NCSiam: Reliable Matching via Neighborhood Consensus for Siamese-Based Object Tracking.

An essential need for accurate visual object tracking is to capture better correlations between the tracking target and the search region. However, the dominant Siamese-based trackers are limited to producing dense similarity maps at once via a cross-correlations operation, ignoring to remedy the contamination caused by erroneous or ambiguous matches. In this paper, we propose a novel tracker, termed neighborhood consensus constraint-based siamese tracker (NCSiam), which takes the idea of neighborhood consensus constraint to refine the produced correlation maps. The intuition behind our approach is that we can support the nearby erroneous or ambiguous matches by analyzing a larger context of the scene that contains a unique match. Specifically, we devise a 4D convolution-based multi-level similarity refinement (MLSR) strategy. Taking the primary similarity maps obtained from a cross-correlation as input, MLSR acquires reliable matches by analyzing neighborhood consensus patterns in 4D space, thus enhancing the discriminability between the tracking target and the distractors. Besides, traditional Siamese-based trackers directly perform classification and regression on similarity response maps which discard appearance or semantic information. Therefore, an appearance affinity decoder (AAD) is developed to take full advantage of the semantic information of the search region. To further improve performance, we design a task-specific disentanglement (TSD) module to decouple the learned representations into classification-specific and regression-specific embeddings. Extensive experiments are conducted on six challenging benchmarks, including GOT-10k, TrackingNet, LaSOT, UAV123, OTB2015, and VOT2020. The results demonstrate the effectiveness of our method. The code will be available at https://github.com/laybebe/NCSiam.

Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates.

Understanding the biodiversity and genetics of gut microbiomes has important implications for host physiology and industrial enzymes, whereas most studies have been focused on bacteria and archaea, and to a lesser extent on fungi and viruses. One group, still underexplored and elusive, is ciliated protozoa, despite its importance in shaping microbiota populations. Integrating single-cell sequencing and an assembly-and-identification pipeline, we acquired 52 high-quality ciliate genomes of 22 rumen morphospecies from 11 abundant morphogenera. With these genomes, we resolved the taxonomic and phylogenetic framework that revised the 22 morphospecies into 19 species spanning 13 genera and reassigned the genus Dasytricha from Isotrichidae to a new family Dasytrichidae. Comparative genomic analyses revealed that extensive horizontal gene transfers and gene family expansion provided rumen ciliate species with a broad array of carbohydrate-active enzymes (CAZymes) to degrade all major kinds of plant and microbial carbohydrates. In particular, the genomes of Diplodiniinae and Ophryoscolecinae species encode as many CAZymes as gut fungi, and ~80% of their degradative CAZymes act on plant cell-wall. The activities of horizontally transferred cellulase and xylanase of ciliates were experimentally verified and were 2-9 folds higher than those of the inferred corresponding bacterial donors. Additionally, the new ciliate dataset greatly facilitated rumen metagenomic analyses by allowing ~12% of the metagenomic sequencing reads to be classified as ciliate sequences.

Spatially Regularized Structural Support Vector Machine for Robust Visual Tracking.

Structural support vector machine (SSVM) is popular in the visual tracking field as it provides a consistent target representation for both learning and detection. However, the spatial distribution of feature is not considered in standard SSVM-based trackers, therefore leading to limited performance. To obtain a robust discriminative classifier, this paper proposes a novel tracking framework that spatially regularizes SSVM, which yields a new spatially regularized SSVM (SRSSVM). We utilize the spatial regularization prior to penalize the learning classifier with the same size as the target region. The location of classifier spatially located far from the center of region is assigned large weight and vice versa. Then, it is introduced into the SSVM model as a regularization factor to learn the robust discriminative model. Furthermore, an optimizing algorithm with dual coordination descent is presented to efficiently solve the SRSSVM tracking model. Our proposed SRSSVM tracking method has low computational cost like the traditional linear SSVM tracker while can significantly improve the robustness of the discriminative classifier. The experimental results on three popular tracking benchmark data sets show that the proposed SRSSVM tracking method performs favorably against the state-of-the-art trackers.

Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging.

Phenolics contents in wine grapes are key indicators for assessing ripeness. Near-infrared hyperspectral images during ripening have been explored to achieve an effective method for predicting phenolics contents. Principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) models were built, respectively. The results show that SVR behaves globally better than PLSR and PCR, except in predicting tannins content of seeds. For the best prediction results, the squared correlation coefficient and root mean square error reached 0.8960 and 0.1069g/L (+)-catechin equivalents (CE), respectively, for tannins in skins, 0.9065 and 0.1776 (g/L CE) for total iron-reactive phenolics (TIRP) in skins, 0.8789 and 0.1442 (g/L M3G) for anthocyanins in skins, 0.9243 and 0.2401 (g/L CE) for tannins in seeds, and 0.8790 and 0.5190 (g/L CE) for TIRP in seeds. Our results indicated that NIR hyperspectral imaging has good prospects for evaluation of phenolics in wine grapes.

Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging.

The aim of this study was to demonstrate the capability of hyperspectral imaging in predicting anthocyanin content changes in wine grapes during ripening. One hundred twenty groups of Cabernet Sauvignon grapes were collected periodically after veraison. The hyperspectral images were recorded by a hyperspectral imaging system with a spectral range from 900 to 1700 nm. The anthocyanin content was measured by the pH differential method. A quantitative model was developed using partial least squares regression (PLSR) or support vector regression (SVR) for calculating the anthocyanin content. The best model was obtained using SVR, yielding a coefficient of validation (P-R(2)) of 0.9414 and a root mean square error of prediction (RMSEP) of 0.0046, higher than the PLSR model, which had a P-R(2) of 0.8407 and a RMSEP of 0.0129. Therefore, hyperspectral imaging can be a fast and non-destructive method for predicting the anthocyanin content of wine grapes during ripening.