nils-herrmann/clean_scirepeval huggingface dataset:
Transparent conducting silver nanowire networks.

We present a transparent conducting electrode composed of a periodic two-dimensional network of silver nanowires. Networks of Ag nanowires are made with wire diameters of 45-110 nm and a pitch of 500, 700, and 1000 nm. Anomalous optical transmission is observed, with an averaged transmission up to 91% for the best transmitting network and sheet resistances as low as 6.5 Ω/sq for the best conducting network. Our most dilute networks show lower sheet resistance and higher optical transmittance than an 80 nm thick layer of ITO sputtered on glass. By comparing measurements and simulations, we identify four distinct physical phenomena that govern the transmission of light through the networks: all related to the excitation of localized surface plasmons and surface plasmon polaritons on the wires. The insights given in this paper provide the key guidelines for designing high-transmittance and low-resistance nanowire electrodes for optoelectronic devices, including thin-film solar cells. For the latter, we discuss the general design principles to use the nanowire electrodes also as a light trapping scheme.
Jacobian matrix-based observability analysis for state estimation

In this paper, numerical observability analysis is restudied. Algorithms to determine observable islands and to decide a minimal set of pseudo-measurements to make the unobservable system observable are presented. The algorithms make direct use of the measurement Jacobian matrix. Gaussian elimination, which makes the whole process of observability analysis simple and effective, is the only computation required by the algorithms. Numerical examples are used to illustrate the proposed algorithms. Comparison of computation expense on the Texas system among the proposed algorithm and the existing algorithms is performed.
WIDEBAND CIRCULARLY POLARIZED UHF RFID READER ANTENNA WITH HIGH GAIN AND WIDE AXIAL RATIO BEAMWIDTHS

A broadband circularly polarized patch antenna with high gain and wide axial ratio beamwidths is proposed for ultra-highfrequency (UHF) RF identification (RFID) applications in this paper. The antenna is composed of a square patch, a feed network printed on the bottom side of the substrates and an antenna radome. The CP radiation of the proposed antenna is excited by four cylinder probes which transmit four signals that have equal amplitude with quadrature phase difference (0◦, 90◦, 180◦, and 270◦) generated from the feed network. To obtain an optimum peak gain and a broad CP bandwidth, 100Ω isolation resistor is omitted in the feed network for obtaining high radiation efficiency, and the effects of varying the feed positions and dimensions of the various parameters on the antenna performances are respectively investigated. Simulation results are compared with the measurements, and a good agreement is obtained. The measured results show that the proposed antenna can provide broad impendence bandwidth of 19.7% (815–993 MHz) (reflection coefficient less than −15 dB), a maximum gain of 9.65 dBi, and a 3-dB axial ratio (AR) bandwidth of about 11% (860–960 MHz). The results indicate that the proposed antenna is an excellent candidate for UHF RFID reader system. At last, read performance of the proposed antenna array in RFID systems is presented, which verify the superior features of the proposed antenna in practical RFID system applications.  2012, Scheduled 26 June 2012 *  Ping Wang (wangpingcqz@163.com).
The 2017 terahertz science and technology roadmap

Science and technologies based on terahertz frequency electromagnetic radiation (100 GHz–30 THz) have developed rapidly over the last 30 years. For most of the 20th Century, terahertz radiation, then referred to as sub-millimeter wave or far-infrared radiation, was mainly utilized by astronomers and some spectroscopists. Following the development of laser based terahertz time-domain spectroscopy in the 1980s and 1990s the field of THz science and technology expanded rapidly, to the extent that it now touches many areas from fundamental science to ‘real world’ applications. For example THz radiation is being used to optimize materials for new solar cells, and may also be a key technology for the next generation of airport security scanners. While the field was emerging it was possible to keep track of all new developments, however now the field has grown so much that it is increasingly difficult to follow the diverse range of new discoveries and applications that are appearing. At this point in time, when the field of THz science and technology is moving from an emerging to a more established and interdisciplinary field, it is apt to present a roadmap to help identify the breadth and future directions of the field. The aim of this roadmap is to present a snapshot of the present state of THz science and technology in 2017, and provide an opinion on the challenges and opportunities that the future holds. To be able to achieve this aim, we have invited a group of international experts to write 18 sections that cover most of the key areas of THz science and technology. We hope that The 2017 Roadmap on THz science and technology will prove to be a useful resource by providing a wide ranging introduction to the capabilities of THz radiation for those outside or just entering the field as well as providing perspective and breadth for those who are well established. We also feel that this review should serve as a useful guide for government and funding agencies.
Improving activity classification for health applications on mobile devices using active and semi-supervised learning

