nils-herrmann/clean_scirepeval huggingface dataset:
Efficient stabilization of crop yield prediction in the Canadian Prairies

This paper describes how spatial dependence can be incorporated into statistical models for crop yield along with the dangers of ignoring it. In particular, approaches that ignore this dependence suffer in their ability to capture (and predict) the underlying phenomena. By judiciously selecting biophysically based explanatory variables and using spatially-determined prior probability distributions, a Bayesian model for crop yield is created that not only allows for increased modelling flexibility but also for improved prediction over existing least-squares methods. The model is focused on providing efficient predictions rop water stress index
SDBF: Smart DNS brute-forcer

The structure of the domain name is highly relevant for providing insights into the management, organization and operation of a given enterprise. Security assessment and network penetration testing are using information sourced from the DNS service in order to map the network, perform reconnaissance tasks, identify services and target individual hosts. Tracking the domain names used by popular Botnets is another major application that needs to undercover their underlying DNS structure. Current approaches for this purpose are limited to simplistic brute force scanning or reverse DNS, but these are unreliable. Brute force attacks depend of a huge list of known words and thus, will not work against unknown names, while reverse DNS is not always setup or properly configured. In this paper, we address the issue of fast and efficient generation of DNS names and describe practical experiences against real world large scale DNS names. Our approach is based on techniques derived from natural language modeling and leverage Markov Chain Models in order to build the first DNS scanner (SDBF) that is leveraging both, training and advanced language modeling approaches.
The Women's EMPOWER Survey: Identifying Women's Perceptions on Vulvar and Vaginal Atrophy and Its Treatment.

Vulvar and vaginal atrophy (VVA) affects up to two thirds of postmenopausal women, but most symptomatic women do not receive prescription therapy.



To evaluate postmenopausal women's perceptions of VVA and treatment options for symptoms in the Women's EMPOWER survey.



The Rose Research firm conducted an internet survey of female consumers provided by Lightspeed Global Market Insite. Women at least 45 years of age who reported symptoms of VVA and residing in the United States were recruited.



Survey results were compiled and analyzed by all women and by treatment subgroups.



Respondents (N = 1,858) had a median age of 58 years (range = 45-90). Only 7% currently used prescribed VVA therapies (local estrogen therapies or oral selective estrogen receptor modulators), whereas 18% were former users of prescribed VVA therapies, 25% used over-the-counter treatments, and 50% had never used any treatment. Many women (81%) were not aware of VVA or that it is a medical condition. Most never users (72%) had never discussed their symptoms with a health care professional (HCP). The main reason for women not to discuss their symptoms with an HCP was that they believed that VVA was just a natural part of aging and something to live with. When women spoke to an HCP about their symptoms, most (85%) initiated the discussion. Preferred sources of information were written material from the HCP's office (46%) or questionnaires to fill out before seeing the HCP (41%).The most negative attributes of hormonal products were perceived risk of systemic absorption, messiness of local creams, and the need to reuse an applicator. Overall, HCPs only recommended vaginal estrogen therapy to 23% and oral hormone therapies to 18% of women. When using vaginal estrogen therapy, less than half of women adhered to and complied with posology; only 33% to 51% of women were very to extremely satisfied with their efficacy.



The Women's EMPOWER survey showed that VVA continues to be an under-recognized and under-treated condition, despite recent educational initiatives. A disconnect in education, communication, and information between HCPs and their menopausal patients remains prevalent. Kingsberg S, Krychman M, Graham S, et al. The Women's EMPOWER Survey: Identifying Women's Perceptions on Vulvar and Vaginal Atrophy and Its Treatment.  2017;14:413-424.
Forcing Neurocontrollers to Exploit Sensory Symmetry Through Hard-wired Modularity in the Game of Cellz

Several attempts have been made in the past to construct encoding schemes that allow modularity to emerge in evolving systems, but success is limited. We believe that in order to create successful and scalable encodings for emerging modularity, we first need to explore the benefits of different types of modularity by hard-wiring these into evolvable systems. In this paper we explore different ways of exploiting sensory symmetry inherent in the agent in the simple game Cellz by evolving symmetrically identical modules. It is concluded that significant increases in both speed of evolution and final fitness can be achieved relative to monolithic controllers. Furthermore, we show that simple function approximation task that exhibits sensory symmetry can be used as a quick approximate measure of the utility of an encoding scheme for the more complex game-playing task.
DARPA communicator evaluation: progress from 2000 to 2001

