Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in bacterial community of soil induced by long-term straw returning.

Straw returning is an effective way to improve soil quality. Whether the bacterial community development has been changed by long-term straw returning in non-calcareous soil is not clear. In this study, the following five treatments were administered: soil without fertilizer (CK); wheat and corn straw returning (WC); wheat straw returning with 276 kg N ha−1 yr−1 (WN); manure, 60,000 kg ha−1 pig manure compost (M) and wheat and corn straw returning with 276 kg N ha−1 yr−1 (WCN). The high-throughput 16S rRNA sequencing technology was used to evaluate the bacterial communities. The results showed that the community was composed mostly of two dominant groups (Proteobacteria and Acidobacteria). Bacterial diversity increased after the application of straw and manure. Principal component analyses revealed that the soil bacterial community differed significantly between treatments. The WCN treatment showed relatively higher total soil N, available P, available K, and organic carbon and invertase, urease, cellulase activities and yield than the WC treatment. Our results suggested that application of N fertilizer to straw returning soil had significantly higher soil fertility and enzyme activity than straw returning alone, which resulted in a different bacterial community composition, Stenotrophomonas, Pseudoxanthomonas, and Acinetobacter which were the dominant genera in the WC treatment while Candidatus, Koribacter and Granulicella were the dominant genera in the WCN treatment. To summarize, wheat and maize straw returning with N fertilizer would be the optimum proposal for improving soil quality and yield in the future in non-calcareous fluro-acquic-wheat and maize cultivated soils in the North China Plain in China.
Fast Joint Bilateral Upsampling using Color Edge Information

In this paper, we propose a new depth image upsampler to increase the depth image resolution fast while protecting edge information. We use a Canny edge detector for reduced computational complexity. Joint bilateral upsampler (JBU) increases the resolution of a depth image considering the photometric property of corresponding high-resolution color image. The JBU uses both a spatial weighting function and a color weighting functions evaluated on the data values. In the proposed upsampler, a color weighting function is chosen by edge existence. Experimental results show that the proposed upsampler out performs JBU, in terms of a trade-off between depth image quality and computational complexity.
NIKA 150 GHz polarization observations of the Crab nebula and its spectral energy distribution

The Crab nebula is a supernova remnant exhibiting a highly polarized synchrotron radiation at radio and millimetre wavelengths. It is the brightest source in the microwave sky with an extension of 7 by 5 arcmin, and is commonly used as a standard candle for any experiment which aims to measure the polarization of the sky. Though its spectral energy distribution has been well characterized in total intensity, polarization data are still lacking at millimetre wavelengths. We report in this paper high resolution observations (18′′ FWHM) of the Crab nebula in total intensity and linear polarization at 150 GHz with the NIKA camera. NIKA, operated at the IRAM 30 m telescope from 2012 to 2015, is a camera made of Lumped Element Kinetic Inductance Detectors (LEKIDs) observing the sky at 150 and 260 GHz. From these observations we are able to reconstruct the spatial distribution of the polarization degree and angle of the Crab nebula, which is found to be compatible with previous observations at lower and higher frequencies. Averaging across the source and using other existing data sets we find that the Crab nebula polarization angle is consistent with being constant over a wide range of frequencies with a value of − 87.7° ± 0.3 in Galactic coordinates. We also present the first estimation of the Crab nebula spectral energy distribution polarized flux in a wide frequency range: 30–353 GHz. Assuming a single power law emission model we find that the polarization spectral index βpol = – 0.347 ± 0.026 is compatible with the intensity spectral index β = – 0.323 ± 0.001.
Strengthening ethical community engagement in contemporary Malawi

Although community engagement is increasingly promoted in global health research to improve ethical research practice, there is sometimes a disconnect between the broader moral ambitions for community engagement in the literature and guidelines on the one hand and its rather narrower practical application in health research on the other. In practice, less attention is paid to engaging communities for the ‘intrinsic’ value of showing respect and ensuring inclusive participation of community partners in research design. Rather, more attention is paid to the use of community engagement for ‘instrumental’ purposes to improve community understanding of research and ensure successful study implementation. Against this backdrop, we reviewed the literature and engaged various research stakeholders at a workshop to discuss ways of strengthening ethical engagement of communities and to develop context-relevant guidelines for community engagement in health research in Malawi.
Comparitive phytochemical Analysis and Resilience Pattern Exhibited by thyme and tea tree oil against Selected Poultry Isolates

