nils-herrmann/clean_scirepeval huggingface dataset:
Safe robot navigation using an omnidirectional camera

The paper presents a safe robot navigation system based on omnidirectional vision. The 360 degree camera images are analyzed for obstacle detection and avoidance and of course for navigating safely in the given indoor environment. This module can process images in real time and extracts the direction and distance information of the obstacles from the camera system mounted on the robot. This two data is the output of the module. Because of the distortions of the omnidirectional vision, it is necessary to calibrate the camera and not only for that but also to get the right direction and distances information. Several image processing methods and technics were used which are investigated in the rest of this paper.
On a generalized Levinthal's paradox: The role of long- and short range interactions in complex bio-molecular reactions, including protein and DNA folding.

The current protein folding literature is reviewed. Two main approaches to the problem of folding were selected for this review: geometrical and biophysical. The geometrical approach allows the formulation of topological restrictions on folding, that are usually not taken into account in the construction of physical models. In particular, the topological constraints do not allow the known funnel-like energy landscape modeling, although most common methods of resolving the paradox are based on this method. The very paradox is based on the fact that complex molecules must reach their native conformations (complexes that result from reactions) in an exponentially long time, which clearly contradicts the observed experimental data. In this respect we considered the complexity of the reactions between ligands and proteins. On this general basis, the folding-reaction paradox was reformulated and generalized. We conclude that prospects for solving the paradox should be associated with incorporating a topology aspect in biophysical models of protein folding, through the construction of hybrid models. However, such models should explicitly include long-range force fields and local cell biological conditions, such as structured water complexes and photon/phonon/soliton waves, ordered in discrete frequency bands. In this framework, collective and coherent oscillations in, and between, macromolecules are instrumental in inducing intra- and intercellular resonance, serving as an integral guiding network of life communication: the electrome aspect of the cell. Yet, to identify the actual mechanisms underlying the bonds between molecules (atoms), it will be necessary to perform dedicated experiments to more definitely solve the particular time paradox.
Safe positioning for robotic-assisted laparoscopic prostatectomy.

Robotic-assisted laparoscopic prostatectomy is a surgical procedure performed to eradicate prostate cancer. Use of robotic assistance technology allows smaller incisions than the traditional laparoscopic approach and results in better patient outcomes, such as less blood loss, less pain, shorter hospital stays, and better postoperative potency and continence rates. This surgical approach creates unique challenges in patient positioning for the perioperative team because the patient is placed in the lithotomy with steep Trendelenburg position. Incorrect positioning can lead to nerve damage, pressure ulcers, and other complications. Using a special beanbag positioning device made specifically for use with this severe position helps prevent these complications.
Ready for what? An exploration of the meaning of new graduate nurses' readiness for practice.

Dialogue continues on the "readiness" of new graduates for practice despite significant advancements in the foundational educational preparation for nurses. In this paper, the findings from an exploratory study about the meaning of new graduate "readiness" for practice are reported. Data was collected during focus group interviews with one-hundred and fifty nurses and new graduates. Themes were generated using content analysis. Our findings point to agreement about the meaning of new graduate nurses' readiness for practice as having a generalist foundation and some job specific capabilities, providing safe client care, keeping up with the current realities of nursing practice, being well equipped with the tools needed to adapt to the future needs of clients, and possessing a balance of doing, knowing, and thinking. The findings from this exploratory study have implications for policies and programs targeted towards new graduate nurses entering practice.
Data Augmentation Generative Adversarial Networks

Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In our experiments we can see over 13% increase in accuracy in the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).
Anonymous Data Sharing Scheme in Public Cloud and Its Application in E-Health Record

In the past few years, cloud computing develops very quickly. A large amount of data are uploaded and stored in remote public cloud servers which cannot fully be trusted by users. Especially, more and more enterprises would like to manage their data by the aid of the cloud servers. However, when the data outsourced in the cloud are sensitive, the challenges of security and privacy becomes urgent for wide deployment of the cloud systems. This paper proposes a secure data sharing scheme to ensure the privacy of data owner and the security of the outsourced cloud data. The proposed scheme provides flexible utility of data while solving the privacy and security challenges for data sharing. The security and efficiency analysis demonstrate that the designed scheme is feasible and efficient. At last, we discuss its application in electronic health record.
Skull base chondrosarcoma: evidence-based treatment paradigms.