This paper describes the evaluation methodology and results of the DARPA Communicator spoken dialog system evaluation experiments in 2000 and 2001. Nine spoken dialog systems in the travel planning domain participated in the experiments resulting in a total corpus of 1904 dialogs. We describe and compare the experimental design of the 2000 and 2001 DARPA evaluations. We describe how we established a performance baseline in 2001 for complex tasks. We present our overall approach to data collection, the metrics collected, and the application of PARADISE to these data sets. We compare the results we achieved in 2000 for a number of core metrics with those for 2001. These results demonstrate large performance improvements from 2000 to 2001 and show that the Communicator program goal of conversational interaction for complex tasks has been achieved.
Continuous-flow cold therapy for outpatient anterior cruciate ligament reconstruction.

This prospective, randomized study evaluated continuous-flow cold therapy for postoperative pain in outpatient arthroscopic anterior cruciate ligament (ACL) reconstructions. In group 1, cold therapy was constant for 3 days then as needed in days 4 through 7. Group 2 had no cold therapy. Evaluations and diaries were kept at 1, 2, and 8 hours after surgery, and then daily. Pain was assessed using the VAS and Likert scales. There were 51 cold and 49 noncold patients included. Continuous passive movement (CPM) use averaged 54 hours for cold and 41 hours for noncold groups (P=.003). Prone hangs were done for 192 minutes in the cold group and 151 minutes in the noncold group. Motion at 1 week averaged 5/88 for the cold group and 5/79 the noncold group. The noncold group average visual analog scale (VAS) pain and Likert pain scores were always greater than the cold group. The noncold group average Vicodin use (Knoll, Mt. Olive, NJ) was always greater than the cold group use (P=.001). Continuous-flow cold therapy lowered VAS and Likert scores, reduced Vicodin use, increased prone hangs, CPM, and knee flexion. Continuous-flow cold therapy is safe and effective for outpatient ACL reconstruction reducing pain medication requirements.
Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning

Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by incorporating deep neural networks in learning representations from the input to RL. However, the conventional deep neural network architecture is limited in learning representations for multi-task RL (MT-RL), as multiple tasks can refer to different kinds of representations. In this paper, we thus propose a novel deep neural network architecture, namely generalization tower network (GTN), which can achieve MT-RL within a single learned model. Specifically, the architecture of GTN is composed of both horizontal and vertical streams. In our GTN architecture, horizontal streams are used to learn representation shared in similar tasks. In contrast, the vertical streams are introduced to be more suitable for handling diverse tasks, which encodes hierarchical shared knowledge of these tasks. The effectiveness of the introduced vertical stream is validated by experimental results. Experimental results further verify that our GTN architecture is able to advance the state-of-the-art MT-RL, via being tested on 51 Atari games.
Uterine rupture in pregnancies following myomectomy: A multicenter case series

The purpose of this case series was to retrospectively examine records of cases with uterine rupture in pregnancies following myomectomy and to describe the clinical features and pregnancy outcomes.



This study was conducted as a multicenter case series. The patient databases at 7 tertiary hospitals were queried. Records of patients with a diagnosis of uterine rupture in the pregnancy following myomectomy between January 2012 and December 2014 were retrospectively collected. The uterine rupture cases enrolled in this study were defined as follows: through-and-through uterine rupture or tear of the uterine muscle and serosa, occurrence from 24+0 to 41+6 weeks' gestation, singleton pregnancy, and previous laparoscopic myomectomy (LSM) or laparotomic myomectomy (LTM) status.



Fourteen pregnant women experienced uterine rupture during their pregnancy after LSM or LTM. Preterm delivery of less than 34 weeks' gestation occurred in 5 cases, while intrauterine fetal death occurred in 3, and 3 cases had fetal distress. Of the 14 uterine rupture cases, none occurred during labor. All mothers survived and had no sequelae, unlike the perinatal outcomes, although they were receiving blood transfusion or treatment for uterine artery embolization because of uterine atony or massive hemorrhage.



