nils-herrmann/clean_scirepeval huggingface dataset:
High-Speed Visual Estimation Using Preattentive Processing

A new method is presented for performing rapid and accurate numerical estimation. The method is derived from an area of human cognitive psychology called preattentive processing. Preattentive processing refers to an initial organization of the visual field based on cognitive operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, intensity, orientation, size, and motion. We beleive that studies from preattentive vision should be used to assist in the design of visualization tools, especially those for which high-speed target detection, boundary identification, and region detection are important. In our present study, we investigated two known preattentive features (hue and orientation) in the context of a new task (numerical estimation) in order to see whether preattentive estimation was possible. Our experiments tested displays that were designed to visualize data from salmon migration simulations. The results showed that rapid and accurate estimation was indeed possible using either hue or orientation. Furthermore, random variation in one of these features resulted in no interference when subjects estimated the percentage of the other. To test the generality of our results, we varied two important display parameters—display duration and feature difference—and found boundary conditions for each. Implications of our results for application to real-world data and tasks are discussed.
An Elaborate Design and Implementation of a 3rd order Sigma-Delta Modulator ADC for Wideband Signals

The conventional sigma-delta (SigmaDelta) modulator structures used in telecommunication and audio applications usually cannot satisfy the requirements of signal processing applications for converting the wideband signals into digital samples accurately. In this paper, system design, analytical aspects and optimization methods of a third order incremental sigma-delta (SigmaDelta) modulator will be discussed and finally the designed modulator will be implemented by switched-capacitor circuits. The design of anti-aliasing filter has been integrated inside of modulator signal transfer function. It has been shown that the implemented 3rd order sigma-delta (SigmaDelta) modulator can be designed for the maximum SNR of 54 dB for minimum over- sampling ratio of 16. The modulator operating principles and its analysis in frequency domain and the topologies for its optimizing have been discussed elaborately. Simulation results on implemented modulator validate the system design and its main parameters such as stability and output dynamic range.
Improved simultaneous localization and mapping (SLAM) algorithms for aerial vehicle under dynamic sea surface environment

The aim of this work is to design a SLAM algorithm for localization and mapping of aerial platform for ocean observation. The aim is to determine the direction of travel, given that the aerial platform flies over the water surface and in an environment with few static features and dynamic background. This approach is inspired by the bird techniques which use landmarks as navigation direction. In this case, the blimp is chosen as the platform, therefore the payload is the most important concern in the design so that the desired lift can be achieved. The results show the improved SLAM is were able to achieve the desired waypoint.
Age, Gender and Racial Effects on Electrocardiographic Parameters

This paper summarizes the effect of age, gender and race on Electrocardiographic parameters. The conduction system and heart muscles get degenerative changes with advancing age so these parameters get changed. The ECG parameters also changes under certain diseases. Then it is essential to know the normal limits of these parameters for diagnostic purpose under the influence of age, gender and race. The automated ECG analysis systems require normal limits of these parameters. The age and gender of the population clearly influencing the normal limits of ECG parameters. However the investigation of the effect of Body Mass Index on cross section of the population is further warranted.
Overview of current and future energy storage technologies for electric power applications

In today’s world, there is a continuous global need for more energy which, at the same time, has to be cleaner than the energy produced from the traditional generation technologies. This need has facilitated the increasing penetration of distributed generation (DG) technologies and primarily of renewable energy sources (RES). The extensive use of such energy sources in today’s electricity networks can indisputably minimize the threat of global warming and climate change. However, the power output of these energy sources is not as reliable and as easy to adjust to changing demand cycles as the output from the traditional power sources. This disadvantage can only be effectively overcome by the storing of the excess power produced by DG-RES. Therefore, in order for these new sources to become completely reliable as primary sources of energy, energy storage is a crucial factor. In this work, an overview of the current and future energy storage technologies used for electric power applications is carried out. Most of the technologies are in use today while others are still under intensive research and development. A comparison between the various technologies is presented in terms of the most important technological characteristics of each technology. The comparison shows that each storage technology is different in terms of its ideal network application environment and energy storage scale. This means that in order to achieve optimum results, the unique network environment and the specifications of the storage device have to be studied thoroughly, before a decision for the ideal storage technology to be selected is taken.
Analytical Stiffness Calculation for Permanent Magnetic Bearings With Soft Magnetic Materials

