Use of X-ray to identify contaminants in pelleted seed lots for biosecurity : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science (Agricultural Science) at Massey University, Manawatū, New Zealand

The following Figures were removed for copyright reasons, but may be accessed via their respective source: Figs 16 & 17 (=Landis & Keane, 2010, Figs 1 & 3) and Figs 18-24 (=Blott & Pye, 2008, Figs 1, 8, 9, 14, 15, 16, 17).Thousands of tonnes of seed, of which around 10% is pelleted, comes into New Zealand through international trade every year. However, this trade also brings potential risks to New Zealand biosecurity. Pelleted seeds can contain contaminants, including seeds other than the crop species in the seed lot and inert matter; both may cause negative effects on crop growth or bring pests and diseases. A reliable method is necessary to inspect seed lots for the contaminants. The conventional way to inspect for contaminants in pelleted seeds is to separate the seeds from pellets and inspect visually. However, this is a time consuming and potentially health damaging procedure. A faster and safer non-invasive inspection method is needed urgently. X-ray imaging systems have the potential to non-invasively identify contaminants in seed lots. 2-D X-ray was firstly applied in this research to determine if the system could separate non-target seeds such as weed seed from naked crop “target” seeds, since if 2-D X-ray cannot separate non-target seeds from naked target seeds, there is little chance to separate seeds that are pelleted. In this research, three target species were used. These were beet (Rapistrum, Ranunculus and spinach as contaminants), carrot (Polygonum, Chenopodium and Solanum as contaminants) and lettuce (Sonchus and Lapsana as contaminants), because of their high contamination rates in imported seed lots. Seed shape parameters: dimensions, form, circularity, roughness and intensity, were used to characterize seeds for further comparison. The results showed Ranunulus can be separated from beet by dimensions and intensity; Rapistrum can be separated by elongation, circularity and intensity; spinach was hard to separate from beet. In the carrot group, Chenopodium and Solanum can be separated from carrot by either dimensions, elongation or circularity, while Polygonum cannot be separated from Carrot. For contaminants in lettuce, Sonchus can be separated from lettuce by dimensions and intensity; Lapsana can be separated by elongation and circularity. However, all the separation above was based on mean values, seeds with extreme sizes would limit the effects of shape parameters in seed separation. Determining if pelleting seeds can also be separated using the same parameters was the next important step for determining if 2-D X-ray can be used for pelleted seed inspection. However, little literature can be found regarding specific pelleting materials and pelleting procedures, as they are held by the seed companies. Therefore, protocols for pelleting the relatively small numbers of pelleted seed for research are needed. During several trials on seed pelleting, Methocel™ and gypsum was identified as suitable pelleting materials. The vortex mixer was identified as the best equipment for pelleting using a one-by-one adding method, which was feasible for pelleting both tiny-seeds and small-quantities seeds. The seeds pelleted showed a uniform and well-rounded appearance. However, when applying the same 2-D X-ray for seed separation, the seed projections were hard to be extracted for further analysis, because of the poor differentiation between seeds and pellets. This research explored the potential of using 2-D X-ray to separate naked non-target seed from naked target seeds by seed shape parameters. The outcomes confirmed that the mean values of shape parameters can separate contaminants from target seeds, however at the extreme ends of the range seed parameters overlap will limit the value of the shape parameters. Pelleting seeds under laboratory conditions can also be realized by using vortex mixer as equipment and using Methocel™ and gypsum as pelleting materials. Nonetheless, 2-D X-ray was not a reliable tool to detect pelleted seeds, since it is hard to separate seed projections from pellets with images only from a top view. 3-D X-ray could potentially be applied in future research because of its higher resolution than 2-D X-ray. In addition, 3-D X-ray images enable analysts to analyze seeds from different angles other than one fixed angle, which makes the analysis free from image overlap problems. Although research on 3-D X-ray for seed separation is at its beginning, it is potentially useful for pelleted seed analysis

WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability

Transformer and its variants are fundamental neural architectures in deep learning. Recent works show that learning attention in the Fourier space can improve the long sequence learning capability of Transformers. We argue that wavelet transform shall be a better choice because it captures both position and frequency information with linear time complexity. Therefore, in this paper, we systematically study the synergy between wavelet transform and Transformers. We propose Wavelet Space Attention (WavSpA) that facilitates attention learning in a learnable wavelet coefficient space which replaces the attention in Transformers by (1) applying forward wavelet transform to project the input sequences to multi-resolution bases, (2) conducting attention learning in the wavelet coefficient space, and (3) reconstructing the representation in input space via backward wavelet transform. Extensive experiments on the Long Range Arena demonstrate that learning attention in the wavelet space using either fixed or adaptive wavelets can consistently improve Transformer's performance and also significantly outperform learning in Fourier space. We further show our method can enhance Transformer's reasoning extrapolation capability over distance on the LEGO chain-of-reasoning task