The volatile oils of Melaleuca alternifolia (tea tree) and Thymus vulgaris (thyme), of the family Myrtaceae and Lamiaceae respectively, were asessed for antibacterial activity against different avian pathogens isolated from infected chickens which involved Escherichia coli, Staphylococcus aureus, S.epidermidis, S. carnosus and Salmonella gallinarum. The antimicrobial activity of both the oils was assessed by using disc diffusion, minimum inhibitory concentration (MIC) as well as minimum bactericidal concentration (MBC) method. Thyme oil exhibited higher activity than tea tree oil against all the pathogens. Mean inhibition zones, MIC and MBC values of bacterial strains varied from 19 and 34 mm, 0.03%- 0.15% to 0.07 - 0.3% respectively. GC-MS analysis of essential oil of thyme showed the presence of 13 components. The major components were carvacrol, thymol, terpinene-4-ol, α- Terpinene, carvacrol methyl ether. Tea tree oil showed the presence of 16 components upon GC-MS analysis. The major compounds were Limonene, γ -Terpinene, α- Terpinene, Cineol and α- Terpinolene. Since aromatic and medicinal plants produce a wide variety of volatile terpenes, hydrocarbons (aliphatic and cyclic) and their corresponding oxygenated isoprenoid derivatives and analogues, the bactericidal effect observed in this study may be attributed to these components. Therefore, these oils could be a source of pharmaceutical materials required for the preparation of new therapeutic and antimicrobial agents.
The Informational Dynamics of Mean‒Variance Relationships in Fertilizer Markets: An Entropic Investigation

The risk‒return trade-off is a fundamental relationship that has received a large amount of attention in financial and economic analysis. Indeed, it has important implications for understanding linear dynamics in price returns and active quantitative portfolio optimization. The main contributions of this work include, firstly, examining such a relationship in five major fertilizer markets through different time periods: a period of low variability in returns and a period of high variability such as that during which the recent global financial crisis occurred. Secondly, we explore how entropy in those markets varies during the investigated time periods. This requires us to assess their inherent informational dynamics. The empirical results show that higher volatility is associated with a larger return in diammonium phosphate, potassium chloride, triple super phosphate, and urea market, but not rock phosphate. In addition, the magnitude of this relationship is low during a period of high variability. It is concluded that key statistical patterns of return and the relationship between return and volatility are affected during high variability periods. Our findings indicate that entropy in return and volatility series of each fertilizer market increase significantly during time periods of high variability.
A FORMULATION OF THE FAST MULTIPOLE BOUNDARY ELEMENT METHOD (FMBEM) FOR ACOUSTIC RADIATION AND SCATTERING FROM THREE-DIMENSIONAL STRUCTURES

Compared to the traditional boundary element method (BEM), the single level fast multipole boundary element method (SLFMBEM) or the multilevel fast multipole boundary element method (MLFMBEM) reduces the computational complexity of a job from O(n2) to O(n3/2) or O(n log2n), respectively with n being the number of unknowns; this means a dramatical reduction in terms of CPU-time and storage requirement. Large scale problems, unsolvable with the traditional BEM, can be solved by using the FMBEM. In this paper, the traditional BEM, SLFMBEM, and MLFMBEM are formulated within the framework of the Burton–Miller Collocation BEM for acoustic radiation and scattering from 3D structures. Attention is especially paid to the practical aspects of the method in order to get a reliable and efficient computation code. The performance of the method is tested with practical examples, including one for computing the head-related transfer function (HRTF) between 1000 and 18 000 Hz.
ApproxJoin: Approximate Distributed Joins

A distributed join is a fundamental operation for processing massive datasets in parallel. Unfortunately, computing an equi-join over such datasets is very resource-intensive, even when done in parallel. Given this cost, the equi-join operator becomes a natural candidate for optimization using approximation techniques, which allow users to trade accuracy for latency. Finding the right approximation technique for joins, however, is a challenging task. Sampling, in particular, cannot be directly used in joins; naïvely performing a join over a sample of the dataset will not preserve statistical properties of the query result. To address this problem, we introduce ApproxJoin. We interweave Bloom filter sketching and stratified sampling with the join computation in a new operator that preserves statistical properties of an aggregation over the join output. ApproxJoin leverages Bloom filters to avoid shuffling non-joinable data items around the network, and then applies stratified sampling to obtain a representative sample of the join output. We implemented ApproxJoin in Apache Spark, and evaluated it using microbenchmarks and real-world workloads. Our evaluation shows that ApproxJoin scales well and significantly reduces data movement, without sacrificing tight error bounds on the accuracy of the final results. ApproxJoin achieves a speedup of up to 9x over unmodified Spark-based joins with the same sampling ratio. Furthermore, the speedup is accompanied by a significant reduction in the shuffled data volume, which is up to 82x less than unmodified Spark-based joins.
Human Centered Robotics : Designing Valuable Experiences for Social Robots