According to Earnshaw's theorem, the ratio between axial and radial stiffness is always -2 for pure permanent magnetic configurations with rotational symmetry. Using highly permeable material increases the force and stiffness of permanent magnetic bearings. However, the stiffness in the unstable direction increases more than the stiffness in the stable direction. This paper presents an analytical approach to calculating the axial force and the axial and radial stiffnesses of attractive passive magnetic bearings (PMBs) with back iron. The investigations are based on the method of image charges and show in which magnet geometries lead to reasonable axial to radial stiffness ratios. Furthermore, the magnet dimensions achieving maximum force and stiffness per magnet volume are outlined. Finally, the calculation method was applied to the PMB of a magnetically levitated fan, and the analytical results were compared with a finite element analysis.
NEWS FRAMING AND PUBLIC OPINION: A MEDIATIONAL ANALYSIS OF FRAMING EFFECTS ON POLITICAL ATTITUDES Manuscript under review

A growing number of studies show how media frames can affect the formation of public opinion. To disentangle these effects, scholars must consider their underlying psychological processes or mediators. However, to date, there is no satisfactory account of the mediators of framing effects, and the explanatory power of different established mediators has not been addressed. Based on an experimental study (n = 1,537), this article presents a mediation analysis of a news framing effect on opinion, testing for two significant mediation processes: belief importance and belief content change. Results show that framing is mediated by both belief importance and belief content change, with belief content being the more prominent mediator. The extent to which each process takes effect depends on an individual’s level of political knowledge. Knowledgeable individuals are affected to a greater extent via both belief content and belief importance change. The implications for future framing effects research are discussed.
Peptide Vaccine: Progress and Challenges

Conventional vaccine strategies have been highly efficacious for several decades in reducing mortality and morbidity due to infectious diseases. The bane of conventional vaccines, such as those that include whole organisms or large proteins, appear to be the inclusion of unnecessary antigenic load that, not only contributes little to the protective immune response, but complicates the situation by inducing allergenic and/or reactogenic responses. Peptide vaccines are an attractive alternative strategy that relies on usage of short peptide fragments to engineer the induction of highly targeted immune responses, consequently avoiding allergenic and/or reactogenic sequences. Conversely, peptide vaccines used in isolation are often weakly immunogenic and require particulate carriers for delivery and adjuvanting. In this article, we discuss the specific advantages and considerations in targeted induction of immune responses by peptide vaccines and progresses in the development of such vaccines against various diseases. Additionally, we also discuss the development of particulate carrier strategies and the inherent challenges with regard to safety when combining such technologies with peptide vaccines.
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer Interface (BCI) applications. This can help to understand the limits of the model and how it may be improved, in addition to possibly provide insight about the data itself. Schirrmeister et al. (2017) have recently reported promising results for EEG decoding with deep convolutional neural networks (ConvNets) trained in an end-to-end manner and, with a causal visualization approach, showed that they learn to use spectral amplitude changes in the input. In this study, we investigate how ConvNets represent spectral features through the sequence of intermediate stages of the network. We show higher sensitivity to EEG phase features at earlier stages and higher sensitivity to EEG amplitude features at later stages. Intriguingly, we observed a specialization of individual stages of the network to the classical EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first evidence that particularly in the last convolutional layer, the network learns to detect more complex oscillatory patterns beyond spectral phase and amplitude, reminiscent of the representation of complex visual features in later layers of ConvNets in computer vision tasks. Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.
Micelle-Associated Protein in Epoetin Formulations: A Risk Factor for Immunogenicity?

An upsurge of pure red cell aplasia (PRCA) cases associated with subcutaneous treatment with epoetin alpha has been reported. A formulation change introduced in 1998 is suspected to be the reason for the induction of antibodies that also neutralize the native protein. The aim of this study was to detect the mechanism by which the new formulation may induce these antibodies.  Formulations of epoetin were subjected to gel permeation chromatography with UV detection, and the fractions were analyzed by an immunoassay for the presence of epoetin.  The chromatograms showed that Eprex®/Erypo® contained micelles of Tween 80. A minute amount of epoetin (0.008-0.033% of the total epoetin content) coeluted with the micelles, as evidenced by ELISA. When 0.03% (w/v) Tween 80, corresponding to the concentration in the formulation, was added to the elution medium, the percentage of epoetin eluting before the main peak was 0.68%.  Eprex®/Erypo® contains micelle-associated epoetin, which may be a risk factor for the development of antibodies against epoetin.
A Preliminary Literature Review of Digital Transformation Case Studies

