nils-herrmann/clean_scirepeval huggingface dataset:
Empirical Power Estimation for Phase I Dose Proportionality Studies Based on Power-Law Model Using Confidence Interval Criteria

A Phase I dose-proportionality study is an essential tool to understand drug pharmacokinetic dose-response relationship in early clinical development. There are a number of different approaches to the assessment of dose proportionality. The confidence interval (CI) criteria approach, a staitistically sound and clinically relevant approach, has been proposed to detect dose-proportionality (Smith, et al. 2000), by which the proportionality is declared if the 90% CI for slope is completely contained within the pre-determined critical interval. This method, enhancing the information from a clinical dose-proportionality study, has gradually drawn attention. However, exact power calculation of dose proportinality studies based on CI criteria poses difficulity for practioners since the methodology was essentailly from two one-sided tests (TOST) procedure for the slope, which should be unit under proportionality. It requires sophisticated numerical integration, and it is not available in statistical software packages. This paper presents a SAS Macro to compute the empirical power for the CI-based dose proportinality studies. The resulting sample sizes and corresponding empirical powers suggest that this approach is powerful in detecting dose-proportionality under commonly used sample sizes for phase I studies.
A study of Java's non-Java memory

A Java application sometimes raises an out-of-memory ex-ception. This is usually because it has exhausted the Java heap. However, a Java application can raise an out-of-memory exception when it exhausts the memory used by Java that is not in the Java heap. We call this area non-Java memory. For example, an out-of-memory exception in the non-Java memory can happen when the JVM attempts to load too many classes. Although it is relatively rare to ex-haust the non-Java memory compared to exhausting the Java heap, a Java application can consume a considerable amount of non-Java memory.
 This paper presents a quantitative analysis of non-Java memory. To the best of our knowledge, this is the first in-depth analysis of the non-Java memory. To do this we cre-ated a tool called Memory Analyzer for Redundant, Unused, and String Areas (MARUSA), which gathers memory statis-tics from both the OS and the Java virtual machine, break-ing down and visualizing the non-Java memory usage.
 We studied the use of non-Java memory for a wide range of Java applications, including the DaCapo benchmarks and Apache DayTrader. Our study is based on the IBM J9 Java Virtual Machine for Linux. Although some of our results may be specific to this combination, we believe that most of our observations are applicable to other platforms as well.
An optimisation-based path planner for truck-trailer systems with driving direction changes

This paper presents a path planning concept for trucks with trailers with kingpin hitching. This system is nonholonomic, has no flat output and is not stable in backwards driving direction. These properties are major challenges for path planning. The presented approach concentrates on the loading bay scenario. The considered task is to plan a path for the truck-trailer system from a start to a specified target configuration corresponding to the loading bay. Thereby, close distances to obstacles and multiple driving direction changes have to be handled. Furthermore, a so-called jackknife position has to be avoided. In a first step, an initial path is planned from the target to the start configuration using a tree-based path planner. Afterwards this path is refined locally by solving an optimal control problem. Due to the local nature of the planner, heuristic rules for direction changes are formulated. The performance of the proposed path planner is evaluated in simulation studies.
Analysis of Diode Reverse Recovery Effect on the Improvement of Soft-Switching Range in Zero-Voltage-Transition Bidirectional Converters

In this paper, a prevalent type of zero-voltage- transition bidirectional converters is analyzed with the inclusion of the reverse recovery effect of the diodes. The main drawback of this type is missing the soft-switching condition of the main switches at operating duty cycles smaller than 0.5. As a result, soft-switching condition would be lost in one of the bidirectional converter operating modes (forward or reverse modes) since the duty cycles of the forward and reverse modes are complement of each other. Analysis shows that the rectifying diode reverse recovery would assist in providing the soft-switching condition for the duty cycles below 0.5, which is done by a proper design of the snubber capacitor and with no limitation on the rectifying diode current rate at turn-off. Hence, the problems associated with the soft-switching range and the reverse recovery of the rectifying diode are solved simultaneously, and soft-switching condition for both operating modes of the bidirectional converter is achieved with no extra auxiliary components and no complex control. The theoretical analysis for a bidirectional buck and boost converter is presented in detail, and the validity of the theoretical analysis is justified using the experimental results of a 250-W 135- to 200-V prototype converter.
Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.
Semi-Supervised learning using adversarial networks

