Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Plant Growth-Promoting Microorganisms (PGPMs) on Plant Induction Responses and Subsequent ＂Spodoptera Litura＂ Performance

Plant-growth promoting microorganisms (PGPMs) have become important microbial fertilizers lately. This soilborne microbe-plant interaction may also affect plant defense responses. However, little is known about the effect of PGPMs on induced defensive responses of various plant species. In this study, we assessed the effects of PGPMs and plant species on plant's chemistry and performance of the subsequent insect herbivore Spodoptera litura. Two plant species (cabbage and tomato) were inoculated with PGPMs (a fungus and bacteria). The leaves of plants treated with PGPMs were collected to feed third-instar larvae of S. litura and to assess the plant nutrient and defensive compounds. The results indicated that the PGPM application exerted various influences on the foliar nutrient and anti-nutrient compounds and affected the relative growth rate of S. litura. In summary, applying PGPMs might individually affect the induced responses and cause specific interactions. However, this study only provided partial evidence regarding this interaction effect; additional studies are required to clarify the effects of PGPMs on plant defense responses and subsequent insect herbivores.
To Populate is To Regulate

We examine the effects of instantiating Lewis signaling games within a population of speaker and listener agents with the aim of producing a set of general and robust representations of unstructured pixel data. Preliminary experiments suggest that the set of representations associated with languages generated within a population outperform those generated between a single speaker-listener pair on this objective, making a case for the adoption of population-based approaches in emergent communication studies. Furthermore, post-hoc analysis reveals that population-based learning induces a number of novel factors to the conventional emergent communication setup, inviting a wide range of future research questions regarding communication dynamics and the flow of information within them.
The variance of radio interferometric calibration solutions

This paper investigates the possibility of improving radio interferometric images using an algorithm inspired by an optical method known as “lucky imaging”, which would give more weight to the best-calibrated visibilities used to make a given image. A fundamental relationship between the statistics of interferometric calibration solution residuals and those of the image-plane pixels is derived in this paper. This relationship allows us to understand and describe the statistical properties of the residual image. In this framework, the noise map can be described as the Fourier transform of the covariance between residual visibilities in a new differential Fourier plane. Image-plane artefacts can be seen as one realisation of the pixel covariance distribution, which can be estimated from the antenna gain statistics. Based on this relationship, we propose a means of improving images made with calibrated visibilities using weighting schemes. This improvement would occur after calibration, but before imaging; it is thus ideally used between major iterations of self-calibration loops. Applying the weighting scheme to simulated data improves the noise level in the final image at negligible computational cost.
Similarity-DT: Kernel Similarity Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess high similarity correlation. However, there are no DT synthesis methods to consider the similarity prior for representing DT instead, which can explicitly capture the homogeneous and heterogeneous correlation between different frames of DT. In this paper, we propose a novel DT synthesis method (named Similarity-DT), which embeds the similarity prior into the representation of DT. Specifically, we first raise two hypotheses: the content of texture video frames varies over time-to-time, while the more closed frames should be more similar; the transition between frame-to-frame could be modeled as a linear or nonlinear function to capture the similarity correlation. Then, our proposed Similarity-DT integrates kernel learning and extreme learning machine (ELM) into a powerful unified synthesis model to learn kernel similarity embedding to represent the spatial-temporal transition among frame-to-frame of DTs. Extensive experiments on DT videos collected from internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding effectively exhibits the discriminative representation for DTs. Hence our method is capable of preserving long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. We also show that our method effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art approaches.
A semi-supervised segmentation algorithm as applied to k-means using information value

Segmentation (or partitioning) of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main streams of segmentation and examples exist where the application of these techniques improved the performance of predictive models. Both these streams focus, however, on a single aspect (i.e. either target separation or independent variable distribution) and combining them may deliver better results in some instances. In this paper a semi-supervised segmentation algorithm is presented, which is based on k-means clustering and which applies information value for the purpose of informing the segmentation process. Simulated data are used to identify a few key characteristics that may cause one segmentation technique to outperform another. In the empirical study the newly proposed semi-supervised segmentation algorithm outperforms both an unsupervised and a supervised segmentation technique, when compared by using the Gini coecient as performance measure of the resulting predictive models. Key words : Banking, clustering, multivariate statistics, data mining
Quantum speedups need structure