As robots get more capable and begin to leave the lab and enter the real world, it is becoming increasingly important to ensure that we are building applications that solve real world problems and provide valuable experiences to their users. Designing for social robots presents many challenges, including identifying a valuable use case, dealing with limitations of robot capabilities and setting expectations for our users. Additionally, designers need to consider multiple methods of interaction. These include voice interfaces, gestures and other methods of perception and input that may be available on the robot. From a user perspective, many of these elements of the interface are not yet familiar. Therefore designers need to consider interaction with social robots by supporting familiar cognitive models, while also helping to build new ones. This paper explores various design tools and methods that can be used in the area of social robot design to encourage consideration of the target user in the process. Principles for Human-Centered Robotics are presented and an overview of some collaborative design activities are provided that can be applied to the design of social robots. To provide context to these activities, examples are given of how some of these methods have been used during the development of commercial applications for SoftBank’s robots.
Clarifying the “A” in CAI for Learners of Different Abilities

The study investigated the degree to which students of different abilities acquired strategic planning knowledge from an intellectual computer game. The relationships among ability and student performance in instruction were examined. Students learned a computer problem solving game under two forms of instruction. Performance was monitored interactively. Results showed more successful students 1) acquired strategic planning knowledge by induction from examples and 2) also performed better on the transfer tasks. Ability differences were found. Considerations for further investigation of the relationships among learner characteristics and instructional variations in computer learning are discussed. Implications for the use of cognitively engaging software in educational settings also are addressed.
How to tell when a clustering is (approximately) correct using convex relaxations

We introduce the Sublevel Set (SS) method, a generic method to obtain sufficient guarantees of near-optimality and uniqueness (up to small perturbations) for a clustering. This method can be instantiated for a variety of clustering loss functions for which convex relaxations exist. Obtaining the guarantees in practice amounts to solving a convex optimization. We demonstrate the applicability of this method by obtaining distribution free guarantees for K-means clustering on realistic data sets.
Chesson's coexistence theory

We give a comprehensive review of Chesson's coexistence theory, summarizing, for the first time, all its fundamental details in one single document. Our goal is for both theoretical and empirical ecologists to be able to use the theory to interpret their findings, and to get a precise sense of the limits of its applicability. To this end, we introduce an explicit handling of limiting factors, and a new way of defining the scaling factors that partition invasion growth rates into the different mechanisms contributing to coexistence. We explain terminology such as relative nonlinearity, storage effect, and growth‐density covariance, both in a formal setting and through their biological interpretation. We review the theory's applications and contributions to our current understanding of species coexistence. While the theory is very general, it is not well suited to all problems, so we carefully point out its limitations. Finally, we critique the paradigm of decomposing invasion growth rates into stabilizing and equalizing components: we argue that these concepts are useful when used judiciously, but have often been employed in an overly simplified way to justify false claims.
Direct Shape Regression Networks for End-to-End Face Alignment

Face alignment has been extensively studied in computer vision community due to its fundamental role in facial analysis, but it remains an unsolved problem. The major challenges lie in the highly nonlinear relationship between face images and associated facial shapes, which is coupled by underlying correlation of landmarks. Existing methods mainly rely on cascaded regression, suffering from intrinsic shortcomings, e.g., strong dependency on initialization and failure to exploit landmark correlations. In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework. Specifically, by deploying doubly convolutional layer and by using the Fourier feature pooling layer proposed in this paper, DSRN efficiently constructs strong representations to disentangle highly nonlinear relationships between images and shapes; by incorporating a linear layer of low-rank learning, DSRN effectively encodes correlations of landmarks to improve performance. DSRN leverages the strengths of kernels for nonlinear feature extraction and neural networks for structured prediction, and provides the first end-to-end learning architecture for direct face alignment. Its effectiveness and generality are validated by extensive experiments on five benchmark datasets, including AFLW, 300W, CelebA, MAFL, and 300VW. All empirical results demonstrate that DSRN consistently produces high performance and in most cases surpasses state-of-the-art.
Learning Compressed Transforms with Low Displacement Rank

