nils-herrmann/clean_scirepeval huggingface dataset:
Fluency does not express implicit knowledge of artificial grammars

It is commonly held that implicit knowledge expresses itself as fluency. A perceptual clarification task was used to examine the relationship between perceptual processing fluency, subjective familiarity, and grammaticality judgments in a task frequently used to produce implicit knowledge, artificial grammar learning (AGL). Four experiments examined the effects of naturally occurring differences and manipulated differences in perceptual fluency, where decisions were based on a brief exposure to test-strings (during the clarification task only) or normal exposure. When perceptual fluency was not manipulated, it was weakly related to familiarity and grammaticality judgments, but unrelated to grammatical status and hence not a source of accuracy. Counterbalanced grammatical and ungrammatical strings did not differ in perceptual fluency but differed substantially in subjective familiarity. When fluency was manipulated, faster clarifying strings were rated as more familiar and were more often endorsed as grammatical but only where exposure was brief. Results indicate that subjective familiarity derived from a source other than perceptual fluency, is the primary basis for accuracy in AGL. Perceptual fluency is found to be a dumb heuristic influencing responding only in the absence of actual implicit knowledge.
NEX: Virtual Machine Monitor Level Single System Support in Xen

Numerous approaches have been investigated to achieve single system image in order to simplify the complexity of programming and administration of cluster. This paper focuses on virtualization approach and presents NEX, a system of cooperative hypervisors with single system image support. NEX could provide a virtual SMP machine over cluster nodes. Commodity operating systems that support SMP could run across nodes through NEX in parallel. The approach is based on prior research on virtualization and distributed shared memory. This paper describes the design and implementation of NEX and gives a preliminary evaluation.
Normal Residual Blocks Albedo Residual Blocks Light Estimator SH light Normal Conv . Albedo Conv . Conv . Normal Albedo Shading Image Recon

We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained image of a human face into shape, reflectance and illuminance. Our network is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic images and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation.
Candida auris Candidemia in Kuwait, 2014

References 1. Vlieghe E, Phe T, De Smet B, Veng C, Kham C. Increase in Salmonella enterica serovar Paratyphi A infections in Phnom Penh, Cambodia, January 2011 to August 2013. Euro Surveill. 2013; 18:20592. 2. Tourdjman M, Le Hello S, Gossner C. Unusual increase in reported cases of Paratyphoid A fever among travellers returning from Cambodia, January to September 2013. Euro Surveill. 2013;18:20594. 3. Newton AE, Mintz ED. Infectious diseases related to travel: typhoid & paratyphoid fever. CDC health information for international travel [cited 2014 May 11]. http://wwwnc.cdc.gov/travel/ yellowbook/2014/chapter-3-infectious-diseases-related-to-travel/ typhoid-and-paratyphoid-fever 4. Mahon BE, Newton AE, Mintz ED. Effectiveness of typhoid vaccination in US travelers. Vaccine. 2014;32:3577–9. http://dx.doi.org/10.1016/j.vaccine.2014.04.055 5. Akhtar S, Sarker MR, Jabeen K, Sattar A, Qamar A, Fasih N. Antimicrobial resistance in Salmonella enterica serovar typhi and paratyphi in South Asia—current status, issues and prospects.  Increasing rates of Salmonella Paratyphi A and the current status of its vaccine development.
Explaining and Generalizing Back-Translation through Wake-Sleep

Back-translation has become a commonly employed heuristic for semi-supervised neural machine translation. The technique is both straightforward to apply and has led to stateof-the-art results. In this work, we offer a principled interpretation of back-translation as approximate inference in a generative model of bitext and show how the standard implementation of back-translation corresponds to a single iteration of the wake-sleep algorithm in our proposed model. Moreover, this interpretation suggests a natural iterative generalization, which we demonstrate leads to further improvement of up to 1.6 BLEU.
Anonymity and security in delay tolerant networks