Mobile phones' increasing ubiquity has created many opportunities for personal context sensing. Personal activity is an important part of a user's context, and automatically recognizing it is vital for health and fitness monitoring applications. Recording a stream of activity data enables monitoring patients with chronic conditions affecting ambulation and motion, as well as those undergoing rehabilitation treatments. Modern mobile phones are powerful enough to perform activity classification in real time, but they typically use a static classifier that is trained in advance or require the user to manually add training data after the application is on his/her device. This paper investigates ways of automatically augmenting activity classifiers after they are deployed in an application. It compares active learning and three different semi-supervised learning methods, self-learning, En-Co-Training, and democratic co-learning, to determine which show promise for this purpose. The results show that active learning, En-Co-Training, and democratic co-learning perform well when the initial classifier's accuracy is low (75–80%). When the initial accuracy is already high (90%), these methods are no longer effective, but they do not hurt the accuracy either. Overall, active learning gave the highest improvement, but democratic co-learning was almost as good and does not require user interaction. Thus, democratic co-learning would be the best choice for most applications, since it would significantly increase the accuracy for initial classifiers that performed poorly.
Leaky gut - concept or clinical entity?

This article evaluates the current status of the gut barrier in gastrointestinal disorders.



The gut barrier is a complex, multicomponent, interactive, and bidirectional entity that includes, but is not restricted to, the epithelial cell layer. Intestinal permeability, the phenomenon most readily and commonly studied, reflects just one (albeit an important one) function of the barrier that is intimately related to and interacts with luminal contents, including the microbiota. The mucosal immune response also influences barrier integrity; effects of inflammation per se must be accounted for in the interpretation of permeability studies in disease states.



Although several aspects of barrier function can be assessed in man, one must be aware of exactly what a given test measures, as well as of its limitations. The temptation to employ results from a test of paracellular flux to imply a role for barrier dysfunction in disorders thought to be based on bacterial or macromolecular translocation must be resisted. Although changes in barrier function have been described in several gastrointestinal disorders, their primacy remains to be defined. At present, few studies support efficacy for an intervention that improves barrier function in altering the natural history of a disease process.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects

This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).
Exploring the Determinants of Knowledge Exchange in Virtual Communities

Virtual communities have become ubiquitous and vital in nearly all professions. These communities typically involve voluntary participation, and given that these communities are not organization specific, community members' motivations for participation vary. In this study, we investigate the motivations of the members of a professional virtual community to engage in knowledge exchange. We synthesize social exchange theory and the theory of reasoned action to identify critical determinants of attitudes toward knowledge exchange in virtual communities, namely, trust among participants, anticipated reciprocal relationships, and the relevance of the community to participants' jobs. Additionally, we posit that attitudes will influence the intention to use the virtual community and that this relationship will be moderated by the perceived quality of the information exchanged within the community. We test our research model using data compiled from a community of research scientists in South Korea. Our findings indicate that trust among participants has a positive influence on attitudes toward both sharing and acquiring knowledge. The anticipation of a reciprocal relationship has a positive effect on attitudes toward knowledge acquisition, and job relevance has a positive effect on attitudes toward knowledge sharing. Furthermore, attitudes toward knowledge acquisition affect attitudes toward knowledge sharing, and attitudes toward knowledge sharing positively influence intentions to use a virtual community. We also find that perceived information quality negatively moderates the relationship between attitudes toward knowledge sharing and user intentions to use a virtual community. We interpret and discuss these findings, and their implications for research and practice.
Towards Reliable Grasping and Manipulation in Household Environments

We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).
How online social interactions influence customer information contribution behavior in online social shopping communities: A social learning theory perspective

Online social shopping communities are transforming the way customers communicate and exchange product information with others. To date, the issue of customer participation in online social shopping communities has become an important but underexplored research area in the academic literature. In this study, we examined how online social interactions affect customer information contribution behavior. We also explored the moderating role of customer reputation in the relationship between observational learning and reinforcement learning as well as customer information contribution behavior. Analyses of panel data from 6,121 customers in an online social fashion platform revealed that they are significant factors affecting customer information contribution behavior and that reinforcement learning exhibits a stronger effect than observational learning. The results also showed that customer reputation has a significant negative moderating effect on the relationship between observational learning and customer information contribution behavior. This study not only enriched our theoretical understanding of information contribution behavior but also provided guidelines for online social shopping community administrators to better design their community features.
CRF framework for supervised preference aggregation

We develop a flexible Conditional Random Field framework for supervised preference aggregation, which combines preferences from multiple experts over items to form a distribution over rankings. The distribution is based on an energy comprised of unary and pairwise potentials allowing us to effectively capture correlations between both items and experts. We describe procedures for learning in this modelnand demonstrate that inference can be done much more efficiently thannin analogous models. Experiments on benchmark tasks demonstrate significant performance gains over existing rank aggregation methods.
Dual-Mode Power-Split Transmission for Hybrid Electric Vehicles

