nils-herrmann/clean_scirepeval huggingface dataset:
Current knowledge of Schisandra chinensis (Turcz.) Baill. (Chinese magnolia vine) as a medicinal plant species: a review on the bioactive components, pharmacological properties, analytical and biotechnological studies

Schisandra chinensis Turcz. (Baill.) is a plant species whose fruits have been well known in Far Eastern medicine for a long time. However, schisandra seems to be a plant still underestimated in contemporary therapy still in the countries of East Asia. The article presents latest available information on the chemical composition of this plant species. Special attention is given to dibenzo cyclooctadiene lignans. In addition, recent studies of the biological activity of dibenzocyclooctadiene lignans and schisandra fruit extracts are recapitulated. The paper gives a short resume of their beneficial effects in biological systems in vitro, in animals, and in humans, thus underlining their medicinal potential. The cosmetic properties are depicted, too. The analytical methods used for assaying schisandra lignans in the scientific studies and also in industry are also presented. Moreover, special attention is given to the information on the latest biotechnological studies of this plant species. The intention of this review is to contribute to a better understanding of the huge potential of the pharmacological relevance of S. chinensis.
Why tree-crop interactions in agroforestry appear at odds with tree-grass interactions in tropical savannahs

This paper describes recent research findings on resource sharing between trees and crops in the semiarid tropics and attempts to reconcile this information with current knowledge of the interactions between savannah trees and understorey vegetation by examining agroforestry systems from the perspective of succession. In general, productivity of natural vegetation under savannah trees increases as rainfall decreases, while the opposite occurs in agroforestry. One explanation is that in the savannah, the beneficial effects of microclimatic improvements (e.g. lower temperatures and evaporation losses) are greater in more xeric environments. Mature savannah trees have a high proportion of woody above-ground structure compared to foliage, so that the amount of water 'saved' (largely by reduction in soil evaporation) is greater than water 'lost' through transpiration by trees. By contrast, in agroforestry practices such as alley cropping where tree density is high, any beneficial effects of the trees on microclimate are negated by reductions in soil moisture due to increasing interception losses and tree transpiration. While investment in woody structure can improve the water economy beneath agroforestry trees, it inevitably reduces the growth rate of the trees and thus increases the time required for improved understorey productivity. Therefore, agroforesters prefer trees with more direct and immediate benefits to farmers. The greatest opportunity for simultaneous agroforestry practices is therefore to fill niches within the landscape where resources are currently under-utilised by crops. In this way, agroforestry can mimic the large scale patch dynamics and successional progression of a natural ecosystem.
Solving Weighted Constraint Satisfaction Problems with Memetic/Exact Hybrid Algorithms

A weighted constraint satisfaction problem (WCSP) is a constraint satisfaction problem in which preferences among solutions can be expressed. Bucket elimination is a complete technique commonly used to solve this kind of constraint satisfaction problem. When the memory required to apply bucket elimination is too high, a heuristic method based on it (denominated mini-buckets) can be used to calculate bounds for the optimal solution. Nevertheless, the curse of dimensionality makes these techniques impractical on large scale problems. In response to this situation, we present a memetic algorithm for WCSPs in which bucket elimination is used as a mechanism for recombining solutions, providing the best possible child from the parental set. Subsequently, a multi-level model in which this exact/metaheuristic hybrid is further hybridized with branch-and-bound techniques and mini-buckets is studied. As a case study, we have applied these algorithms to the resolution of the maximum density still life problem, a hard constraint optimization problem based on Conway’s game of life. The resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches. Moreover, it is shown that this proposal provides new best known solutions for very large instances.
Visual task solution strategies in tree diagrams

We investigate visual task solution strategies when exploring traditional, orthogonal, and radial node-link tree layouts, four orientations of the non-radial layouts, as well as varying difficulty of the task. The strategies are identified by examining eye movement data recorded in a controlled user study previously conducted by Burch et al. For detailed analysis of the spatio-temporal structures and patterns in the eye tracking data, we employ visual analytics techniques adopted from related methodology for geographic movement data by Andrienko et al. In this way, we complement the statistical analysis of task completion times and error rates reported by Burch et al. with spatio-temporal strategies that explain the variation in completion times. We identify differences between task solution strategies dependent on layout type, orientation, and task difficulty. Furthermore, we examine differences between groups of participants split according to completion time. Our analysis identifies that for all layouts it took nearly the same time to find the task solution node, but in the radial layout the solution was not confirmed directly. Instead, a more frequent cross-checking occurs afterwards, which is the main reason for the impaired performance of radial layouts.
Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers.

Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially.



The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate.



A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method.



Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward.
Iterative Learning Identification for Linear Time-Varying Systems

This brief presents an approach for identifying the parameters of linear time-varying systems that repeat their trajectories. The identification is based on the concept that parameter identification results can be improved by incorporating information learned from previous executions. The learning laws for this iterative learning identification are determined through an optimization framework. The convergence analysis of the algorithm is presented along with the experimental results to demonstrate its effectiveness. The algorithm is demonstrated to be capable of simultaneously estimating rapidly varying parameters and addressing robustness to noise by adopting a time-varying design approach.
C 6-4 THERMAL MAPPING OF HERMETICALLY SEALED COMPRESSORS USING COMPUTATIONAL FLUID DYNAMICS TECHNIQUE

The complex fluid flow and heat transfer phenomena in hermetic compressors is very difficult to analyze theoretically because of the assumptions made while solving the problem and insufficient understanding of the physics involved. There is a need to thoroughly understand such phenomena to target the efficiency improvements of the compressor to meet current high EER requirements. This paper deals with the use of Computational Fluid Dynamics (CFD), a highly sophisticated numerical technique based on Finite Control Volume formulation, to analyze the entire compressor domain.
Wideband Circular-Polarization Reconfigurable Antenna With L-Shaped Feeding Probes

A wideband circularly polarized reconfigurable patch antenna with L-shaped feeding probes is presented, which can generate unidirectional radiation performance that is switchable between left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). To realize this property, an L-probe fed square patch antenna is chosen as the radiator. A compact reconfigurable feeding network is implemented to excite the patch and generate either LHCP or RHCP over a wide operating bandwidth. The proposed antenna achieves the desired radiation patterns and has excellent characteristics, including a wide bandwidth, a compact structure, and a low profile. Measured results exhibit approximately identical performance for both polarization modes. Wide impedance, 31.6% from 1.2 to 1.65 GHz, and axial-ratio, 20.8% from 1.29 to 1.59 GHz, bandwidths are obtained. The gain is very stable across the entire bandwidth with a 6.9-dBic peak value. The reported circular-polarization reconfigurable antenna can mitigate the polarization mismatching problem in multipath wireless environments, increase the channel capacity of the system, and enable polarization coding.
A first-class approach to genericity

This paper describes how to add first-class generic types---including mixins---to strongly-typed OO languages with nominal subtyping such as Java and C#. A generic type system is "first-class" if generic types can appear in any context where conventional types can appear. In this context, a mixin is simply a generic class that extends one of its type parameters, e.g., a class C<T> that extends T. Although mixins of this form are widely used in Cpp (via templates), they are clumsy and error-prone because Cpp treats mixins as macros, forcing each mixin instantiation to be separately compiled and type-checked. The abstraction embodied in a mixin is never separately analyzed.Our formulation of mixins using first-class genericity accommodates sound local (class-by-class) type checking. A mixin can be fully type-checked given symbol tables for each of the classes that it directly references---the same context in which Java performs incremental class compilation. To our knowledge, no previous formal analysis of first-class genericity in languages with nominal type systems has been conducted, which is surprising because nominal subtyping has become predominant in mainstream object-oriented programming languages.What makes our treatment of first-class genericity particularly interesting and important is the fact that it can be added to the existing Java language without any change to the underlying Java Virtual Machine. Moreover, the extension is backward compatible with legacy Java source and class files. Although our discussion of a practical implementation strategy focuses on Java, the same implementation techniques could be applied to other object-oriented languages such as C# or Eiffel that support incremental compilation, dynamic class loading, and nominal subtyping.
Production of conidia of Penicillium camemberti in liquid medium through microcycles of conidiation