Semi-supervised learning is a topic of practical importance because of the difficulty of obtaining numerous labeled data. In this paper, we apply an extension of adversarial autoencoder to semi-supervised learning tasks. In attempt to separate style and content, we divide the latent representation of the autoencoder into two parts. We regularize the autoencoder by imposing a prior distribution on both parts to make them independent. As a result, one of the latent representations is associated with content, which is useful to classify the images. We demonstrate that our method disentangles style and content of the input images and achieves less test error rate than vanilla autoencoder on MNIST semi-supervised classification tasks.
Measuring Endorsement of the New Ecological Paradigm : A Revised NEP Scale

Dunlap and Van Liere’s New Environmental Paradigm (NEP) Scale, published in 1978, has become a widely used measure of proenvironmental orientation. This article develops a revised NEP Scale designed to improve upon the original one in several respects: (1) It taps a wider range of facets of an ecological worldview, (2) It offers a balanced set of proand anti-NEP items, and (3) It avoids outmoded terminology. The new scale, termed the New Ecological Paradigm Scale, consists of 15 items. Results of a 1990 Washington State survey suggest that the items can be treated as an internally consistent summated rating scale and also indicate a modest growth in pro-NEP responses among Washington residents over the 14 years since the original study.
Performance evaluation of Low Density Spreading Multiple Access

In this paper, we evaluate the performance of Multicarrier-Low Density Spreading Multiple Access (MC-LDSMA) as a multiple access technique for mobile communication systems. The MC-LDSMA technique is compared with current multiple access techniques, OFDMA and SC-FDMA. The performance is evaluated in terms of cubic metric, block error rate, spectral efficiency and fairness. The aim is to investigate the expected gains of using MC-LDSMA in the uplink for next generation cellular systems. The simulation results of the link and system-level performance evaluation show that MC-LDSMA has significant performance improvements over SC-FDMA and OFDMA. It is shown that using MC-LDSMA can considerably reduce the required transmission power and increase the spectral efficiency and fairness among the users.
Sensorless Control for SPMSM With Concentrated Windings Using Multisignal Injection Method

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is a high-performance drive machine and has been adopted in many applications. The difficulty of implementing its sensorless control at low and zero speeds is its multiple saliencies, which is much more significant than most other ac machines. The traditional decoupling methods provide successful results only under the condition that high-order saliencies are not stronger than half of the primary saliency. Furthermore, the behavior of the multiple saliencies is principally frequency dependent. Based on the characteristics of such machines, this paper proposes a multisignal injection method for realizing sensorless control. This method injects multiple high-frequency signals with different frequencies and magnitudes into the machine. Different frequency components in the response current signals are demodulated and then combined together to get the clear primary saliency signal, which is used to identify the rotor position. This new method was validated using a cwSPMSM at low speed. The experimental results proved the effectiveness and accuracy of the new method.
Can developer-module networks predict failures?

Software teams should follow a well defined goal and keep their work focused. Work fragmentation is bad for efficiency and quality. In this paper we empirically investigate the relationship between the fragmentation of developer contributions and the number of post-release failures. Our approach is to represent developer contributions with a developer-module network that we call contribution network. We use network centrality measures to measure the degree of fragmentation of developer contributions. Fragmentation is determined by the centrality of software modules in the contribution network. Our claim is that central software modules are more likely to be failure-prone than modules located in surrounding areas of the network. We analyze this hypothesis by exploring the network centrality of Microsoft Windows Vista binaries using several network centrality measures as well as linear and logistic regression analysis. In particular, we investigate which centrality measures are significant to predict the probability and number of post-release failures. Results of our experiments show that central modules are more failure-prone than modules located in surrounding areas of the network. Results further confirm that number of authors and number of commits are significant predictors for the probability of post-release failures. For predicting the number of post-release failures the closeness centrality measure is most significant.
Evaluation of shearwave elastography for the characterisation of focal liver lesions on ultrasound