We prove the following conjecture, raised by Aaronson and Ambainis in 2008: Let $f:\{-1,1\}^n \rightarrow [-1,1]$ be a multilinear polynomial of degree $d$. Then there exists a variable $x_i$ whose influence on $f$ is at least $\mathrm{poly}(\mathrm{Var}(f)/d)$. As was shown by Aaronson and Ambainis, this result implies the following well-known conjecture on the power of quantum computing, dating back to 1999: Let $Q$ be a quantum algorithm that makes $T$ queries to a Boolean input and let $\epsilon,\delta > 0$. Then there exists a deterministic classical algorithm that makes $\mathrm{poly}(T,1/\epsilon,1/\delta)$ queries to the input and that approximates $Q$'s acceptance probability to within an additive error $\epsilon$ on a $1-\delta$ fraction of inputs. In other words, any quantum algorithm can be simulated on most inputs by a classical algorithm which is only polynomially slower, in terms of query complexity.
I T ] 2 F eb 2 01 7 On asymptotically good ramp secret sharing schemes ∗

Asymptotically good sequences of linear ramp secret sharing schemes have been intensively studied by Cramer et al. in terms of sequences of pairs of nested algebraic geometric codes [4, 5, 6, 7, 8, 10]. In those works the focus is on full privacy and full reconstruction. In this paper we analyze additional parameters describing the asymptotic behavior of partial information leakage and possibly also partial reconstruction giving a more complete picture of the access structure for sequences of linear ramp secret sharing schemes. Our study involves a detailed treatment of the (relative) generalized Hamming weights of the considered codes.
Effects of liposomes containing exogenous recombinant porcine somatotropin on rats

Sixty-four Sprague Dawley (SD) rats were divided randomly into 4 groups of 16. The control group was injected with physiological saline (1 ml); group 1 was injected with pure porcine somatotropin (PST) daily, 1 ml per rat (1.2 mg/ml); group 2 was injected with PST liposome every 7 days (8.4 mg/ml); group 3 was injected with PST liposome every 14 days (16.8 mg/ml). Porcine somatotropin (PST) and PST liposomes were injected into the abdominal cavity of the rats for 28 days. Both PST and PST liposome stimulated the growth performance of rats. There were, however, significant differences in growth rate and IGF-I content between the treatment and the control groups (P<0.05); the growth effect of PST liposomes on rats was mediated by the sustained-release formulation used in this experiment. The effect of this formulation lasted more than seven days.
Measuring Variability Factors in Consumer Value for Profit Optimization in a Firm — A Framework for Analysis Rajagopal

Consumer value may be defined as a tool to measure the prolonged satisfaction and the on-going propensity to buy products and services. Though there are many issues floating in current debates about consumer value, it may be argued that the importance of consumer value in terms of the level of satisfaction is evident in providing a revenue stream to companies, and thereby repeat purchase behavior is of strategic importance to management. The consumer value concept is used to assess product performance and eventually to determine the competitive market structure and the product-market boundaries. Consumer value may be measured as product efficiency viewed from the consumer’s perspective, i.e., as a ratio of outputs (e.g., resale value, reliability, safety, or comfort) that consumers obtain from a product relative to inputs (price, running costs, and so on) that consumers have to deliver in exchange. The efficiency value derived can be understood as the return on the consumer’s investment. Products offering a maximum consumer value relative to all other alternatives in the market are characterized as efficient. This paper develops the framework for measuring consumer value with reference to establishing the long-run relationship with the firm and optimizing its profit levels. The discussions in this paper address the core issues of consumer value in retailing products and services: how to conceptualize it, how to measure it, and how to manage it.
xSLUE: A Benchmark and Analysis Platform for Cross-Style Language Understanding and Evaluation

Every natural text is written in some style. The style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. Some factors implicitly reflect the author's personality, while others are explicitly controlled by the author's choices in order to achieve some personal or social goal. One cannot form a complete understanding of a text and its author without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides a benchmark corpus (xSLUE) with an online platform (this http URL) for cross-style language understanding and evaluation. The benchmark contains text in 15 different styles and 23 classification tasks. For each task, we provide the fine-tuned classifier for further analysis. Our analysis shows that some styles are highly dependent on each other (e.g., impoliteness and offense), and some domains (e.g., tweets, political debates) are stylistically more diverse than others (e.g., academic manuscripts). We discuss the technical challenges of cross-style understanding and potential directions for future research: cross-style modeling which shares the internal representation for low-resource or low-performance styles and other applications such as cross-style generation.
SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different. In this paper, we propose a gradient detach based stacked complementary losses (SCL) method that uses detection losses as the primary objective, and cuts in several auxiliary losses in different network stages accompanying with gradient detach training to learn more discriminative representations. We argue that the prior methods mainly leverage more loss functions for training but ignore the interaction of different losses and also the compatible training strategy (gradient detach updating in our work). Thus, our proposed method is a more syncretic adaptation learning process. We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a significant margin. For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.
Improving Node Classification by Co-training Node Pair Classification: A Novel Training Framework for General Graph Neural Networks