A delay tolerant network (DTN) is a store and forward network where end-to-end connectivity is not assumed and where opportunistic links between nodes are used to transfer data. An emerging application of DTNs are rural area DTNs, which provide Internet connectivity to rural areas in developing regions using conventional transportation mediums, like buses. Potential applications of these rural area DTNs are e-governance, telemedicine and citizen journalism. Therefore, security and privacy are critical for DTNs. Traditional cryptographic techniques based on PKI-certified public keys assume continuous network access, which makes these techniques inapplicable to DTNs. We present the first anonymous communication solution for DTNs and introduce a new anonymous authentication protocol as a part of it. Furthermore, we present a security infrastructure for DTNs to provide efficient secure communication based on identity-based cryptography. We show that our solutions have better performance than existing security infrastructures for DTNs.
Stimulation of muscle protein synthesis by whey and caseinate ingestion after resistance exercise in elderly individuals.

Sarcopenia is a well-known phenomenon in elderly individuals and resistance exercise together with sufficient amino acid (AA) availability has proved to be a counteractive implement. However, the source of AA and supplement timing require further investigation. The objective was to compare muscle protein synthesis (MPS) to intakes of whey and caseinate after heavy resistance exercise in healthy elderly individuals, and, furthermore, to compare the timing effect of caseinate intake. Twenty-four elderly men and women (mean ± SEM; 68 ± 1 years) were randomized to one of four groups: caseinate intake before exercise (CasPre), caseinate intake immediately after exercise (CasPost), whey intake immediately after exercise (Whey), or intake of a non-caloric control drink (Control). Muscle myofibrillar and collagen fractional synthesis rates (FSR) were measured by a primed continuous infusion of L-[1-(13) C]leucine using labeled proteins during a 6-h recovery period. No differences were observed in muscle myofibrillar and collagen FSR with Whey (0.09 ± 0.01%/h) compared with CasPost (0.09 ± 0.003%/h), and it did not differ between CasPre (0.10 ± 0.01%/h) and CasPost. MPS does not differ with whey and caseinate feeding immediately after heavy resistance exercise in elderly individuals, and MPS is similar with caseinate ingestion before and after exercise.
A System for Identifying Toilet User by Characteristics of Paper Roll Rotation

Along with the progress of miniaturization and energy saving technologies of sensors, biological information in our daily life can be monitored by installing the sensors to a lavatory bowl. Lavatory is usually shared among several people, therefore biological information need to be identified. Using camera, microphone, or scales is not appropriate considering privacy in a lavatory. In this paper, we focus on the difference in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotation of a toilet paper roll with a gyroscope. The evaluation results confirmed that 85.8% accuracy was achieved for a five-people group in a laboratory environment.
Non-contact Method for Producing Tactile Sensation Using Airborne Ultrasound

This paper describes a new tactile device which produces stress fields in 3D space. Combined with 3D stereoscopic displays, this device is expected to provide high-fidelity tactile feedback for the interaction with 3D visual objects. The principle is based on a non-linear phenomenon of ultrasound, acoustic radiation pressure. We fabricated a prototype device to confirm the feasibility as a tactile display. The prototype consists of 91 airborne ultrasound transducers packed in the hexagonal arrangement, a 12 channel driving circuit, and a PC. The transducers which were in the same distance from the center of the transducer array were connected to form a 12 channel annular array. The measured total output force within the focal region was 0.8 gf. The spatial resolution was 20 mm. The prototype could produce sufficient vibrations up to 1 kHz.
A Robot Localization System Combining RSSI and Phase Shift in UHF-RFID Signals

This paper proposes a global indoor localization system based on the radio frequency identification (RFID) technology. A reader, installed on the robot, measures the received signal strength indication (RSSI) and the phase shift of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. The position of the tags in the environment is only roughly known at the beginning. Exploiting the complementary features of RSSI and phase-shift information in RFID signals (together with odometry data), a multihypothesis extended and unscented Kalman filter is proposed to localize the robot and to simultaneously improve the initial estimate on the tag coordinates. The simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.
Data classification with multilayer perceptrons using a generalized error function