Microcycle conidiation is a survival mechanism of fungi encountering unfavorable conditions. In this phenomenon, asexual spores germinate secondary spores directly without formation of mycelium. As Penicillium camemberti conidia have the ability to produce conidiophores after germination in liquid culture induced by a thermal stress (18 and 30 °C), our work has aimed at producing conidia through this mean. Incubation at 18 and 30 °C increased the swelling of conidia and their proportion thereby producing conidiophores. Our results showed that the microcycle of conidiation can produce 5 × 108 conidia ml−1 after 7 days at 18 °C of culture. The activity of these conidia was checked through culture on a solid medium. Conidia produced by microcycle conidiation formed a normal mycelium on the surface of solid media and 25 % could still germinate after 5 months of storage.
A Dual-Band Dual-Polarization Slot Patch Antenna for GPS and Wi-Fi Applications

In this letter, a dual-band and dual-polarization capacitive-fed slot patch antenna is investigated. The proposed antenna can operate at 1.575 GHz for Global Positioning System and 2.4 GHz for Wi-Fi system with the corresponding polarizations. A 90 ° hybrid coupler chip was used to excite the right-hand circular polarization required for optimum GPS performance. For the high frequency band, a pair of linearly polarized arc-shaped slots radiating at 2.4 GHz are embedded in the circular patch. The operating bandwidths of the antenna are enhanced by the multilayered geometry, and the capacitive disks feedpoints placed between the substrate layers. The measured impedance bandwidths at the lower and high bands are 320 and 230 MHz, respectively. The measured 3-dB axial-ratio bandwidth is 120 MHz.
A fast local laplacian filter for high quality suggestive contours generation

Line drawings play an important role in shape description due to they can convey meaningful information by outstanding the key component and distracting details or ignoring less important. Suggestive contours are a type of lines to produce high quality line drawings. To generate those contours, we can generally start from two aspects: from image space or object space. The image space strategies can not only extract suggestive contours much faster than object space methods, but also don't require the information of the 3D objects. However they are sensitive to small noise, which is ubiquitous in the digital image. In this paper, before extracting lines, we apply an accelerated structure-preserving local Laplacian filter to smooth the shaded image. Through our experiments, we draw the conclusion that our method can effectively suppress the redundant details, generating a cleaner, higher quality line drawing by image space methods, and can compare with the result by object space ones.
An improved deep neural network model for job matching

Job matching which benefit job seekers, employees and employers is very important today. In this work, a deep neural network model is proposed to predict an employee's future career details, which includes position name, salary and company scale based on the online resume data. Like most NLP tasks, the input features are multi-field, non-sparse, discrete and categorical, while their dependencies are mostly unknown. Previous works were mostly focused on engineering, which resulted in a large feature space and heavy computation. To solve this task, we use embedding layers to explore feature interactions and merge two automatically learned features extracted from the resumes. Experimental results on over 70,000 real-word online resumes show that our model outperforms shallow models, like SVM and Random Forests, in effectiveness and accuracy.
A Multi-task Convolutional Neural Network for Joint Iris Detection and Presentation Attack Detection

In this work, we propose a multi-task convolutional neural network learning approach that can simultaneously perform iris localization and presentation attack detection (PAD). The proposed multi-task PAD (MT-PAD) is inspired by an object detection method which directly regresses the parameters of the iris bounding box and computes the probability of presentation attack from the input ocular image. Experiments involving both intra-sensor and cross-sensor scenarios suggest that the proposed method can achieve state-of-the-art results on publicly available datasets. To the best of our knowledge, this is the first work that performs iris detection and iris presentation attack detection simultaneously.
Simultaneous self-calibration of a projector and a camera using structured light

We propose a method for geometric calibration of an active vision system, composed of a projector and a camera, using structured light projection. Unlike existing methods of self-calibration for projector-camera systems, our method estimates the intrinsic parameters of both the projector and the camera as well as extrinsic parameters except a global scale without any calibration apparatus such as a checker-pattern board. Our method is based on the decomposition of a radial fundamental matrix into intrinsic and extrinsic parameters. Dense and accurate correspondences are obtained utilizing structured light patterns consisting of Gray code and phase-shifting sinusoidal code. To alleviate the sensitivity issue in estimating and decomposing the radial fundamental matrix, we propose an optimization approach that guarantees the possible solution using a prior for the principal points. We demonstrate the stability of our method using several examples and evaluate the system quantitatively and qualitatively.
What the hyena's laugh tells: Sex, age, dominance and individual signature in the giggling call of Crocuta crocuta

