nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating sustainability transitions pathways : Bridging analytical approaches to address governance challenges

The paper sets out a proposal for bridging and linking three approaches to the analysis of transitions to sustainable and low-carbon societies: quantitative systems modelling; socio-technical transition analysis; and initiative-based learning. We argue that each of these approaches presents a partial and incomplete picture, which has implications for the quality and usefulness of the insights they can deliver for policy and practice. A framework for bridging these different approaches promises to enrich each of the approaches, while providing the basis for a more robust and complete analysis of sustainable transitions pathways that serves better to address questions and dilemmas faced by decision-makers and practitioners. We elaborate five key challenges for the analysis and governance of transitions pathways, and compare the three approaches in relation to each of these. We suggest an integration strategy based on alignment, bridging, and iteration, arguing that a structured dialogue between practitioners of different approaches is needed. In practical terms, such a dialogue would be organised around three areas of joint knowledge production: defining common analytical or governance problems to be tackled through integration; establishing shared concepts (boundary objects); and establishing operational bridging devices (data and metrics, pathways evaluation and their delivery). Such processes could include experts and societal partners. We draw conclusions about future research perspectives and the role of analysis in transitions governance.
Probability and statistics with reliability, queuing, and computer science applications

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Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing works on the problem either ignore the label-label and instance-instance correlations or just assume these correlations are linear and unstructured. Considering that semantic correlations between images are actually structured, in this paper we propose to incorporate structured semantic correlations to solve the missing label problem of multi-label learning. Specifically, we project images to the semantic space with an effective semantic descriptor. A semantic graph is then constructed on these images to capture the structured correlations between them. We utilize the semantic graph Laplacian as a smooth term in the multi-label learning formulation to incorporate the structured semantic correlations. Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations. We achieve better results than state-of-the-art multi-label learning methods on four benchmark datasets.
Women's Preferences for Penis Size: A New Research Method Using Selection among 3D Models

Women's preferences for penis size may affect men's comfort with their own bodies and may have implications for sexual health. Studies of women's penis size preferences typically have relied on their abstract ratings or selecting amongst 2D, flaccid images. This study used haptic stimuli to allow assessment of women's size recall accuracy for the first time, as well as examine their preferences for erect penis sizes in different relationship contexts. Women (N = 75) selected amongst 33, 3D models. Women recalled model size accurately using this method, although they made more errors with respect to penis length than circumference. Women preferred a penis of slightly larger circumference and length for one-time (length = 6.4 inches/16.3 cm, circumference = 5.0 inches/12.7 cm) versus long-term (length = 6.3 inches/16.0 cm, circumference = 4.8 inches/12.2 cm) sexual partners. These first estimates of erect penis size preferences using 3D models suggest women accurately recall size and prefer penises only slightly larger than average.
Modeling Missing Data in Clinical Time Series with RNNs

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children’s Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can as predictive as the results themselves.
Benchmarking State-of-the-Art Deep Learning Software Tools

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools coming to public. Training a deep network is usually a very time-consuming process. To address the huge computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training and inference time. However, different tools exhibit different features and running performance when they train different types of deep networks on different hardware platforms, making it difficult for end users to select an appropriate pair of software and hardware. In this paper, we present our attempt to benchmark several state-of-the-art GPU-accelerated deep learning software tools, including Caffe, CNTK, TensorFlow, and Torch. We focus on evaluating the running time performance (i.e., speed) of these tools with three popular types of neural networks on two representative CPU platforms and three representative GPU platforms. Our contribution is two-fold. First, for end users of deep learning software tools, our benchmarking results can serve as a reference to selecting appropriate hardware platforms and software tools. Second, for developers of deep learning software tools, our in-depth analysis points out possible future directions to further optimize the running performance.
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations

Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images. Deep-learning segmentation frameworks rely not only on the choice of network architecture but also on the choice of loss function. When the segmentation process targets rare observations, a severe class imbalance is likely to occur between candidate labels, thus resulting in sub-optimal performance. In order to mitigate this issue, strategies such as the weighted cross-entropy function, the sensitivity function or the Dice loss function, have been proposed. In this work, we investigate the behavior of these loss functions and their sensitivity to learning rate tuning in the presence of different rates of label imbalance across 2D and 3D segmentation tasks. We also propose to use the class re-balancing properties of the Generalized Dice overlap, a known metric for segmentation assessment, as a robust and accurate deep-learning loss function for unbalanced tasks.
Map building with mobile robots in dynamic environments

The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this paper we consider the problem of creating maps with mobile robots in dynamic environments. We present a new approach that interleaves mapping and localization with a probabilistic technique to identify spurious measurements. In several experiments we demonstrate that our algorithm generates accurate 2d and 3d in different kinds of dynamic indoor and outdoor environments. We also use our algorithm to isolate the dynamic objects and to generate three-dimensional representation of them.
Waveguide-Fed Cavity Backed Slot Antenna Array with high efficiency in the Ku-band