The learning process of a multilayer perceptron requires the optimization of an error function E(y,t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E(Exp), inspired by the Z-EDM algorithm that we have recently proposed. An important property of E(Exp) is its ability to emulate the behavior of other error functions by the sole adjustment of a real-valued parameter. In other words, E(Exp) is a sort of generalized error function embodying complementary features of other functions. The experimental results show that the flexibility of the new, generalized, error function allows one to obtain the best results achievable with the other functions with a performance improvement in some cases.
OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph

This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google’s semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources. After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples. A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star. This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study. These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google’s mission to “organize the world’s information and make it universally accessible and useful”.
Content Caching and Distribution in Smart Grid Enabled Wireless Networks

To facilitate wireless transmission of multimedia content to mobile users, we propose a content caching and distribution framework for smart grid enabled OFDM networks, where each popular multimedia file is coded and distributively stored in multiple energy harvesting enabled serving nodes (SNs), and the green energy distributively harvested by SNs can be shared with each other through the smart grid. The distributive caching, green energy sharing, and the on-grid energy backup have improved the reliability and performance of the wireless multimedia downloading process. To minimize the total on-grid power consumption of the whole network, while guaranteeing that each user can retrieve the whole content, the user association scheme is jointly designed with consideration of resource allocation, including subchannel assignment, power allocation, and power flow among nodes. Simulation results demonstrate that bringing content, green energy, and SN closer to the end user can notably reduce the on-grid energy consumption.
A wafer-scale neuromorphic hardware system for large-scale neural modeling

Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal an integrated software/hardware framework has been developed which is centered around a unified neural system description language, called PyNN, that allows the scientist to describe a model and execute it in a transparent fashion on either a neuromorphic hardware system or a numerical simulator. A very large analog neuromorphic hardware system developed within FACETS is able to use complex neural models as well as realistic network topologies, i.e. it can realize more than 10000 synapses per neuron, to allow the direct execution of models which previously could have been simulated numerically only.
Genetic Algorithm for VRP with Constraints Based on Feasible Insertion

In the paper we propose a genetic algorithm based on insertion heuristics for the vehicle routing problem with constraints. A random insertion heuristic is used to construct initial solutions and to reconstruct the existing ones. The location where a randomly chosen node will be inserted is selected by calculating an objective function. The process of random insertion preserves stochastic characteristics of the genetic algorithm and preserves feasibility of generated individuals. The defined crossover and mutation operators incorporate random insertion heuristics, analyse individuals and select which parts should be reinserted. Additionally, the second population is used in the mutation process. The second population increases the probability that the solution, obtained in the mutation process, will survive in the first population and increase the probability to find the global optimum. The result comparison shows that the solutions, found by the proposed algorithm, are similar to the optimal solutions obtained by other genetic algorithms. However, in most cases the proposed algorithm finds the solution in a shorter time and it makes this algorithm competitive with others.
Bat algorithm for scheduling workflow applications in cloud

Workflow is one of the important aspects of cloud computing today. Cloud computing is one of the fastest growing technologies in the world. Workflows can be used in cloud as we use them in grid. Many operations in the cloud are based on workflow execution. Workflow systems are now becoming more complex and for such kind of systems efficient workflow management is important. Workflow scheduling is an important part of workflow management. Scheduling in general is NP-hard problem. To solve such kind of problems exhaustive methods cannot be used. Only non-exhaustive techniques can be used. In this paper we have used a metaheuristic approach called bat algorithm. Bat algorithm is specifically designed for optimizing hard problems. Here, bat algorithm with the help of binary bat algorithm is used for scheduling workflow in a cloud. Specifically the mapping of tasks and resources is done using this method. The optimal resources are selected such that the overall cost of the workflow is minimal.
Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee

The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013-2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.
Capsule Network Performance on Complex Data

In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN’s have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN’s: their failure to take into account of important spatial hierarchies between features, and their lack of rotational invariance . As long as certain key features of an object are present in the test data, CNN’s classify the test data as the object, disregarding features’ relative spatial orientation to each other. This causes false positives. The lack of rotational invariance in CNN’s would cause the network to incorrectly assign the object another label, causing false negatives. To address this concern, Hinton et al. propose a novel type of neural network using the concept of capsules in a recent paper. With the use of dynamic routing and reconstruction regularization, the capsule network model would be both rotation invariant and spatially aware.
Pow! Boom! Kablam! Effects of Viewing Superhero Programs on Aggressive, Prosocial, and Defending Behaviors in Preschool Children.