To determine the elasticity characteristics of focal liver lesions (FLLs) by shearwave elastography (SWE). We used SWE in 108 patients with 161 FLLs and in the adjacent liver for quantitative and qualitative FLLs stiffness assessment. The Mann–Whitney test was used to assess the difference between the groups of lesions where a P value less than 0.05 was considered significant. SWE acquisitions failed in 22 nodules (14 %) in 13 patients. For the 139 lesions successfully evaluated, SWE values were (in kPa), for the 3 focal fatty sparings (FFS) 6.6 ± 0.3, for the 10 adenomas 9.4 ± 4.3, for the 22 haemangiomas 13.8 ± −5.5, for the 16 focal nodular hyperplasias (FNHs) 33 ± −14.7, for the 2 scars 53.7 ± 4.7, for the 26 HCCs 14.86 ± 10, for the 53 metastasis 28.8 ± 16, and for the 7 cholangiocarcinomas 56.9 ± 25.6. FNHs had significant differences in stiffness compared with adenomas (P = 0.0002). Fifty percent of the FNHs had a radial pattern of elevated elasticity. A significant difference was also found between HCCs and cholangiocarcinomas elasticity (P = 0.0004). SWE could be useful in differentiating FNHs and adenomas, or HCCs and cholangiocarcinomas by ultrasound. • Elastography is becoming quite widely used as an adjunct to conventional ultrasound • Shearwave elastography (SWE) could help differentiate adenomas from fibrous nodular hyperplasia • SWE could also be helpful in distinguishing between hepatocellular carcinomas and cholangiocarcinomas • SWE could improve the identify hepatocellular carcinomas in cirrhotic livers
Altitude Training: Strong Bounds for Single-Layer Dropout

Dropout training, originally designed for deep neural networks, has been successful on high-dimensional single-layer natural language tasks. This paper proposes a theoretical explanation for this phenomenon: we show that, under a generative Poisson topic model with long documents, dropout training improves the exponent in the generalization bound for empirical risk minimization. Dropout achieves this gain much like a marathon runner who practices at altitude: once a classifier learns to perform reasonably well on training examples that have been artificially corrupted by dropout, it will do very well on the uncorrupted test set. We also show that, under similar conditions, dropout preserves the Bayes decision boundary and should therefore induce minimal bias in high dimensions.
Mammalian Rho GTPases: new insights into their functions from in vivo studies

Rho GTPases are key regulators of cytoskeletal dynamics and affect many cellular processes, including cell polarity, migration, vesicle trafficking and cytokinesis. These proteins are conserved from plants and yeast to mammals, and function by interacting with and stimulating various downstream targets, including actin nucleators, protein kinases and phospholipases. The roles of Rho GTPases have been extensively studied in different mammalian cell types using mainly dominant negative and constitutively active mutants. The recent availability of knockout mice for several members of the Rho family reveals new information about their roles in signalling to the cytoskeleton and in development.
THE FALL AND RISE OF STRATEGIC PLANNING1: A META-ANALYSIS OF THE ENVIRONMENT-PLANNING-OUTCOME LINK

In his Harvard Business Review article in 1994, Henry Mintzberg coined strategic planning as the mere programming, i.e. the calculation of a plan. As such “strategic planning isn’t strategic thinking”, he confined (p 107). Indeed, strategic planning may actually spoil any creative strategic thinking and, hence, the possibility to create a competitive advantage. He condemned that planning itself has decreased the commitment of executives and created a disadvantageous atmosphere spit with formalized instead of creative thinking. Now, two decades later, calculating plans has become in vogue again. Since Bigdata has entered the business world, it has been celebrated as a savior in an increasing complex economic environment. In this vein, Mintzberg’s accuse has become more relevant than ever. Especially because of the globalization and internationalization of markets, companies are increasingly facing challenging business environments. Today’s markets are characterized by fierce competition, rapid changes and increasing uncertainty and complexity where Bigdata is becoming a central determinant for executives to decide on their strategic plan. Therefore, we ask: Does planning actually create a competitive advantage? What are the social and operational outcomes of strategic planning? Which environment actually favors the planning of strategies?
Beyond Relevance: Adapting Exploration/Exploitation in Information Retrieval

We present a novel adaptation technique for search engines to better support information-seeking activities that include both lookup and exploratory tasks. Building on previous findings, we describe (1) a classifier that recognizes task type (lookup vs. exploratory) as a user is searching and (2) a reinforcement learning based search engine that adapts accordingly the balance of exploration/exploitation in ranking the documents. This allows supporting both task types surreptitiously without changing the familiar list-based interface. Search results include more diverse results when users are exploring and more precise results for lookup tasks. Users found more useful results in exploratory tasks when compared to a base-line system, which is specifically tuned for lookup tasks.
Esthetic Concerns and Acceptability of Treatment Modalities in Primary Teeth: A Comparison between Children and Their Parents