This paper reports the design of a Waveguide-Fed Cavity Backed Slot Antenna Array in Ku-band. The antenna is made entire via simple milling process. The overall antenna structure consists of 3 layers. The bottom layer is a waveguide feed network to provide corporate power division. In turn, the waveguide feed network is fed by a conventional SMA connector from the back. The waveguide network couples energy to an array of cavity via an aperture. This constitutes the middle layer. Each cavity then excites an array of 2×2 radiating slots in the top layer. The radiating slot elements and the feed network are designed to achieve wide bandwidth and gain performance. Finally, an 8×8 array antenna is designed with about 25dBi gain and bandwidth of 1.6GHz in the Ku-band.
Learning Transferable Architectures for Scalable Image Recognition

Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the "NASNet search space") which enables transferability. In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a "NASNet architecture". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.
Structure of West Nile virus.

West Nile virus (WNV) is found throughout Africa, Europe, Central Asia, and, most recently, in North America. The first outbreak in the United States was in New York City during the summer of 1999, and the virus subsequently spread across the United States (1). In 2002, there were 4156 reported cases of human illness associated with WNV and 284 deaths. WNV is a flavivirus transmitted primarily by Culex mosquitoes to vertebrate hosts. Flaviviruses include members such as dengue virus, yellow fever virus, and tick-borne encephalitis virus and are positivestrand RNA viruses that contain three structural proteins and a host-derived lipid bilayer. Two lineages of WNV (I and II) have been identified (2). All isolates that cause severe human disease fall into lineage I. With the use of cryoelectron microscopy (cryoEM) and image reconstruction techniques, we have determined a 17 Å resolution structure of WNV New York 99, the strain responsible for the outbreak in the United States (3). The virus has icosahedral symmetry and is 500 Å in diameter with no surface projections or spikes , which are prominent on other envelope-containing viruses such as influenza, HIV, and measles virus. A cross-sectional view  shows concentric layers of density indicating the multilayer organization of the virus. The outermost layer contains the highest density and corresponds to the E and M transmembrane proteins. The 35 to 40 Å thick lipid bilayer appears nonspherical with the transmembrane components of the M and E proteins visible. The nucleocapsid core contains multiple copies of the capsid protein and the genome RNA. The major surface protein E is responsible for receptor binding, host membrane fusion, and can elicit a neutralizing antibody response. Although the atomic structure of the WNV E protein is not known, structures of the ectodomains of the closely related dengue and tick-borne encephalitis virus E proteins
HENet: A Highly Efficient Convolutional Neural Networks Optimized for Accuracy, Speed and Storage

In order to enhance the real-time performance of convolutional neural networks (CNN), more and more researchers are focusing on improving the efficiency of CNN. Based on the analysis of some CNN architectures, such as ResNet, DenseNet, ShuffleNet and so on, we combined their advantages and proposed a very efficient model called Highly Efficient Networks(HENet). The new architecture uses an unusual way to combine group convolution and channel shuffle which was mentioned in ShuffleNet. Inspired by ResNet and DenseNet, we also propose a new way to use element-wise addition and concatenation connection with each block. In order to make greater use of feature maps, pooling operations are removed from HENet. The experiments show that our model’s efficiency is more than 1 times higher than ShuffleNet on many open source datasets, such as CIFAR-10/100 and SVHN.
Creep and Shrinkage Prediction Model for Analysis and Design of Concrete Structures : Model B 3 By

The present paper presents in chapter 1 a model for the characterization of concrete creep and shrinkage in design of concrete structures (Model B3), which is simpler, agrees better with the experimental data and is better theoretically justified than the previous models. The model complies with the general guidelines recently formulated by RILEM TC-107ß1. Justifications of various aspects of the model and diverse refinements are given in Chapter 2, and many simple explanations are appended in the commentary at the end of Chapter 1 (these parts do not to be read by those who merely want to apply the model). The prediction model B3 is calibrated by a computerized data bank comprising practically all the relevant test data obtained in various laboratories throughout the world. The coefficients of variation of the deviations of the model from the data are distinctly smaller than those for the latest CEB model (1990), and much smaller than those for the previous model in ACI 209 (which was developed in the mid-1960’s). The model is simpler than the previous models (BP and BPKX) developed at Northwestern University, yet it has comparable accuracy and is more rational. The effect of concrete composition and design strength on the model parameters is the main source of error of the model. A method to reduce this error by updating one or two model parameters on the basis of short-time creep tests is given. The updating of model parameters is particularly important for high-strength concretes and other special concretes containing various admixtures, superplasticizers, water-reducing agents and pozzolanic materials. For the updating of shrinkage prediction, a new method in which the shrinkage half-time is calibrated by simultaneous measurements of water loss is presented. This approach circumvents the large sensitivity of the shrinkage extrapolation problem to small changes in the material parameters. The new model allows a more realistic assessment of the creep and shrinkage effects in concrete structures, which significantly affect the durability and long-time serviceability of civil engineering infrastructure.
The neural basis of human dance.