Many schools and parents try to motivate children to become defenders of victimized peers. Defending behavior is common in the media (particularly in superhero programs); however, no study has examined the effect of media on defending behavior. The aim of the study was to examine longitudinal associations between superhero engagement and a variety of aggressive, prosocial, and defending behaviors in preschool children. Participants consisted of 240 preschoolers (49% male) and their parents who reported on child media use and outcomes at 2 different time points. Preschooler's engagement with superheroes was related to increased physical and relational aggression 1 year later. Engagement with superheroes was not related to prosocial or defending behaviors. Implications of the results are discussed.
Sciatic nerve structure and nomenclature: epineurium to paraneurium: is this a new paradigm?

Sciatic nerve structure and nomenclature: epineurium to paraneurium is this a new paradigm? We read with interest the study by Perlas et al., (1) about the sciatic nerve block at the level of its division in the popliteal fossa. We have been developing this technique in our routine practice during the past 7 years and have no doub about the effi cacy and safety of this approach (2,3). However, we do not agree with the author's defi nition of the structure and limits of the nerve. Given the impact of publications from the principal author's research group on the regional anesthesia community, we are compelled to comment on proposed terminology that we feel may create confusion and contribute to the creation of a new paradigm in peripheral nerve blockade. The peripheral nerve is a well-defi ned anatomical entity with an unequivocal histological structure . The fascicle is the noble and functional unit of the nerves. Fascicles are constituted by a group of axons covered individually by the endoneurium and tightly packed within the perineurium. The epineurium comprises all the tissues that hold and surround the fascicles and defi nes the macroscopic external limit of the nerve. Epineurium includes loose connective and adipose tissue and epineurial vessels. Fascicles can be found as isolated units or in groups of fascicles supported and held together into a mixed collagen and fat tissue in different proportions (within the epineurial cover). The epineurium cover is the thin layer of connective tissue that encloses the whole structure and constitutes the anatomical limit of the nerve. It acts as a mechanical barrier (limiting the spread of injected local anesthetic), but not as a physical barrier (allowing the passive diffusion of local anesthetic along the concentration gradient). The paraneurium is the connective tissue that supports and connects the nerve with the surrounding structures (eg, muscles, bone, joints, tendons, and vessels) and acts as a gliding layer. We agree that the limits of the epineurium of the sciatic nerve, like those of the brachial plexus, are more complex than in single nerves. Therefore, the sciatic nerve block deserves special consideration. If we accept that the sciatic nerve is an anatomical unit, the epineurium should include the groups of fascicles that will constitute the tibial and the common peroneal nerves. Similarly, the epineurium of the common peroneal nerve contains the fascicles that will be part of the lateral cutane-ous, …
Morse landscapes of Riemannian functionals and related problems

The subject of this talk is Morse landscapes of natural functionals on infinitedimensional moduli spaces appearing in Riemannian geometry. First, we explain how recursion theory can be used to demonstrate that for many natural functionals on spaces of Riemannian structures, spaces of submanifolds, etc., their Morse landscapes are always more complicated than what follows from purely topological reasons. These Morse landscapes exhibit non-trivial “deep” local minima, cycles in sublevel sets that become nullhomologous only in sublevel sets corresponding to a much higher value of functional, etc. Our second topic is Morse landscapes of the length functional on loop spaces. Here the main conclusion (obtained jointly with Regina Rotman) is that these Morse landscapes can be much more complicated than what follows from topological considerations only if the length functional has “many” “deep” local minima, and the values of the length at these local minima are not “very large”.