Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Elevated CO2 and Temperature on Twospotted Spider Mite (Acari: Tetranychidae) Feeding on Maize1

Abstract Climate models predict that the atmospheric CO2 concentration will double and atmospheric temperatures will increase 1.8–4.0°C globally by the end of this century. These changes will influence the growth of both crop plants and herbivores. The effects of elevated CO2 and temperature on the twospotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae), feeding on maize, Zea mays L. , were examined in environmentally controlled chambers. Elevated CO2 decreased N content and increased total nonstructural carbohydrate (TNC) content and the TNC:N ratio in maize leaves. Developmental time of larvae, protonymphs, deutonymphs, and the entire immature development of T. urticae was reduced at elevated temperatures, while elevated CO2 slightly mitigated the effects of elevated temperature on developmental time. The fecundity of T. urticae declined under elevated CO2 and temperature. Tetranychus urticae had lower net reproductive rate and shorter generation time at elevated temperatures, regardless of CO2 level. These results indicate that elevated CO2-mediated decrease in nutritional quality of maize leaves extends the development time of T. urticae. Taken together, with the increase of CO2 and temperature, the growth and development was accelerated for T. urticae feeding on maize, but its fecundity declined, which will decrease the T. urticae damage to maize for each generation. However, the number of generations will increase in high accumulative temperature regions, which may exacerbate T. urticae damage to maize. Thus, more integrated studies and realistic experimental designs are needed to predict the maize–T. urticae interaction under future climate change scenarios.
Endophytes in maize (Zea mays) in New Zealand

Endophytes in Maize (Zea mays) in New Zealand by Jennifer Joy Brookes The aim of this study was to isolate fungal endophytes from maize in New Zealand (NZ) and to select endophytes with potential to reduce insect pests and/or plant diseases. Culture methods were used to isolate 322 isolates of fungi belonging to four phyla from maize (Zea mays L.) plants. Plants were sampled over two growing seasons (2014 and 2015) in two regions of NZ. Morphological and molecular (ITS rDNA sequencing) techniques were used to identify the fungi. The most common genera recovered were Fusarium, followed by Alternaria, Trichoderma, Epicoccum, Mucor, Penicillium and Cladosoprium spp. Of the Acomycota isolates, 33 genera from 6 classes were recovered. Basidiomyctes were represented by two classes and Zycomycota by one class and a superphylum, Heterokonta, was represented with one class. To determine fungi with potential as biocontrol agents, several assay approaches were taken. Initially, most fungi were used to challenge a plant pathogen on media plates in dual culture experiments. This allowed selection of 21 promising isolates which were inoculated into maize plants by seed coating, then used in plant disease assay and a caterpillar feeding challenge assay. A final eight isolates were selected as the most promising for conferring beneficial traits on plants: Sordaria fimicola, Mucor racemosus, Mucor fragilis, Trichoderma atroviride, Penicillium brasilianum, Fusarium equiseti, Fusarium acuminatum and Fusarium proliferatum. These isolates have shown potential as BCAs against disease and/or insects in laboratory assays.
Unmasking Contextual Stereotypes: Measuring and Mitigating BERT’s Gender Bias

Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that our method of measuring bias is appropriate for languages such as English, but not for languages with a rich morphology and gender-marking, such as German. Our results highlight the importance of investigating bias and mitigation techniques cross-linguistically,especially in view of the current emphasis on large-scale, multilingual language models.
Geometric Scattering Attention Networks

Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning. However, scattering often relies on handcrafted design, requiring careful selection of frequency bands via a cascade of wavelet transforms, as well as an effective weight sharing scheme to combine low- and band-pass information. Here, we introduce a new attention-based architecture to produce adaptive task-driven node representations by implicitly learning node-wise weights for combining multiple scattering and GCN channels in the network. We show the resulting geometric scattering attention network (GSAN) outperforms previous networks in semi-supervised node classification, while also enabling a spectral study of extracted information by examining node-wise attention weights.
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting

Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind calibration method for 2-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimally-weighted LS joint estimation of the sensors' gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to K-level arrays (K > 2), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation.
Fixed Points of Semi-free G-CW-complex with Prescribed Homotopy Type

Smith theory says that the fixed point of a semi-free action of a group G on a contractible space is Z_p-acyclic for any prime factor p of G. Jones proved the converse of Smith theory for the case G is a cyclic group acting on finite CW-complexes. We extend the theory to semi-free group action on finite CW-complexes of given homotopy type, in various settings. In particular, the converse of Smith theory holds if and only if certain K-theoretical obstruction vanishes. We also give some examples that show the effects of different types of the K-theoretical obstruction.
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over $14000$ models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties "encouraged" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered "disentangled" and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.
The Occurrence of Rocky Habitable-zone Planets around Solar-like Stars from Kepler Data

We present the occurrence rates for rocky planets in the habitable zones (HZs) of main-sequence dwarf stars based on the Kepler DR25 planet candidate catalog and Gaia-based stellar properties. We provide the first analysis in terms of star-dependent instellation flux, which allows us to track HZ planets. We define η⊕ as the HZ occurrence of planets with radii between 0.5 and 1.5 R⊕ orbiting stars with effective temperatures between 4800 and 6300 K. We find that η⊕ for the conservative HZ is between (errors reflect 68% credible intervals) and planets per star, while the optimistic HZ occurrence is between and planets per star. These bounds reflect two extreme assumptions about the extrapolation of completeness beyond orbital periods where DR25 completeness data are available. The large uncertainties are due to the small number of detected small HZ planets. We find similar occurrence rates between using Poisson likelihood Bayesian analysis and using Approximate Bayesian Computation. Our results are corrected for catalog completeness and reliability. Both completeness and the planet occurrence rate are dependent on stellar effective temperature. We also present occurrence rates for various stellar populations and planet size ranges. We estimate with 95% confidence that, on average, the nearest HZ planet around G and K dwarfs is ∼6 pc away and there are ∼4 HZ rocky planets around G and K dwarfs within 10 pc of the Sun.
Quantum algorithms for the polynomial eigenvalue problems

Polynomial eigenvalue problems (PEPs) arise in a variety of science and engineering applications, and many breakthroughs in the development of classical algorithms to solve PEPs have been made in the past decades. Here we attempt to solve PEPs in a quantum computer. Firstly, for generalized eigenvalue problems (GEPs) $A\x = \lambda B\x$ with $A,B$ symmetric, and $B$ positive definite, we give a quantum algorithm based on block-encoding and quantum phase estimation. In a more general case when $B$ is invertible, $B^{-1}A$ is diagonalizable and all the eigenvalues are real, we propose a quantum algorithm based on the Fourier spectral method to solve ordinary differential equations (ODEs). The inputs of our algorithms can be any desired states, and the outputs are superpositions of the eigenpairs. The complexities are polylog in the matrix size and linear in the precision. The dependence on precision is optimal. Secondly, we show that when $B$ is singular, any quantum algorithm uses at least $\Omega(\sqrt{n})$ queries to compute the eigenvalues, where $n$ is the matrix size. Thirdly, based on the linearization method and the connection between PEPs and higher-order ODEs, we provide two quantum algorithms to solve PEPs by extending the quantum algorithm for GEPs. We also give detailed complexity analysis of the algorithm for two special types of quadratic eigenvalue problems that are important in practice. Finally, under an extra assumption, we propose a quantum algorithm to solve PEPs when the eigenvalues are complex.
Analytic formulae for random walks on stochastic uniform growth trees