In women of childbearing age who are scheduled to undergo LTM or LSM, the potential risk of uterine rupture on subsequent pregnancy should be explained before surgery. Pregnancy in women after myomectomy should be carefully observed, and they should be adequately counseled during this period.
Voltage Level Shifters – Review

A review on various CMOS voltage level shifters is presented in this paper. A voltage level-shifter shifts the level of input voltage to desired output voltage. Voltage Level Shifter circuits are compared with respect to output voltage level, power consumption and delay. Systems often require voltage level translation devices to allow interfacing between integrated circuit devices built from different voltage technologies. The choice of the proper voltage level translation device depends on many factors and will affect the performance and efficiency of the circuit application.
Dendral and Meta-Dendral: Their Applications Dimension

The DENDRAL and Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization ofscientific reasoningand theformalization of scientific knowledge for this purpose. An early motivation for our work was to explore the power of existing AI methods, such as heuristic search, for reasoning in difficult scientific problems [7]. Another concern has been to exploit the AI methodology to understand better some fundamental questions in the philosophy of science, for example the processes by which explanatory hypotheses are discovered or judged adequate [18]. From the start, the project has had an applications dimension [9, 10, 27]. It has sought to develop "expert level" agents to assist in the solution ofproblems in their discipline that require complex symbolic reasoning. The applications dimension is the focus of this paper. In order to achieve high performance, the DENDRAL programs incorporate large amounts ofknowledge about the area of science to which they are applied, structure elucidation in organic chemistry. A "smart assistant" for a chemist needs tobe able toperform many tasks as well as an expert, but need not necessarily understand the domain at the same theoretical level as the expert. The over-all structure elucidation task is described below (Section 2) followed by a description of the role of the DENDRAL programs within that framework (Section 3). The Meta-DENDRAL programs (Section 4) use a weaker body of knowledge about the domain ofmass spectrometry because their task is to formulate rules of mass spectrometry by induction from empirical data. A strong model of the domain would bias therules unnecessarily.
Multiple Classifier System for Remote Sensing Image Classification: A Review

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.
Loss, Restoration, and Maintenance of Plasma Membrane Integrity

A break in the integrity of the plasma membrane immediately compromises this structure’s essential role as a barrier, and this can kill the affected cell. Yet animal cell plasma membranes, unprotected by a cell wall, are highly vulnerable to mechanically induced disruption. Moreover, many tissue environments generate and receive “physiological” levels of mechanical force that impose shear, tensile, and compressive stresses on constituent cells. We examine here the tissue conditions that lead to membrane disruptions and the mechanisms that cells use for preventing disruption-induced death and for rapidly restoring membrane integrity. Mechanical stress also induces an adaptive response by cells, which must sense and respond to this stimulus. A skeletal muscle, for example, experiences during its lifetime a highly variable degree of mechanical load. Its individual myofibers must adapt to this changing mechanical environment, hypertrophying in response to increased load and atrophying in response to decreased load. Such changes in tissue architecture are important because they improve mechanical functioning, make economical use of valuable resources, repair or replace injured components, and/or prevent future injury. We here review briefly what is known about the role of plasma membrane disruption in transducing cell responses to mechanical load.
Reduced compartmental models of neocortical pyramidal cells

Model neurons composed of hundreds of compartments are currently used for studying phenomena at the level of the single cell. Large network simulations require a simplified model of a single neuron that retains the electrotonic and synaptic integrative properties of the real cell. We introduce a method for reducing the number of compartments of neocortical pyramidal neuron models (from 400 to 8-9 compartments) through a simple collapsing method based on conserving the axial resistance rather than on the surface area of the dendritic tree. The reduced models retain the general morphology of the pyramidal cells on which they are based, allowing accurate positioning of synaptic inputs and ionic conductances on individual model cells, as well as construction of spatially accurate network models. The reduced models run significantly faster than the full models, yet faithfully reproduce their electrical responses.
Genetic algorithms for evolving deep neural networks

In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and propose a GA-assisted method for deep learning. Our experimental results indicate that this GA-assisted approach improves the performance of a deep autoencoder, producing a sparser neural network.
Real-Time Urban Microclimate Analysis Using Internet of Things

