extends DDPG to bootstrapped actor-critic architecture. SOUP improves the efficiency of exploration by multiple actor heads capturing more potential actions and multiple critic heads evaluating more reasonable Q-values collaboratively. The crux of double bootstrapped architecture is to tackle the fluctuations in performance, caused by multiple heads of spotty capacity varying throughout training. To alleviate the instability, a self-adaptive confidence mechanism is introduced to dynamically adjust the weights of bootstrapped heads and enhance the ensemble performance effectively and efficiently. We demonstrate that SOUP achieves faster learning by at least 45% while improving cumulative reward and stability substantially in comparison to vanilla DDPG on OpenAI Gym’s MuJoCo environments.