Chondrosarcomas are indolent but invasive chondroid malignancies that can form in the skull base. Standard management of chondrosarcoma involves surgical resection and adjuvant radiation therapy. This review evaluates evidence from the literature to assess the importance of the surgical approach and extent of resection on outcomes for patients with skull base chondrosarcoma. Also evaluated is the ability of the multiple modalities of radiation therapy, such as conventional fractionated radiotherapy, proton beam, and stereotactic radiosurgery, to control tumor growth. Finally, emerging therapies for the treatment of skull-base chondrosarcoma are discussed.
PatternNet and PatternLRP - Improving the interpretability of neural networks

Deep learning has significantly advanced the state of the art in machine learning. However, neural networks are often considered black boxes. There is significant effort to develop techniques that explain a classifier’s decisions. Although some of these approaches have resulted in compelling visualisations, there is a lack of theory of what is actually explained. Here we present an analysis of these methods and formulate a quality criterion for explanation methods. On this ground, we propose an improved method that may serve as an extension for existing backprojection and decomposition techniques.
2-Litre polyethylene glycol-citrate-simethicone plus bisacodyl versus 4-litre polyethylene glycol as preparation for colonoscopy in chronic constipation.

Chronic constipation is a risk factor of inadequate bowel preparation for colonoscopy; however, no large clinical trials have been performed in this subgroup of patients.



To compare bowel cleansing efficacy, tolerability and acceptability of 2-L polyethylene-glycol-citrate-simethicone (PEG-CS) plus 2-day bisacodyl (reinforced regimen) vs. 4-L PEG in patients with chronic constipation undergoing colonoscopy.



Randomized, observer-blind, parallel group study. Adult outpatients undergoing colonoscopy were randomly allocated to 2-L PEG-CS/bisacodyl or 4-L PEG, taken as split regimens before colonoscopy. Quality of bowel preparation was assessed by the Ottawa Bowel Cleansing Scale (OBCS). The amount of foam/bubble interfering with colonic visualization was also measured.



400 patients were enrolled. There was no significant difference in successful cleansing (OBCS score ≤6): 80.2% in the 2-L PEG-CS/bisacodyl vs. 81.4% in the 4-L PEG group. Significantly more patients taking 2L PEG-CS/bisacodyl showed no or minimal foam/bubbles in all colonic segments (80% vs. 63%; p<0.001). 2-L PEG-CS/bisacodyl was significantly more acceptable for ease of administration (p<0.001), willingness to repeat (p<0.001) and showed better compliance (p=0.002).



Split 2-L PEG-CS plus bisacodyl was not superior to split 4-L PEG for colonoscopy bowel cleansing in patients with chronic constipation; however, it performed better than the standard regimen in terms of colonic mucosa visualization, patient acceptance and compliance.
Cyber–Physical Security of a Smart Grid Infrastructure

It is often appealing to assume that existing solutions can be directly applied to emerging engineering domains. Unfortunately, careful investigation of the unique challenges presented by new domains exposes its idiosyncrasies, thus often requiring new approaches and solutions. In this paper, we argue that the “smart” grid, replacing its incredibly successful and reliable predecessor, poses a series of new security challenges, among others, that require novel approaches to the field of cyber security. We will call this new field cyber-physical security. The tight coupling between information and communication technologies and physical systems introduces new security concerns, requiring a rethinking of the commonly used objectives and methods. Existing security approaches are either inapplicable, not viable, insufficiently scalable, incompatible, or simply inadequate to address the challenges posed by highly complex environments such as the smart grid. A concerted effort by the entire industry, the research community, and the policy makers is required to achieve the vision of a secure smart grid infrastructure.
Short Survey on Static Hand Gesture Recognition

This paper presents a survey of methods which have been recently proposed for recognizing static hand gestures. These approaches are first summarized and then are assessed based on a common dataset. Because mentioned methods employ different types of input, the survey focuses on stages of feature extraction and classification. Other former steps, such as pre-processing and hand segmentation, are slightly modified. In experiments, this work does not only consider the recognition accuracy but also suggests suitable scenarios for each method according to its advantages and limitations. Hand gesture; rank-order correlation matrix, Gabor filter; block; centroid distance; Fourier transform
Swipe and Tell: Using Implicit Feedback to Predict User Engagement on Tablets

