Investigating the Effect of Mergers and Acquisitions on Firm’s Operating Performance

Previous research results about the effect of Mergers and Acquisitions on the performance of firms are ambiguous. Using firms matched on the basis of size and industry as the benchmark and several operating performance measures, the evidence from this study imply that Mergers and Acquisitions completed in the US with minimum deal value $500 million over the period 2000 to 2005 result in no significant improvements in operating performance. Further, there is some evidence that the type of the Mergers and Acquisitions and the method of payment have no impact on the postmerger performance

SENSE: a Shared Encoder Network for Scene-flow Estimation

We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation. Our key insight is that sharing features makes the network more compact, induces better feature representations, and can better exploit interactions among these tasks to handle partially labeled data. With a shared encoder, we can flexibly add decoders for different tasks during training. This modular design leads to a compact and efficient model at inference time. Exploiting the interactions among these tasks allows us to introduce distillation and self-supervised losses in addition to supervised losses, which can better handle partially labeled real-world data. SENSE achieves state-of-the-art results on several optical flow benchmarks and runs as fast as networks specifically designed for optical flow. It also compares favorably against the state of the art on stereo and scene flow, while consuming much less memory.Comment: ICCV 2019 Ora

Effects of water volume of drip irrigation on soil bacterial communities and its association with soil properties in jujube cultivation

IntroductionJujube is one of an important crop in Xinjiang, China, a area suffered by water scarcity and DI has been proven as a suitable mode for jujube cultivation. Soil bacterial community play a vital role in biogeochemical cycles to support the crop growth, and water content is considered as one of the important factors for them. However, limited research has explored the optimum irrigation strategies, such as water volume of DI, to maximize the benefits of jujube cultivation by regulating the soil bacterial communities.MethodsTherefore, in this study, we conducted DI experiments on jujube fields in Xinjiang with three different water volume levels, and measured the soil properties and bacterial communities of the flowering and fruit setting (FFS) and end of growth (EG) stages.Results and discussionSignificant lower jujube yield and soil available nutrients were observed in samples with low water amount. In addition, we discovered significant effects of the water amount of DI and jujube growth stages on soil bacterial communities. Based on the compare of samples among different growth stages and water amounts some growth stage related bacterial genera (Mycobacterium, Bradyrhizobium, and Bacillus) and water amount-related bacterial phyla (Chloroflexi, Nitrospirota, and Myxococcota) were recognized. Moreover, according to the results of null model, soil bacterial communities were governed by stochastic and deterministic processes under middle and low water volumes of DI, respectively. Finally, we deduced that middle water amount (600 mm) could be the optimal condition of DI for jujube cultivation because the higher jujube yield, deterministic assembly, and stronger correlations between soil properties and bacterial community under this condition. Our findings provide guidance for promoting the application of DI in jujube cultivation, and further research is needed to investigate the underlying mechanisms of soil bacterial community to promote the jujube yield

T-Rex: Counting by Visual Prompting

We introduce T-Rex, an interactive object counting model designed to first detect and then count any objects. We formulate object counting as an open-set object detection task with the integration of visual prompts. Users can specify the objects of interest by marking points or boxes on a reference image, and T-Rex then detects all objects with a similar pattern. Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects. T-Rex has achieved state-of-the-art performance on several class-agnostic counting benchmarks. To further exploit its potential, we established a new counting benchmark encompassing diverse scenarios and challenges. Both quantitative and qualitative results show that T-Rex possesses exceptional zero-shot counting capabilities. We also present various practical application scenarios for T-Rex, illustrating its potential in the realm of visual prompting.Comment: Technical report. Work in progres

Real-time Controllable Denoising for Image and Video

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.Comment: CVPR 202

An Improved Corner Detection Algorithm Based on Harris

Abstract. In order to accurately extract corners from the image with high texture complexity, the paper analyzed the traditional corner detection algorithm based on gray value of image. Although Harris corner detection algorithm has higher accuracy, but there also exists the following problems: extracting false corners, the information of the corners is missing and computation time is a bit long. So an improved corner detection algorithm combined Harris with SUSAN corner detection algorithm is proposed, the new algorithm first use the Harris to detect corners of image, then use the SUSAN to eliminate the false corners. By comparing the test results show that the new algorithm to extract corners very effective, and better than the Harris algorithm in the performance of corner detection