In this paper, an innovative power-split device (PSD) is introduced, and its application in a hybrid powertrain system is studied. The new PSD is a mechanism that allows operation in two different power-split modes through locking/unlocking of two clutches. In one mode, the PSD operates similar to a standard planetary gear unit, and in the other mode, it works the same as a compound planetary set. A well-known analogous system is the Toyota Hybrid System (THS) and is used for comparison purposes. It is demonstrated that by the new system, the transmission losses are reduced by a considerable extent, and thus, the efficiency is improved. A controller is designed based on fuzzy logic, which receives the battery state of charge (SOC), the vehicle speed, and the power that is requested at the wheels to coordinate each component in such a way as to optimize the entire system efficiency. A numerical optimization algorithm is applied to sustain the SOC in high regions and shift engine operating points to higher efficiency regions. Simulation results demonstrate notable improvements in fuel economy and performance characteristics.
Spherical convolutions and their application in molecular modelling

Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based on the cubed-sphere representation. We investigate the challenges that arise in this setting, and extend our discussion to include scenarios of spherical volumes, with several strategies for parameterizing the radial dimension. As a proof of concept, we conclude with an assessment of the performance of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions in this setting. In particular, despite the lack of any domain specific feature-engineering, we demonstrate performance comparable to state-of-the-art methods in the field, which build on decades of domain-specific knowledge.
Controlling Swarms of Robots Using Interpolated Implicit Functions

We address the synthesis of controllers for large groups of robots and sensors, tackling the specific problem of controlling a swarm of robots to generate patterns specified by implicit functions of the form s(x, y) = 0. We derive decentralized controllers that allow the robots to converge to a given curve S and spread along this curve. We consider implicit functions that are weighted sums of radial basis functions created by interpolating from a set of constraint points, which give us a high degree of control over the desired 2D curves. We describe the generation of simple plans for swarms of robots using these functions and illustrate our approach through simulations and real experiments.
Multiple trait analysis of genetic mapping for quantitative trait loci.

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.
A 56GS/S 6b DAC in 65nm CMOS with 256×6b memory

Modern optical systems increasingly rely on DSP techniques for data transmission at 40Gbs and recently at 100Gbs and above. A significant challenge towards CMOS TX DSP SoC integration is due to requirements for four 6b DACs  to operate at 56Gs/s with low power and small footprint. To date, the highest sampling rate of 43Gs/s 6b DAC is reported in SiGe BiCMOS process . CMOS DAC implementations are constraint to 12Gs/s with the output signal frequency limited to 1.5GHz [2–4]. This paper demonstrates more than one order of magnitude improvement in 6b CMOS DAC design with a test circuit operating at 56Gs/s, achieving SFDR >30dBc and ENOB>4.3b up to the output frequency of 26.9GHz. Total power dissipation is less than 750mW and the core DAC die area is less than 0.6×0.4 mm2.
Buildings, Beauty, and the Brain: A Neuroscience of Architectural Experience

A burgeoning interest in the intersection of neuroscience and architecture promises to offer biologically inspired insights into the design of spaces. The goal of such interdisciplinary approaches to architecture is to motivate construction of environments that would contribute to peoples' flourishing in behavior, health, and well-being. We suggest that this nascent field of neuroarchitecture is at a pivotal point in which neuroscience and architecture are poised to extend to a neuroscience of architecture. In such a research program, architectural experiences themselves are the target of neuroscientific inquiry. Here, we draw lessons from recent developments in neuroaesthetics to suggest how neuroarchitecture might mature into an experimental science. We review the extant literature and offer an initial framework from which to contextualize such research. Finally, we outline theoretical and technical challenges that lie ahead.
Increasing The Accuracy Of An Existing Fingerprint Recognition System Using Adaptive Technique

Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person’s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. The biometric system operates as verification mode or identification mode depending on the requirement of an application. The verification mode validates a person’s identity by comparing captured biometric data with ready made template. The identification mode recognizes a person’s identity by performing matches against multiple fingerprint biometric templates. Fingerprints are widely used in daily life for more than 100 years due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Fingerprint is a pattern of ridges, furrows and minutiae, which are extracted using inked impression on a paper or sensors. A good quality fingerprint contains 25 to 80 minutiae depending on sensor resolution and finger placement on the sensor. The false minutiae are the false ridge breaks due to insufficient amount of ink and crossconnections due to over inking. It is difficult to extract reliably minutia from poor quality fingerprint impressions arising from very dry fingers and fingers mutilated by scars, scratches due to accidents, injuries. Minutia based fingerprint recognition consists of Thinning, Minutiae extraction, Minutiae matching and Computing matching score.  Ridge Enhancement, Thinning, Binarization, Histogram Equalization, Crossing Number, Minutiae Score.