Random walk, as a representation depicting discrete-time unbiased Markov process, has attracted increasing attention in the past. The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time, denote by $\overline{\mathcal{F}}$. As known, the commonly-utilized methods for determining $\overline{\mathcal{F}}$ are mainly based on Laplacian spectra on networks under consideration. On the other hand, methods of this type can become prohibitively complicated and even fail to work in the worst case where the corresponding Laplacian matrix is difficult to describe in the first place. In this paper, we will propose an effective approach to addressing this kind of issues on some tree networks, such as, Vicsek fractal, with intriguing structural properties, for instance, fractal feature. As opposed to most of previous work focusing on estimating $\overline{\mathcal{F}}$ on growth trees that share deterministic structure, our goal is to consider stochastic cases where probability is introduced into the process of growing trees. To this end, we first build up a general formula between Wiener index, denoted by $\mathcal{W}$, and $\overline{\mathcal{F}}$ on a tree. This enables us to convert issues to answer into calculation of $\mathcal{W}$ on networks in question, which helps us gain what we are seeking for. Additionally, it is straightforward to obtain Kirchhoff index on our tree networks using the approach proposed instead of spectral technique. As an immediate consequence, the previously published results in deterministic cases are easily covered by formulae established in this paper. Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
The Isometric Embedding Problem in a Null Cone.

In the first part of this paper, we observe the openness part of the continuity argument, as applied to the Weyl problem by Li-Wang, holds in an arbitrary ambient geometry. We also partially generalize the argument to $\mathbb{S}^n$, showing that an arbitrary metric perturbation can be isometrically embedded up to a solution of the homogenous Codazzi equation. In the second part, we study these results within an ambient Null Cone. Firstly, given a path of metrics on $\mathbb{S}^2$ and an initial isometric embedding, we develop a small parameter existence and uniqueness theorem for paths of isometric embeddings. Then, after imposing asymptotic decay conditions on the Null Cone, we show that any metric on $\mathbb{S}^2$ can be isometrically embedded up to a scaling factor. Finally, we prove the existence of a foliation in a neighborhood of infinity.
Hierarchical Gaussian Processes with Wasserstein-2 Kernels

We investigate the usefulness of Wasserstein-2 kernels in the context of hierarchical Gaussian Processes. Stemming from an observation that stacking Gaussian Processes severely diminishes the model's ability to detect outliers, which when combined with non-zero mean functions, further extrapolates low variance to regions with low training data density, we posit that directly taking into account the variance in the computation of Wasserstein-2 kernels is of key importance towards maintaining outlier status as we progress through the hierarchy. We propose two new models operating in Wasserstein space which can be seen as equivalents to Deep Kernel Learning and Deep GPs. Through extensive experiments, we show improved performance on large scale datasets and improved out-of-distribution detection on both toy and real data.
Effects of line orientation in visual evoked potentials. Spatial dynamics, and gender differences of neural oblique effect

The aim of the study was to investigate neural mechanisms of orientation discrimination in humans by recording the 128-channel event-related potentials (ERPs) of the brain. Stimuli were black-and-white gratings having four orientations: horizontal, vertical, and oblique 45 and 135 deg. from vertical. Thirty-five healthy subjects (16 males, 19 females) participating in the experiments were asked to differentiate the orientations and push the button. The results showed that the orientation processing consists of two main stages. In the occipital, parietal and temporal areas the P100 and N150 components reflecting the early perceptual stage of processing showed amplitudes greater for oblique orientations than for cardinal ones - “inverse” oblique effect. Further, in the central-parietal and frontal areas the P300 and N400 components reflecting the later cognitive stage of processing showed the “classic” oblique effect with greater amplitudes for cardinal orientations. Females demonstrated lower ERP amplitudes and less pronounced differences between responses elicited by cardinal and oblique orientations. The results suggest that in humans the orientation discrimination occurs in a distributed neural network involving the early sensory as well as higher anterior cortical areas. The classic behavioral oblique effect might be controlled by later processing in the anterior cortical areas. The insufficient neural orientation selectivity observed in females may be considered as the biological basis of their poorer visuospatial abilities.
Diffusive coupling of two well-mixed compartments elucidates elementary principles of protein-based pattern formation