Among mammals living in social groups, individuals form communication networks where they signal their identity and social status, facilitating social interaction. In spite of its importance for understanding of mammalian societies, the coding of individual-related information in the vocal signals of non-primate mammals has been relatively neglected. The present study focuses on the spotted hyena Crocuta crocuta, a social carnivore known for its complex female-dominated society. We investigate if and how the well-known hyena's laugh, also known as the giggle call, encodes information about the emitter. By analyzing acoustic structure in both temporal and frequency domains, we show that the hyena's laugh can encode information about age, individual identity and dominant/subordinate status, providing cues to receivers that could enable assessment of the social position of an emitting individual. The range of messages encoded in the hyena's laugh is likely to play a role during social interactions. This call, together with other vocalizations and other sensory channels, should ensure an array of communication signals that support the complex social system of the spotted hyena. Experimental studies are now needed to decipher precisely the communication network of this species.
Cascaded Pose Regression Revisited: Face Alignment in Videos

Automated pose estimation is a fundamental task in computer vision. In this paper, we investigate the generic framework of Cascaded Pose Regression (CPR), which demonstrates practical effectiveness in pose estimation on deformable and articulated objects. In particular, we focus on the use of CPR for face alignment by exploring existing techniques and verifying their performances on different public facial datasets. We show that the correct selection of pose-invariant features is critical to encode the geometric arrangement of landmarks and crucial for the overall regressor learnability. Furthermore, by incorporating strategies that are commonly used among the state-of-the-art, we interpret the CPR training procedure as a repeated clustering problem with explicit regressor representation, which is complementary to the original CPR algorithm. In our experiment, the qualitative evaluation of existing alignment techniques demonstrates the success of CPR for facial pose inference that can be conveniently adopted to video detection and tracking applications.
Data-centric middleware based digital twin platform for dependable cyber-physical systems

The concept of digital twin, a kind of virtual things with the precise states of the corresponding physical systems, is suggested by industrial domains to accurately estimate the status and predict the operation of machines. Digital twin can be used for development of critical systems, such as self-driving cars and auto-production factories. There, however, will be so different digital twins in terms of resolution, complexity, modelling languages and formats. It is required to cooperate heterogeneous digital twins in standardized ways. Since a centralized digital twin system uses too big resources and energies, it is preferable to make large-scale digital twin system geographically and logically distributed over the Internet. In addition, efficient interworking functions between digital twins and the physical systems are required also. In this paper, we propose a novel architecture of large-scale digital twin platform including distributed digital twin cooperation framework, flexible data-centric communication middleware, and the platform based digital twin application to develop a reliable advanced driver assistance system.
Sex differences in response to visual sexual stimuli: a review.

This article reviews what is currently known about how men and women respond to the presentation of visual sexual stimuli. While the assumption that men respond more to visual sexual stimuli is generally empirically supported, previous reports of sex differences are confounded by the variable content of the stimuli presented and measurement techniques. We propose that the cognitive processing stage of responding to sexual stimuli is the first stage in which sex differences occur. The divergence between men and women is proposed to occur at this time, reflected in differences in neural activation, and contribute to previously reported sex differences in downstream peripheral physiological responses and subjective reports of sexual arousal. Additionally, this review discusses factors that may contribute to the variability in sex differences observed in response to visual sexual stimuli. Factors include participant variables, such as hormonal state and socialized sexual attitudes, as well as variables specific to the content presented in the stimuli. Based on the literature reviewed, we conclude that content characteristics may differentially produce higher levels of sexual arousal in men and women. Specifically, men appear more influenced by the sex of the actors depicted in the stimuli while women's response may differ with the context presented. Sexual motivation, perceived gender role expectations, and sexual attitudes are possible influences. These differences are of practical importance to future research on sexual arousal that aims to use experimental stimuli comparably appealing to men and women and also for general understanding of cognitive sex differences.