When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement.
 We assess the usefulness of swipe interactions on tablets for predicting engagement and make the comparison with using traditional features based on time spent.
 We gathered two unique datasets of more than 250,000 swipes, 100,000 unique article visits, and over 35,000 explicitly judged news articles by modifying two commonly used tablet apps of two newspapers. We tracked all device interactions of 407 experiment participants during one month of habitual news reading.
 We employed a behavioral metric as a proxy for engagement, because our analysis needed to be scalable to many users, and scanning behavior required us to allow users to indicate engagement quickly.
 We point out the importance of taking into account content ordering, report the most predictive features, zoom in on briefly read content and on the most frequently read articles.
 Our findings demonstrate that fine-grained tablet interactions are useful indicators of engagement for newsreaders on tablets. The best features successfully combine both time-based aspects and swipe interactions.
Efficient Classifier for Classification of Prognostic Breast Cancer Data through Data Mining Techniques

Data mining involves the process of recovering related, significant and credential information from a large collection of aggregated data. A major area of current research in data mining is the field of clinical investigations that involve disease diagnosis, prognosis and drug therapy. The objective of this paper is to identify an efficient classifier for prognostic breast cancer data. This research work involves designing a data mining framework that incorporates the task of learning patterns and rules that will facilitate the formulation of decisions in new cases. The machine learning techniques employed to train the proposed system are based on feature relevance analysis and classification algorithms. Wisconsin Prognostic Breast Cancer (WPBC) data from the UCI machine learning repository is utilized by means of data mining techniques to completely train the system on 198 individual cases, each comprising of 33 predictor values. This paper highlights the performance of feature reduction and classification algorithms on the training dataset. We evaluate the number of attributes for split in the Random tree algorithm and the confidence level and minimum size of the leaves in the C4.5 algorithm to produce 100 percent classification accuracy. Our results demonstrate that Random Tree and Quinlan’s C4.5 classification algorithm produce 100 percent accuracy in the training and test phase of classification with proper evaluation of algorithmic parameters.
Investigating Enactive Learning for Autonomous Intelligent Agents

The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty

Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world. Not surprisingly, the poverty stricken regions are also the ones which have a high probability of being a war zone, have poor infrastructure and sometimes have governments that do not cooperate with internationally funded development efforts. We train a CNN on free and publicly available daytime satellite images of the African continent from Landsat 7 to build a model for predicting local economic livelihoods. Only 5% of the satellite images can be associated with labels (which are obtained from DHS Surveys) and thus a semi-supervised approach using a GAN , albeit with a more stable-totrain flavor of GANs called the Wasserstein GAN regularized with gradient penalty [15] is used. The method of multitask learning is employed to regularize the network and also create an end-to-end model for the prediction of multiple poverty metrics.
Variation Among Processors Under Turbo Boost in HPC Systems

The design and manufacture of present-day CPUs causes inherent variation in supercomputer architectures such as variation in power and temperature of the chips. The variation also manifests itself as frequency differences among processors under Turbo Boost dynamic overclocking. This variation can lead to unpredictable and suboptimal performance in tightly coupled HPC applications. In this study, we use compute-intensive kernels and applications to analyze the variation among processors in four top supercomputers: Edison, Cab, Stampede, and Blue Waters. We observe that there is an execution time difference of up to 16% among processors on the Turbo Boost-enabled supercomputers: Edison, Cab, Stampede. There is less than 1% variation on Blue Waters, which does not have a dynamic overclocking feature. We analyze measurements from temperature and power instrumentation and find that intrinsic differences in the chips' power efficiency is the culprit behind the frequency variation. Moreover, we analyze potential solutions such as disabling Turbo Boost, leaving idle cores and replacing slow chips to mitigate the variation. We also propose a speed-aware dynamic task redistribution (load balancing) algorithm to reduce the negative effects of performance variation. Our speed-aware load balancing algorithm improves the performance up to 18% compared to no load balancing performance and 6% better than the non-speed aware counterpart.
An ultra low power, reconfigurable, multi-standard transceiver using fully digital PLL