Semi-supervised learning is a widely used training framework for graph node classification. However, there are two problems existing in this learning method: (1) the original graph topology may not be perfectly aligned with the node classification task; (2) the supervision information in the training set has not been fully used. To tackle these two problems, we design a new task: node pair classification, to assist in training GNN models for the target node classification task. We further propose a novel training framework named Adaptive Co-training, which jointly trains the node classification and the node pair classification after the optimization of graph topology. Extensive experimental results on four representative GNN models have demonstrated that our proposed training framework significantly outperforms baseline methods across three benchmark graph datasets.
Pencantuman melalui pencahayaan salutan hidrogel poliakrilamida ke atas pelbagai jenis tekstil polietilena tereftalat

In this paper, polyacrylamide (PAAm) hydrogels grafted via photopolymerization onto various types of polyethylene terephthalate (PET) as matrix were designed and synthesized. The investigation was carried out based on thickness (0.02 -0.07 mm) of nonwoven PET textiles from various resources as well as commercial PET membrane. In this study, PET matrices with a disk shape of 4.5 cm in diameter were coated with thin hydrogel using UVA photopolymerization system. The resulting grafted PAAm-g-PET was examined through degree of grafting (DG) and characterized by using Fourier Transformed Infrared Spectroscopy (FTIR). The DG above 50 % results showed the ability of PAAm hydrogel to be grafted onto PET. The various range of thickness and surface of PET also gave an impact onto the performance of grafting of PAAm onto PET. FTIR results also confirmed the addition of amide group after grafting process (1720 cm -1, 1100 cm-1, 850 cm-1). The hydrophilicity of hydrogels was reported to impart oil fouling resistance. We expect that grafted hydrogel layer has fascinating future for oil/water separation.
MHD activity in EC-heated TCV plasmas with eITBs

features will be described and the influence on plasma performance will be discussed. For the studies presented here the MHD activity has been measured using sets of magnetic probes to record magnetic field perturbations and sets of detector arrays to record the plasma emission in the soft X-ray range (SXR). The magnetic probes are arranged in a toroidal array (16 probes) and a poloidal array (38 probes) permitting us to identify toroidal and poloidal mode numbers up to n=4 and m=8, respectively. The viewing chords of the X-ray detectors cover the poloidal cross section and permit tomographic reconstruction of the emissivity profiles. The signals from fans of chords have been analyzed to identify MHD activity in the central region of the plasma. During eITB scenarios on TCV two basic types of MHD activities have been observed: tearing modes (TM) and kink-like modes at low m/n rational q-surfaces. The latter show the signature of periodic relaxation oscillations (PROs) with sawtooth-like features in the SXR signals. The tearing modes cause slowly oscillating perturbations (3-6kHz on TCV) of the fields measured by the magnetic probes. In general, tearing modes (TMs) are associated with the appearance of magnetic islands at rational q-surfaces and are driven by current and pressure gradients. In our cases, the tearing modes are localized near q=m/n=2/1 and/or 3/1 surfaces outside the eITB in the region of positive magnetic shear. At the mode onset the values of ￻N range from 0.6 to 1.4
Sea-surface CO 2 fugacity in the subpolar North Atlantic

Abstract. We present the first year-long subpolar trans-Atlantic set of surface seawater CO2 fugacity (fCO2sw) data. The data were obtained aboard the MV Nuka Arctica in 2005 and provide a quasi-continuous picture of the fCO2sw variability between Denmark and Greenland. Complementary real-time high-resolution data of surface chlorophyll-a (chl-a) concentrations and mixed layer depth (MLD) estimates have been collocated with the fCO2sw data. Off-shelf fCO2sw data exhibit a pronounced seasonal cycle. In winter, surface waters are saturated to slightly supersaturated over a wide range of temperatures. Through spring and summer, fCO2sw decreases by approximately 60 μatm, due to biological carbon consumption, which is not fully counteracted by the fCO2sw increase due to summer warming. The changes are synchronous with changes in chl-a concentrations and MLD, both of which are exponentially correlated with fCO2sw in off-shelf regions.
Experience and Repetition as Antecedents of Organizational Routines and Capabilities: A Critique of Behaviorist and Empiricist Approaches

