The Role of the Intestinal Flora and Its Derivatives in Neurocognitive Disorders: A Narrative Review from Surgical Perspective.

Perioperative neurocognitive dysfunction is a significant concern for population health, impacting postoperative recovery and increasing the financial burden on patients. With an increasing number of surgical procedures being performed, the prevention and management of perioperative neurocognitive dysfunction have garnered significant attention. While factors such as age, lifestyle, genetics, and education are known to influence the development of cognitive dysfunction, recent research has highlighted the role of the gut microbiota in neurological health.

Exploring sparsity in graph transformers.

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility of sparsifying GTs, a significant yet under-explored topic.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists.

Logical Relation Inference and Multiview Information Interaction for Domain Adaptation Person Re-Identification.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles.

BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing.

Image dehazing aims to remove haze in images to improve their image quality. However, most image dehazing methods heavily depend on strict prior knowledge and paired training strategy, which would hinder generalization and performance when dealing with unseen scenes. In this paper, to address the above problem, we propose Bidirectional Normalizing Flow (BiN-Flow), which exploits no prior knowledge and constructs a neural network through weakly-paired training with better generalization for image dehazing.

Effects of dietary fibre on intestinal microbiota in geese evaluated by 16SrRNA gene sequencing.

Aims: The purpose of the research is to study the effects of different fibre types and sources on the intestinal flora of geese.

Methods And Results: A total of 48 geese (males: 35 days old) were divided into four groups, each of which included three replicates of four geese. Groups 1-4 were fed a diet containing 5% corn stover Crude fibre (CF, the LJ group), 8% corn stover CF (the HJ group), 5% alfalfa CF (the LM group) or 8% alfalfa CF (the HM group), respectively.

A Contour Co-Tracking Method for Image Pairs.

We proposed a contour co-tracking method for co-segmentation of image pairs based on active contour model. Our method comprehensively re-models objects and backgrounds signified by level set functions, and leverages Hellinger distance to measure the similarity between image regions encoded by probability distributions. The main contribution are as follows.

One year of African swine fever outbreak in China.

African swine fever (ASF) is a major threat to domestic pigs and wild boars. Since 2018, ASF outbreaks have been ongoing in China. As of August 3, 2019, a total of 151 ASF clusters of outbreaks reported in China have caused severe economic losses for the industry, the pig farmers and pork producers, due to the lack of an efficacious vaccine.

Image Hallucination From Attribute Pairs.

Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should be provided with sufficient details for realistic hallucination, meaning that longer sentences with rich content are preferred, but on the other hand, the generator is meanwhile fragile to long sentences due to their complex semantics and syntax like long-range dependencies and the combinatorial explosion of object visual features.

Constrained Discriminative Projection Learning for Image Classification.

Projection learning is widely used in extracting discriminative features for classification. Although numerous methods have already been proposed for this goal, they barely explore the label information during projection learning and fail to obtain satisfactory performance. Besides, many existing methods can learn only a limited number of projections for feature extraction which may degrade the performance in recognition.

Domain-Weighted Majority Voting for Crowdsourcing.

Crowdsourcing labeling systems provide an efficient way to generate multiple inaccurate labels for given observations. If the competence level or the "reputation," which can be explained as the probabilities of annotating the right label, for each crowdsourcing annotators is equal and biased to annotate the right label, majority voting (MV) is the optimal decision rule for merging the multiple labels into a single reliable one. However, in practice, the competence levels of annotators employed by the crowdsourcing labeling systems are often diverse very much.

p -Laplacian Regularization for Scene Recognition.

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem.

Response of goose intestinal microflora to the source and level of dietary fiber.

Geese are capable of digesting and making use of a high-fiber diet, but the mechanism is not well understood and would be of great significance for the development and utilization of roughage resources. In this study, we investigated the effect of dietary fiber (source: corn stover and alfalfa, included at 5% or 8%) on microflora in goose intestines. We used 35-day-old Carlos geese in which we first studied the influence of fiber ingestion on diet digestibility and immune organ indices of geese and found that high dietary fiber (8% content) significantly increased feed intake, the digestibility of neutral and acid detergent fiber, and thymus, bursa, and spleen size.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition.

Screening and identification of lignin-degrading bacteria in termite gut and the construction of LiP-expressing recombinant Lactococcus lactis.

Lignin, a common natural polymers, is abundant and complex, and termites can break down and utilize the lignin in their food. In this study an attempt was made to isolate and characterize the lignolytic bacteria from termite (Reticulitermes chinensis Snyder) gut. Two strains (PY12 and MX5) with high lignin peroxidase (LiP) activity were screened using the azure B method.

Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud.

The rapidly increasing number of images on the internet has further increased the need for efficient indexing for digital image searching of large databases. The design of a cloud service that provides high efficiency but compact image indexing remains challenging, partly due to the well-known semantic gap between user queries and the rich semantics of large-scale data sets. In this paper, we construct a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which narrows the semantic gap by combining both attributes and indexing into a single framework.

Person Re-Identification by Dual-Regularized KISS Metric Learning.

Person re-identification aims to match the images of pedestrians across different camera views from different locations. This is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement. Person re-identification involves two main steps: feature representation and metric learning.

Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition.

Recent emergence of low-cost and easy-operating depth cameras has reinvigorated the research in skeleton-based human action recognition. However, most existing approaches overlook the intrinsic interdependencies between skeleton joints and action classes, thus suffering from unsatisfactory recognition performance. In this paper, a novel latent max-margin multitask learning model is proposed for 3-D action recognition.

Manifold Ranking-Based Matrix Factorization for Saliency Detection.

Saliency detection is used to identify the most important and informative area in a scene, and it is widely used in various vision tasks, including image quality assessment, image matching, and object recognition. Manifold ranking (MR) has been used to great effect for the saliency detection, since it not only incorporates the local spatial information but also utilizes the labeling information from background queries. However, MR completely ignores the feature information extracted from each superpixel.

Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.

Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks.

Person reidentification by minimum classification error-based KISS metric learning.

In recent years, person reidentification has received growing attention with the increasing popularity of intelligent video surveillance. This is because person reidentification is critical for human tracking with multiple cameras. Recently, keep it simple and straightforward (KISS) metric learning has been regarded as a top level algorithm for person reidentification.

Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space.