While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice. Digital strategy, digital technologies, digital transformation, literature review.
Sorting Multidimensional Types : Theory and Application

This paper studies multidimensional matching between workers and jobs. Workers differ in manual and cognitive skills and sort into jobs that demand different combinations of these two skills. To study this multidimensional sorting, I develop a theoretical framework that generalizes the unidimensional notion of assortative matching. I derive the equilibrium in closed form and use this explicit solution to study biased technological change. The key finding is that an increase of worker-job complementarities in cognitive relative to manual inputs leads to more pronounced sorting and wage inequality across cognitive relative to manual skills. This can trigger wage polarization and boost aggregate wage dispersion. I then estimate the model for the US and identify sizeable technology shifts: During the 90s, worker-job complementarities in cognitive inputs increased by 15% whereas complementarities in manual inputs decreased by 41%. Besides this bias in complementarities, there has also been a strong cognitive skill -bias in production. Counterfactual exercises suggest that these technology shifts can account for observed changes in worker-job sorting, wage polarization and a significant part of the increase in US wage dispersion.
A Multi-Scale CRNN Model for Chinese Papery Medical Document Recognition

Paper-based medical documents are still widely used in many countries, while the contents within are difficult for patients to store and manage. In contrast, electronic medical documents not only help solve these problems, but also promote the development of telemedicine and medical big data. Thus, how to transform traditional printed medical documents into electronic ones becomes a key issue. It is worth noting that recognizing Chinese medical document in image form is a challenging task, as there are a variety of characters and symbols, including Greek alphabets, mathematical symbols and so on. The structure of Chinese characters is also often intricate. At present, the popular Optical Character Recognition methods are designed for single-scale characters, which tend to have poor performance in those complex scenarios. Based on Convolutional Recurrent Neural Network (CRNN), this paper proposes a multiscale architecture to recognize multi-lingual characters. To verify the effectiveness, the model is trained on a synthetic dataset and evaluated on a real Chinese medical document dataset. The experimental results demonstrate that the proposed method achieves substantial improvement over the recent methods.
The Small RNA Chaperone Hfq and Multiple Small RNAs Control Quorum Sensing in Vibrio harveyi and Vibrio cholerae

Quorum-sensing bacteria communicate with extracellular signal molecules called autoinducers. This process allows community-wide synchronization of gene expression. A screen for additional components of the Vibrio harveyi and Vibrio cholerae quorum-sensing circuits revealed the protein Hfq. Hfq mediates interactions between small, regulatory RNAs (sRNAs) and specific messenger RNA (mRNA) targets. These interactions typically alter the stability of the target transcripts. We show that Hfq mediates the destabilization of the mRNA encoding the quorum-sensing master regulators LuxR (V. harveyi) and HapR (V. cholerae), implicating an sRNA in the circuit. Using a bioinformatics approach to identify putative sRNAs, we identified four candidate sRNAs in V. cholerae. The simultaneous deletion of all four sRNAs is required to stabilize hapR mRNA. We propose that Hfq, together with these sRNAs, creates an ultrasensitive regulatory switch that controls the critical transition into the high cell density, quorum-sensing mode.
An optimistic and conservative register assignment heuristic for chordal graphs

This paper presents a new register assignment heuristic for procedures in SSA Form, whose interference graphs are chordal; the heuristic is called optimistic chordal coloring (OCC). Previous register assignment heuristics eliminate copy instructions via coalescing, in other words, merging nodes in the interference graph. Node merging, however, can not preserve the chordal graph property, making it unappealing for SSA-based register allocation. OCC is based on graph coloring, but does not employ coalescing, and, consequently, preserves graph chordality, and does not increase its chromatic number; in this sense, OCC is conservative as well as optimistic. OCC is observed to eliminate at least as many dynamically executed copy instructions as iterated register coalescing (IRC) for a set of chordal interference graphs generated from several Mediabench and MiBench applications. In many cases, OCC and IRC were able to find optimal or near-optimal solutions for these graphs. OCC ran 1.89x faster than IRC, on average.
Effects of pulse and DC charging on lithium iron phosphate (LiFePO4) batteries