N=2\mathcal{N} = 2 Schur index and line operators

4d $\mathcal{N} = 2$ SCFTs and their invariants can be often enriched by non-local BPS operators. In this paper we study the flavored Schur index of several types of N = 2 SCFTs with and without line operators, using a series of new integration formula of elliptic functions and Eisenstein series. We demonstrate how to evaluate analytically the Schur index for a series of $A_2$ class-$\mathcal{S}$ theories and the $\mathcal{N} = 4$ SO(7) theory. For all $A_1$ class-$\mathcal{S}$ theories we obtain closed-form expressions for SU(2) Wilson line index, and 't Hooft line index in some simple cases. We also observe the relation between the line operator index with the characters of the associated chiral algebras. Wilson line index for some other low rank gauge theories are also studied.Comment: 72 pages, 9 figures, 5 table

A New Species of Genus Microhyla (Amphibia: Anura: Microhylidae) from Zhejiang Province, China

We described a new species, Microhyla beilunensis sp. nov., from Zhejiang Province of China. Phylogenetic analyses based on the mitochondrial 12S, 16S and CO1 gene sequences suggested that the new taxon was distinctly separated from its congeners and closed to M. mixtura and M. okinavensis. Morphologically, the new species could be identified from its congeners except M. mixtura by several characters: (1) rudimentary webs on toe base; (2) absence of disks and dorsal median longitudinal grooves on finger tips; (3) presence of disks and dorsal median longitudinal grooves on toe tips. As well, the new species could be identified from topotype M. mixtura by the combination of characters: (1) apart from the stripes, bar-shaped and oval-shaped patterns, the rounded spots present on the dorsum of body and legs; (2) the outer metacarpal tubercles prominently larger than the inner one; (3) of males, the ratios of HW, IND, UEW and LAW to SVL of the new species were significantly larger than those of M. mixtura (P < 0.01), and the ratios of SL, IOD, LAHL, HLL, TL, TFL and FL to SVL of the new species were significantly less than those of M. mixtura (P < 0.05)

Summer extreme consecutive dry days over Northeast China in the changing climate: Observed features and projected future changes based on CESM-LE

Northeast China (NEC) is a major crop base in East Asia, and summer drought is one of the climate extremes that significantly influences NEC agricultural production. Therefore, understanding the response of NEC summer drought to global warming is of significance. In this study, based on observation and large-ensemble simulations of the Community Earth System Model (CESM-LE), the variabilities in summer extreme consecutive dry days (CDDs) over NEC are investigated in the present and future climate. In the observation, the NEC summer extreme CDDs showed an increasing trend during the past half century and experienced a significant interdecadal change around the middle 1990s, which is mainly due to the change in the anticyclone over Lake Baikal-Northeast Asia. The anticyclone-related anomalous downward motion and moisture divergence provided favorable conditions for increased summer CDDs over NEC. The CESM-LE multimember ensemble (MME) simulation could reproduce the change in NEC summer extreme CDDs and its related atmospheric circulations, indicating that the observed change in NEC summer extreme CDDs could be largely contributed by anthropogenic forcing. In the future warmer climate, the NEC summer extreme CDDs are projected to show interdecadal variability, which increase by approximately 6.7% in the early 21st century (2020–2030), then decrease by approximately 0.3% in the middle to late 21st century (2040–2080), and further increase by approximately 2.1% in the late 21st century (2085–2100). In addition, the projected changes in the anticyclone over Lake Baikal-Northeast Asia show a similar feature to that of the NEC summer extreme CDDs, which might further provide some confidence in the projection of the NEC summer extreme CDDs due to the physical connection between CDDs and anticyclone in the future

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent advancement in diffusion models. Diffusion models are one type of generative models. At their core, they learn an iterative denoising process that gradually maps a noisy image closer to their training manifolds. LMD leverages this intuition for OOD detection. Specifically, LMD lifts an image off its original manifold by corrupting it, and maps it towards the in-domain manifold with a diffusion model. For an out-of-domain image, the mapped image would have a large distance away from its original manifold, and LMD would identify it as OOD accordingly. We show through extensive experiments that LMD achieves competitive performance across a broad variety of datasets. Code can be found at https://github.com/zhenzhel/lift_map_detect.Comment: ICML 202

VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize per-scene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRDF values of sampled points on a ray and then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3D benchmark