Spatial organization of proteins in cells is important for many biological functions. In general, the nonlinear, spatially coupled models for protein-pattern formation are only accessible to numerical simulations, which has limited insight into the general underlying principles. To overcome this limitation, we adopt the setting of two diffusively coupled, well-mixed compartments that represents the elementary feature of any pattern---an interface. For intracellular systems, the total numbers of proteins are conserved on the relevant timescale of pattern formation. Thus, the essential dynamics is the redistribution of the globally conserved mass densities between the two compartments. We present a phase-portrait analysis in the phase-space of the redistributed masses that provides insights on the physical mechanisms underlying pattern formation. We demonstrate this approach for several paradigmatic model systems. In particular, we show that the pole-to-pole Min oscillations in Escherichia coli are relaxation oscillations of the MinD polarity orientation. This reveals a close relation between cell polarity oscillatory patterns in cells. Critically, our findings suggest that the design principles of intracellular pattern formation are found in characteristic features in these phase portraits (nullclines and fixed points) rather than the topology of the protein-interaction networks.
Micro Stripes Analyses for Iris Presentation Attack Detection

Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our Presentation Attack Detection (PAD) network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art (SoTA) algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations.
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP). II. Survey Overview: A First Look at 1.3 mm Continuum Maps and Molecular Outflows

Planck Galactic Cold Clumps (PGCCs) are considered to be the ideal targets to probe the early phases of star formation. We have conducted a survey of 72 young dense cores inside PGCCs in the Orion complex with the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.3 mm (band 6) using three different configurations (resolutions ∼0.″35, 1.″0, and 7.″0) to statistically investigate their evolutionary stages and substructures. We have obtained images of the 1.3 mm continuum and molecular line emission (12CO, and SiO) at an angular resolution of ∼0. ″35 (∼140 au) with the combined arrays. We find 70 substructures within 48 detected dense cores with median dust mass ∼0.093 M⊙ and deconvolved size ∼0. ″27. Dense substructures are clearly detected within the central 1000 au of four candidate prestellar cores. The sizes and masses of the substructures in continuum emission are found to be significantly reduced with protostellar evolution from Class 0 to Class I. We also study the evolutionary change in the outflow characteristics through the course of protostellar mass accretion. A total of 37 sources exhibit CO outflows, and 20 (>50%) show high-velocity jets in SiO. The CO velocity extents (ΔVs) span from 4 to 110 km s−1 with outflow cavity opening angle width at 400 au ranging from [Θobs]400 ∼ 0. ″6–3. ″9, which corresponds to 33. °4–125. °7. For the majority of the outflow sources, the ΔVs show a positive correlation with [Θobs]400, suggesting that as protostars undergo gravitational collapse, the cavity opening of a protostellar outflow widens and the protostars possibly generate more energetic outflows.
Graph Contrastive Learning with Adaptive Augmentation

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the agreement of representations in the two views. Despite the prosperous development of graph CL methods, the design of graph augmentation schemes—a crucial component in CL—remains rarely explored. We argue that the data augmentation schemes should preserve intrinsic structures and attributes of graphs, which will force the model to learn representations that are insensitive to perturbation on unimportant nodes and edges. However, most existing methods adopt uniform data augmentation schemes, like uniformly dropping edges and uniformly shuffling features, leading to suboptimal performance. In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topology level, we design augmentation schemes based on node centrality measures to highlight important connective structures. On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information. We perform extensive experiments of node classification on a variety of real-world datasets. Experimental results demonstrate that our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts, which validates the effectiveness of the proposed contrastive framework with adaptive augmentation.
Gravitino thermal production revisited

We calculate the gravitino production rate, computing its one-loop thermal self-energy. Gravitino production processes that do not result through thermal cuts of its self-energy, have been identified and taken into account. Correcting analytical errors and numerical approximations in the previous calculations, we present our result. This deviates from the latest estimation by almost 10\%. More importantly, we provide a convenient formula, for calculating the gravitino production rate and its thermal abundance, as a function of the reheating temperature of the Universe.