Exploring Book-Tax Differences, Analyst Coverage, and Forecast Optimism: An Empirical Study

This research explores the relationship between book-tax differences (BTD) and the coverage of financial analysts, as well as how these two factors relate to optimistic forecasts. By evaluating how BTD affects analyst coverage, this research expands on earlier studies and finds a negative link. Additionally, it demonstrates that greater BTD indicates a higher probability of earnings manipulation, leading analysts to have a more pessimistic view of their predictions for such organizations. This research contributes to a better understanding of financial markets by shedding light on analyst behavior, financial reporting, and the complex dynamics underlying BTD in determining analyst estimates

The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System

The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA) of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor) algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification) algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation

Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images

In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (UAGAN), which can perform any-to-any image modality translation and segment the target objects simultaneously in the case where two or more modalities are available. The translation stream is used to capture modality-invariant features of the target anatomical structures. In addition, to focus on segmentation-related features, we add attentional blocks to extract valuable features from the translation stream. Experiments on three-modality brain tumor segmentation indicate that UAGAN outperforms the existing methods in most cases.Comment: 9 pages, 4 figures, Accepted by MICCAI201

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

Roles of long noncoding RNAs and small extracellular vesicle-long noncoding RNAs in type 2 diabetes

Wenguang Chang, Man Wang, Yuan Zhang and Fei Yu collected all of the data, and Wenguang Chang, Bin Hu, Katarzyna Goljanek-Whysall and Peifeng Li wrote and revised the manuscript. All authors have read and approved the final version of the manuscript. This work was supported by National Natural Science Foundation of China (81700704).Peer reviewedPublisher PD

The crosstalk between the gut microbiota and tumor immunity: Implications for cancer progression and treatment outcomes

The gastrointestinal tract is inhabited by trillions of commensal microorganisms that constitute the gut microbiota. As a main metabolic organ, the gut microbiota has co-evolved in a symbiotic relationship with its host, contributing to physiological homeostasis. Recent advances have provided mechanistic insights into the dual role of the gut microbiota in cancer pathogenesis. Particularly, compelling evidence indicates that the gut microbiota exerts regulatory effects on the host immune system to fight against cancer development. Some microbiota-derived metabolites have been suggested as potential activators of antitumor immunity. On the contrary, the disequilibrium of intestinal microbial communities, a condition termed dysbiosis, can induce cancer development. The altered gut microbiota reprograms the hostile tumor microenvironment (TME), thus allowing cancer cells to avoid immunosurvelliance. Furthermore, the gut microbiota has been associated with the effects and complications of cancer therapy given its prominent immunoregulatory properties. Therapeutic measures that aim to manipulate the interplay between the gut microbiota and tumor immunity may bring new breakthroughs in cancer treatment. Herein, we provide a comprehensive update on the evidence for the implication of the gut microbiota in immune-oncology and discuss the fundamental mechanisms underlying the influence of intestinal microbial communities on systemic cancer therapy, in order to provide important clues toward improving treatment outcomes in cancer patients

Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method

This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability, and robustness in case of malfunction of the front steering angle sensor. It also provided a software redundancy and backup solution, as well as improved fault tolerance. The strategy of the sensorless control is based on the sliding mode method to estimate the replacement of the front steering input from the errors between the vehicle’s measured and desired values of the vehicle’s sideslip angle and yaw rate. The simulation results demonstrate that the observer effectively estimated the front-wheel steering angle at both low and high speeds scenarios in the cornering and lane change manoeuvres. Furthermore, the sensorless control approach can achieve equivalent control performances to the sensor-based controller including a small and stable yaw rate response and zero sideslip angle. The results of the study offer a potential solution for improving manoeuvrability, stability, and sensor fault tolerance of four-wheel steering vehicles