The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual. Existing use of LDR matrices in deep learning has applied fixed displacement operators encoding forms of shift invariance akin to convolutions. We introduce a rich class of LDR matrices with more general displacement operators, and explicitly learn over both the operators and the low-rank component. This class generalizes several previous constructions while preserving compression and efficient computation. We prove bounds on the VC dimension of multi-layer neural networks with structured weight matrices and show empirically that our compact parameterization can reduce the sample complexity of learning. When replacing weight layers in fully-connected, convolutional, and recurrent neural networks for image classification and language modeling tasks, our new classes exceed the accuracy of existing compression approaches, and on some tasks even outperform general unstructured layers while using more than 20X fewer parameters.
An Analytical framework for examining changes in performance measurement systems within the banking sector

Drawing on institutional theory, more specifically DiMaggio & Powell’s (1983) notion of institutional isomorphism, and Oliver’s (1991) typology of strategic responses to institutional pressures, this paper develops an analytical framework to examine the factors that influence organisations to change their performance measurement systems, and the responses to consequential change efforts within the context of the banking industry. The paper suggests that various macro-level environmental factors which affect the functioning of banks (e.g. economic conditions, technological innovations, socio-cultural and political factors) exert pressure (coercive, mimetic and normative) to change their performance measurement practices. The paper proposes that rather than passively conforming to such pressure, banks respond strategically, with the strategic responses taking various forms including non-compliance. The proposed framework could be used by managers and researchers to examine and understand changes in performance measurement systems in banks and to facilitate the effective adoption and implementation of performance measurement systems.
Targeted Stimulation Using Differences in Activation Probability across the Strength–Duration Space

Electrical stimulation is ubiquitous as a method for activating neuronal tissue, but there is still significant room for advancement in the ability of these electrical devices to implement smart stimulus waveform design to more selectively target populations of neurons. The capability of a device to encode more complicated and precise messages to a neuronal network greatly increases if the stimulus input space is broadened to include variable shaped waveforms and multiple stimulating electrodes. The relationship between a stimulating electrode and the activated population is unknown; a priori. For that reason, the population of excitable neurons must be characterized in real-time and for every combination of stimulating electrodes and neuronal populations. Our automated experimental system allows investigation into the stimulus-evoked neuronal response to a current pulse using dissociated neuronal cultures grown atop microelectrode arrays (MEAs). The studies presented here demonstrate that differential activation is achievable between two neurons using either multiple stimulating electrodes or variable waveform shapes. By changing the aspect ratio of a rectangular current pulse; the stimulus activated neurons in the strength–duration (SD) waveform space with differing probabilities. Additionally, in the case when two neuronal activation curves intersect each other in the SD space; one neuron can be selectively activated with short-pulse-width; high-current stimuli while the other can be selectively activated with long-pulse-width; low-current stimuli. Exploring the capabilities and limitations of electrical stimulation allows for improvements to the delivery of stimulus pulses to activate neuronal populations. Many state-of-the-art research and clinical stimulation solutions, including those using a single microelectrode, can benefit from waveform design methods to improve stimulus efficacy. These findings have even greater import into multi-electrode systems because spatially distributed electrodes further enhance accessibility to differential neuronal activation.
Giant components in three-parameter random directed graphs

A three-parameter model of a random directed graph (digraph) is specified by the probability of ‘up arrows' from vertex i to vertex j where i < j, the probability of ‘down arrows' from i to j where i ≥ j, and the probability of bidirectional arrows between i and j. In this model, a phase transition—the abrupt appearance of a giant strongly connected component—takes place as the parameters cross a critical surface. The critical surface is determined explicitly. Before the giant component appears, almost surely all non-trivial components are small cycles. The asymptotic probability that the digraph contains no cycles of length 3 or more is computed explicitly. This model and its analysis are motivated by the theory of food webs in ecology.
Methods and Protocols for Electrochemical Energy Storage Materials Research

We present an overview of the procedures and methods to prepare and evaluate materials for electrochemical cells in battery research in our laboratory, including cell fabrication, two- and three-electrode cell studies, and methodology for evaluating diffusion coefficients and impedance measurements. Informative characterization techniques employed to assess new materials for batteries are also described, including operando XRD, pair-distribution function analysis, X-ray photoelectron spectroscopy, and operando X-ray absorption spectroscopy. Examples of insightful information that each technique has provided in the research areas of Li-S, Na-ion, and Mg batteries are presented along with excellent references for detailed descriptions of the theory, experimental procedures, and various designs, as well as methods for data processing and analysis.
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.