Human dance was investigated with positron emission tomography to identify its systems-level organization. Three core aspects of dance were examined: entrainment, meter and patterned movement. Amateur dancers performed small-scale, cyclically repeated tango steps on an inclined surface to the beat of tango music, without visual guidance. Entrainment of dance steps to music, compared to self-pacing of movement, was supported by anterior cerebellar vermis. Movement to a regular, metric rhythm, compared to movement to an irregular rhythm, implicated the right putamen in the voluntary control of metric motion. Spatial navigation of leg movement during dance, when controlling for muscle contraction, activated the medial superior parietal lobule, reflecting proprioceptive and somatosensory contributions to spatial cognition in dance. Finally, additional cortical, subcortical and cerebellar regions were active at the systems level. Consistent with recent work on simpler, rhythmic, motor-sensory behaviors, these data reveal the interacting network of brain areas active during spatially patterned, bipedal, rhythmic movements that are integrated in dance.
Robot Representing and Reasoning with Knowledge from Reinforcement Learning

Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge representation and reasoning (KRR) paradigms are strong in declarative KRR tasks, but are ill-equipped to learn from such experiences. In this work, we integrate logical-probabilistic KRR with model-based RL, enabling agents to simultaneously reason with declarative knowledge and learn from interaction experiences. The knowledge from humans and RL is unified and used for dynamically computing task-specific planning models under potentially new environments. Experiments were conducted using a mobile robot working on dialog, navigation, and delivery tasks. Results show significant improvements, in comparison to existing model-based RL methods.
Applications of the pharmacokinetic/pharmacodynamic (PK/PD) analysis of antimicrobial agents.

The alarming increase of resistance against multiple currently available antibiotics is leading to a rapid lose of treatment options against infectious diseases. Since the antibiotic resistance is partially due to a misuse or abuse of the antibiotics, this situation can be reverted when improving their use. One strategy is the optimization of the antimicrobial dosing regimens. In fact, inappropriate drug choice and suboptimal dosing are two major factors that should be considered because they lead to the emergence of drug resistance and consequently, poorer clinical outcomes. Pharmacokinetic/pharmacodynamic (PK/PD) analysis in combination with Monte Carlo simulation allows to optimize dosing regimens of the antibiotic agents in order to conserve their therapeutic value. Therefore, the aim of this review is to explain the basis of the PK/PD analysis and associated techniques, and provide a brief revision of the applications of PK/PD analysis from a therapeutic point-of-view. The establishment and reevaluation of clinical breakpoints is the sticking point in antibiotic therapy as the clinical use of the antibiotics depends on them. Two methodologies are described to establish the PK/PD breakpoints, which are a big part of the clinical breakpoint setting machine. Furthermore, the main subpopulations of patients with altered characteristics that can condition the PK/PD behavior (such as critically ill, elderly, pediatric or obese patients) and therefore, the outcome of the antibiotic therapy, are reviewed. Finally, some recommendations are provided from a PK/PD point of view to enhance the efficacy of prophylaxis protocols used in surgery.
Interorganizational Cooperation: The Role of Information Technology in Empirical Comparison of US and Japanese Supplier Relations

This paper investigates the comparative role of several factors, including information technology (IT), predicting the level of cooperation between two independent organizations. Drawing upon multiple theoretical perspectives, we develop five hypotheses about the impact on interorganizational cooperation of three sets of factors: (1) the characteristics of the environment withm which the relationship operates, (2) the characteristics of the relationship itself, and (3) the characteristics of how IT is used within the relationship. Each of these conceptual constructs is operationalized and measured within the specific context of buyer-supplier relationships in the automobile industry. The hypotheses are tested across two national settings (the US and Japan) using multiple regression analyzes conducted on a data set of 447 distinct relationships. The results indicate that the use of IT and the characteristics of the environment do not play the same role in explaining interorganizational cooperation in the two country settings, while in both countries the characteristics of the relationship significantly contribute to change in 112.
Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification

Attributes, or mid-level semantic features, have gained popularity in the past few years in domains ranging from activity recognition to face verification. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes. In most works to date, attributes have been considered independent of each other. However, attributes can be strongly related, such as heavy makeup and wearing lipstick as well as male and goatee and many others. We propose a multi-task deep convolutional neural network (MCNN) with an auxiliary network at the top (AUX) which takes advantage of attribute relationships for improved classification. We call our final network MCNN-AUX. MCNNAUX uses attribute relationships in three ways: by sharing the lowest layers for all attributes, by sharing the higher layers for spatially-related attributes, and by feeding the attribute scores from MCNN into the AUX network to find score-level relationships. Using MCNN-AUX rather than individual attribute classifiers, we are able to reduce the number of parameters in the network from 64 million to fewer than 16 million and reduce the training time by a factor of 16. We demonstrate the effectiveness of our method by producing results on two challenging publicly available datasets achieving state-of-the-art performance on many attributes.