This paper presents an ultra low power reconfigurable, multi-standard (IEEE802.15.4, BLE, 5Mbps proprietary) ISM2.4GHz band transceiver compliant to FCC, ETSI class 2 and ARIB regulations. It uses a DPLL with counter based area and power efficient re-circulating TDC, current reuse low area DCO, dynamic divider, class-AB PA, and fully integrated LDOs. The RX is reconfigurable between zero-IF/low-IF along with antenna diversity. The transceiver consumes 3.5mA (TX), 3.1mA (RX) from 3.0V battery with on-chip DCDC converter, and occupies 1.1mm2 in 65nm CMOS process. The RX front-end provides 42dB gain, 6dB NF, and -34dBm input P1dB.
Interaction of Open Innovation and Business Ecosystem

Open innovation breaks the original innovation border of organization and emphasizes the use of suppliers, customers, partners, and other internal and external innovative thinking and resources. How to effectively implement and manage open innovation has become a new business problem. Business ecosystem is the network system of value creation and co-evolution achieved by suppliers, users, partner, and other groups with self-organization mode. This study began with the risk analysis of open innovation implementation; then innovation process was embedded into business ecosystem structure; open innovation mode based on business ecosystem was proposed; business ecosystem based on open innovation was built according to influence degree of each innovative object. Study finds that both sides have a mutual promotion relationship, which provides a new analysis perspective for open innovation and business ecosystem; at the same time, it is also conducive to guiding the concrete practice of implementing open innovation.
The RWTH aachen university open source speech recognition system

We announce the public availability of the RWTH Aachen University speech recognition toolkit. The toolkit includes state of the art speech recognition technology for acoustic model training and decoding. Speaker adaptation, speaker adaptive training, unsupervised training, a finite state automata library, and an efficient tree search decoder are notable components. Comprehensive documentation, example setups for training and recognition, and a tutorial are provided to support newcomers.
eSphere: extracting spheres from unorganized point clouds

Spheres are popular geometric primitives found in many manufactured objects. However, sphere fitting and extraction have not been investigated in depth. In this paper, a robust method is proposed to extract multiple spheres accurately and simultaneously from unorganized point clouds. Moreover, a novel validation step is presented to assess the quality of the detected spheres, which help remove the confusion between perfect spheres and sphere-like shapes such as ellipsoids and paraboloids. A novel sampling strategy is introduced to reduce computational burden for sphere extraction. Experiments on both synthetic and scanned point clouds with different levels of noise and outliers are conducted and the results compared to state-of-the-art methods. These experiments demonstrate the efficiency and robustness of the proposed sphere extraction method.
Capital Market-Driven Corporate Finance

Much of empirical corporate finance focuses on sources of the demand for various forms of capital, not the supply. Recently, this has changed. Supply effects of equity and credit markets can arise from a combination of three ingredients: investor tastes, limited intermediation, and corporate opportunism. Investor tastes when combined with imperfectly competitive intermediaries lead prices and interest rates to deviate from fundamental values. Opportunistic firms respond by issuing securities with high prices and investing the proceeds. A link between capital market prices and corporate finance can in principle come from either supply or demand. This framework helps to organize empirical approaches that more precisely identify and quantify supply effects through variation in one of these three ingredients. Taken as a whole, the evidence shows that shifting equity and credit market conditions play an important role in dictating corporate finance and investment. 181 A nn u. R ev . F in . E co n. 2 00 9. 1: 18 120 5. D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g by H ar va rd U ni ve rs ity o n 02 /1 1/ 14 . F or p er so na l u se o nl y.
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. P156 Multiscale modeling of ischemic stroke with the NEURON reaction‐diffusion module Adam J. H. Newton, Alexandra H. Seidenstein, Robert A. McDougal, William W. Lytton Department of Neuroscience, Yale University, New Haven, CT 06520, USA; Department Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY 11203, USA; NYU School of Engineering, 6 MetroTech Center, Brooklyn, NY 11201, USA; Kings County Hospital Center, Brooklyn, NY 11203, USA Correspondence: Adam J. H. Newton (adam.newton@yale.edu) BMC Neuroscience 2017, 18 (Suppl 1):P156