We discuss the behaviorist and empiricist foundations of the organizational routines and capabilities literature, specifically the extant emphasis placed on experience, repetition and observation as the key inputs and mechanisms of behavior, learning and change in organizations. Based on this discussion we highlight several concerns associated with specifying experience and repetition as antecedents of routines and capabilities, namely, (1) the problem of origins and causation, (2) the problem of extremes, (3) the problem of intentionality, (4) the problem of new knowledge, and (5) the problem of the environment. We highlight the “poverty of stimulus” argument and more generally discuss how internalist or rationalist, choice-based approach might provide a more fruitful (though preliminary) foundation for extant research on organizational routines and capabilities.
The antisense expression of AhPEPC1 increases seed oil production in peanuts (Arachis hypogaea L.) ; La expresión antisentido AhPEPC1 incrementa la producción de aceite de semillas de cacahuete (Arachis hypogaea L.)

L. Pan, J. Zhang, X. Chi, N. Chen, M. Chen, M. Wang, T. Wang, Z. Yang, Z. Zhang, Y. Wan, S. Yu* and F. Liu* State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomic Sciences, Shandong Agricultural University, Tai’an, 271018, P R China Shandong Peanut Research Institute, Qingdao, 266100, P R China Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, P R China Corresponding authors: shanlinyu2012@163.com; liufz@sdau.edu. cn
CosModel: An Interaction Model for SOCIAL NETWORK GAMES

Social network games (videogames for online social networks), have become very popular among videogame developers.Nevertheless, the conceptual aids for designers and developers of this type of games are scarce, particularly the aids that focus on interactionbetween players. Due to this, CosModel is proposed as a model for the design of social network games. CosModel is made up of three interaction design views that aid in the construction of these games and focus on enhancing player interactions. One of these views features the use of a metaphor as a conceptual aid for the design of social network games. This paper presents the theoretical background that supportsthe model, the model's development procedure and structure, and a proposed process for its implementation.
Electrospun PVA Nanofibres for Gas Filtration Applications

Nanofibres are easily produced from polymer solutions of polyvinyl alcohol using the elec trospinning method. Nanofibres combined with nonwoven PP material (polypropylene) have potential uses in a wide range of filtration applications. The high surface area of a high porous structure makes nanofibres ideally suited for many filtering submicron parti cles from gas. The main aim of this article is to determine which organic compounds can be eliminated from gas using a nonwoven PP material filter covered by a PVA nanofibre membrane. In this study PVA solutions were prepared with 8% PVA. A nanofibre web was manufactured using the electrospinning method on a NS LAB NanospiderTM laboratory machine. Specimens of filtered gas were obtained with a device which was made especially for this research. An IR spectrum of the filtered gas and filters was written. The research showed that a nonwoven PP material filter covered by a PVA nanofibre membrane is suit able to eliminate organic polar compounds, ethers and compounds containing carbonyl groups from gas.
Around the Way: Testing ΛCDM with Milky Way Stellar Stream Constraints

Recent analyses of the Pal 5 and GD-1 tidal streams suggest that the inner dark matter halo of the Milky Way is close to spherical, in tension with predictions from collisionless N-body simulations of cosmological structure formation. We use the Eris simulation to test whether the combination of dissipative physics and hierarchical structure formation can produce Milky Way–like galaxies whose dark matter halos match the tidal stream constraints from the GD-1 and Pal 5 clusters. We use a dynamical model of the simulated Eris galaxy to generate many realizations of the GD-1 and Pal 5 tidal streams, marginalize over observational uncertainties in the cluster galactocentric positions and velocities, and compare with the observational constraints. We find that the total density and potential of Eris contributed by baryons and dark matter satisfies constraints from the existing Milky Way stellar stream data, as the baryons both round and redistribute the dark matter during the dissipative formation of the galaxy, and provide a centrally concentrated mass distribution that rounds the inner potential. The Eris dark matter halo or a spherical Navarro–Frenk–White dark matter work comparably well in modeling the stream data. In contrast, the equivalent dark matter–only ErisDark simulation produces a prolate halo that cannot reproduce the observed stream data. The ongoing Gaia mission will provide decisive tests of the consistency between and Milky Way streams, and should distinguish between models like Eris and more spherical halos.