Real-time environment monitoring and analysis is an important research area of Internet of Things (IoT). Understanding the behavior of the complex ecosystem requires analysis of detailed observations of an environment over a range of different conditions. One such example in urban areas includes the study of tree canopy cover over the microclimate environment using heterogeneous sensor data. There are several challenges that need to be addressed, such as obtaining reliable and detailed observations over monitoring area, detecting unusual events from data, and visualizing events in real-time in a way that is easily understandable by the end users (e.g., city councils). In this regard, we propose an integrated geovisualization framework, built for real-time wireless sensor network data on the synergy of computational intelligence and visual methods, to analyze complex patterns of urban microclimate. A Bayesian maximum entropy-based method and a hyperellipsoidal model-based algorithm have been build in our integrated framework to address above challenges. The proposed integrated framework was verified using the dataset from an indoor and two outdoor network of IoT devices deployed at two strategically selected locations in Melbourne, Australia. The data from these deployments are used for evaluation and demonstration of these components’ functionality along with the designed interactive visualization components.
Combining Taxonomies using Word2vec

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.
A Future for Software Engineering?

This paper suggests the need for a software engineering research community conversation about the future that the community would like to have. The paper observes that the research directions the community has taken in the past, dating at least back to the formative NATO Conferences in the late 1960's, have been driven largely by desire to meet the needs of practice. The paper suggests that the community should discuss whether it is now appropriate to balance this problem-solving-oriented research with a stronger complement of curiosity-driven research. This paper does not advocate what that balance should be. Neither does it advocate what curiosity driven research topics should be pursued (although illustrative examples are offered). It does does advocate the need for a community conversation about these questions.
An unusual case of transient neonatal pustular melanosis: a diagnostic puzzle

A newborn's skin may exhibit a variety of changes during the first weeks of life, and rashes are extremely common in the neonatal period, representing a significant source of parental concern. In particular, a variety of skin eruptions can present as pustules. Most of them are innocuous and self-limiting, while others can be the manifestation of an infectious disease or even indicative of serious underlying disorders. Transient neonatal pustular melanosis is an uncommon vesiculopustular rash characterized by small pustules on a non-erythematous base, noted at birth or during the first day of life, without systemic symptoms. The lesions rupture spontaneously, leaving hyperpigmented macules that usually fade within few weeks. Clinical recognition of this disease can help physicians avoid unnecessary diagnostic testing and treatment for infectious etiologies because no specific therapy is recommended. The clinical aspect and time of onset are generally sufficient to make the correct diagnosis. Nevertheless, peculiar clinical presentations may require additional work-up to rule out life-threatening conditions, and dermatological consultation and histological examination are required for the final diagnosis.  We report an exceedingly unusual presentation of transient neonatal pustular melanosis, suggesting the importance of a systematic diagnostic approach to allow a confident recognition of this benign condition.
Localization of the PIP2 sensor of TRPV1 ion channels.

Although a large number of ion channels are now believed to be regulated by phosphoinositides, particularly phosphoinositide 4,5-bisphosphate (PIP2), the mechanisms involved in phosphoinositide regulation are unclear. For the TRP superfamily of ion channels, the role and mechanism of PIP2 modulation has been especially difficult to resolve. Outstanding questions include: is PIP2 the endogenous regulatory lipid; does PIP2 potentiate all TRPs or are some TRPs inhibited by PIP2; where does PIP2 interact with TRP channels; and is the mechanism of modulation conserved among disparate subfamilies? We first addressed whether the PIP2 sensor resides within the primary sequence of the channel itself, or, as recently proposed, within an accessory integral membrane protein called Pirt. Here we show that Pirt does not alter the phosphoinositide sensitivity of TRPV1 in HEK-293 cells, that there is no FRET between TRPV1 and Pirt, and that dissociated dorsal root ganglion neurons from Pirt knock-out mice have an apparent affinity for PIP2 indistinguishable from that of their wild-type littermates. We followed by focusing on the role of the C terminus of TRPV1 in sensing PIP2. Here, we show that the distal C-terminal region is not required for PIP2 regulation, as PIP2 activation remains intact in channels in which the distal C-terminal has been truncated. Furthermore, we used a novel in vitro binding assay to demonstrate that the proximal C-terminal region of TRPV1 is sufficient for PIP2 binding. Together, our data suggest that the proximal C-terminal region of TRPV1 can interact directly with PIP2 and may play a key role in PIP2 regulation of the channel.