Esthetic concerns in primary teeth have been studied mainly from the point of view of parents. The aim of this study was to study compare the opinions of children aged 5-8 years to have an opinion regarding the changes in appearance of their teeth due to dental caries and the materials used to restore those teeth.  A total of 107 children and both of their parents (n = 321), who were seeking dental treatment, were included in this study. A tool comprising a questionnaire and pictures of carious lesions and their treatment arranged in the form of a presentation was validated and tested on 20 children and their parents. The validated tool was then tested on all participants.  Children had acceptable validity statistics for the tool suggesting that they were able to make informed decisions regarding esthetic restorations. There was no difference between the responses of the children and their parents on most points. Zirconia crowns appeared to be the most acceptable full coverage restoration for primary anterior teeth among both children and their parents.  Within the limitations of the study it can be concluded that children in their sixth year of life are capable of appreciating the esthetics of the restorations for their anterior teeth.
A crossroads between innate and adaptive responses

Considerados como la base evolutiva vertebrada tras su radiación adaptativa en el Devónico, los peces constituyen en la actualidad el grupo más exitoso y diversificado de vertebrados. Como grupo, este conjunto heterogéneo de organismos representa una aparente encrucijada entre la respuesta inmunitaria innata y la aparición de una respuesta inmunitaria adaptativa. La mayoría de órganos inmunitarios de los mamíferos tienen sus homólogos en los peces. Sin embargo, su eventual menor complejidad estructural podría potencialmente limitar la capacidad para generar una respuesta inmunitaria completamente funcional frente a la invasión de patógenos. Se discute aquí la capacidad de los peces para generar respuestas inmunitarias exitosas, teniendo en cuenta la robustez aparente de la respuesta innata de los peces, en comparación con la observada en vertebrados superiores.
The relationship between entrepreneurship and economic development : is it U-shaped ?

Following a centuries-long decline in the rate of self-employment, a discontinuity in this downward trend is observed for many advanced economies starting in the 1970s and 1980s. In some countries the rate of self-employment appears to increase. At the same time, cross-sectional analysis shows a U-shaped relationship between start-up rates of enterprise and levels of economic development. We provide an overview of the empirical evidence concerning the relationship between independent entrepreneurship, also known as self-employment or business ownership, and economic development. We argue that the reemergence of independent entrepreneurship is based on at least two ‘revolutions’. If we distinguish between solo selfemployed at the lower end of the entrepreneurship spectrum, and ambitious and/or innovative entrepreneurs at the upper end, many advanced economies show a revival at both extremes. Policymakers in advanced economies should be aware of both revolutions and tailor their policies accordingly.
Deep Convolutional Neural Networks and Noisy Images

The presence of noise represent a relevant issue in image feature extraction and classification. In deep learning, representation is learned directly from the data and, therefore, the classification model is influenced by the quality of the input. However, the ability of deep convolutional neural networks to deal with images that have a different quality when compare to those used to train the network is still to be fully understood. In this paper, we evaluate the generalization of models learned by different networks using noisy images. Our results show that noise cause the classification problem to become harder. However, when image quality is prone to variations after deployment, it might be advantageous to employ models learned using noisy data.
Reproducing standard SCME channel models for massive MIMO base station radiated testing

Massive MIMO is a multi-user technology, where radio base stations (BSs) are equipped with a large number of antennas to simultaneously serve many terminals in the same time-frequency resource. Performance evaluation of such large-scale antenna systems in the design and development stage is challenging. In this paper, we propose to evaluate massive MIMO BSs with a sectorized multi-probe anechoic chamber (MPAC) setup. A sectorized MPAC setup with 16 probe antennas distributed uniformly within [−60°, 60°] in azimuth domain is utilized to reproduce target channel models. A 8 × 8 and a 16 × 16 uniform planar array at 3.5 GHz are selected as the BS under evaluation, respectively. Radio channel emulation accuracies in terms of power-angular spectrum, spatial correlation and beamforming pattern are investigated for the proposed MPAC setup and desired channel models.