Resonant converters which use a small DC bus capacitor to achieve high power factor are desirable for low cost Inductive Power Transfer (IPT) applications but produce amplitude modulated waveforms which are then present on any coupled load. The modulated coupled voltage produces pulse currents which could be used for battery charging purposes. In order to understand the effects of such pulse charging, two Lithium Iron Phosphate (LiFePO4) batteries underwent 2000 cycles of charge and discharging cycling utilizing both pulse and DC charging profiles. The cycling results show that such pulse charging is comparable to conventional DC charging and may be suitable for low cost battery charging applications without impacting battery life.
Protein Ligation in Living Cells Using Sortase

Sortagging is a versatile method for site-specific modification of proteins as applied to a variety of in vitro reactions. Here, we explore possibilities of adapting the sortase method for use in living cells. For intracellular sortagging, we employ the Ca²⁺-independent sortase A transpeptidase (SrtA) from Streptococcus pyogenes. Substrate proteins were equipped with the C-terminal sortase-recognition motif (LPXTG); we used proteins with an N-terminal (oligo)glycine as nucleophiles. We show that sortase-dependent protein ligation can be achieved in Saccharomyces cerevisiae and in mammalian HEK293T cells, both in the cytosol and in the lumen of the endoplasmic reticulum (ER). ER luminal sortagging enables secretion of the reaction products, among which circular polypeptides. Protein ligation of substrate and nucleophile occurs within 30 min of translation. The versatility of the method is shown by protein ligation of multiple substrates with green fluorescent protein-based nucleophiles in different intracellular compartments.
PERFORMANCE ANALYSIS OF TEXT AND IMAGE STEGANOGRAPHY WITH RSA ALGORITHM IN CLOUD COMPUTING

Cloud computing provides a lot of shareable resources payable on demand to the users. The drawback with cloud computing is the security challenges since the data in the cloud are managed by third party. Steganography and cryptography are some of the security measures applied in the cloud to secure user data. The objective of steganography is to hide the existence of communication from the unintended users whereas cryptography does provide security to user data to be transferred in the cloud. Since users pay for the services utilize in the cloud, the need to evaluate the performance of the algorithms used in the cloud to secure user data in order to know the resource consumed by such algorithms such as storage memory, network bandwidth, computing power, encryption and decryption time becomes imperative. In this work, we implemented and evaluated the performance of Text steganography and RSA algorithm and Image steganography and RSA as Digital signature considering four test cases. The simulation results show that, image steganography with RSA as digital signature performs better than text steganography and RSA algorithm. The performance differences between the two algorithms are 10.76, 9.93, 10.53 and 10.53 seconds for encryption time, 60.68, 40.94, 40.9, and 41.85 seconds for decryption time, 8.1, 10.92, 15.2 and 5.17 mb for memory used when hiding data, 5.3, 1.95 and 17.18 mb for memory used when extracting data, 0.93, 1.04, 1.36 and 3.76 mb for bandwidth used, 75.75, 36.2, 36.9 and 37.45 kwh for processing power used when hiding and extracting data respectively. Except in test case2 where Text steganography and RSA algorithm perform better than Image Steganography and RSA as Digital Signature in terms of memory used when extracting data with performance difference of -5.09 mb because of the bit size of the image data when extracted. This research work recommend the use of image steganography and RSA as digital signature to cloud service providers and users since it can secure major data types such as text, image, audio and video used in the cloud and consume less system resources.
Antiviral immunity in crustaceans.

Viral diseases of shrimp have caused negative effects on the economy in several countries in Asia, South America and America, where they have numerous shrimp culture industries. The studies on the immunity of shrimp and other crustaceans have mainly focused on general aspects of immunity and as a consequence little is known about the antiviral responses in crustaceans. The aim of this review is to update recent knowledge of innate immunity against viral infections in crustaceans. Several antiviral molecules have been isolated and characterized recently from decapods. Characterization and identification of these molecules might provide a promising strategy for protection and treatment of these viral diseases. In addition dsRNA-induced antiviral immunity is also included.