How volatilities nonlocal in time affect the price dynamics in complex financial systems

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.Comment: 16 pages, 7 figure

Multi-Modality Deep Network for Extreme Learned Image Compression

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours.Comment: 13 pages, 14 figures, accepted by AAAI 202

Inserting Extra Train Services on High-Speed Railway

With the aim of supporting future traffic needs, an account of how to reconstruct an existing cyclic timetable by inserting additional train services will be given in this paper. The Timetable-based Extra Train Services Inserting (TETSI) problem is regarded as an integration of railway scheduling and rescheduling problem. The TETSI problem therefore is considered involving many constraints, such as flexible running times, dwell times, headway and time windows. Characterized based on an event-activity graph, a general Mixed Integer Program model for this problem is formulated. In addition, several extensions to the general model are further proposed. The real-world constraints that concerning the acceleration and deceleration times, priority for overtaking, allowed adjustments, periodic structure and frequency of services are incorporated into the general model. From numerical investigations using data from Shanghai-Hangzhou High-Speed Railway in China, the proposed framework and associated techniques are tested and shown to be effective

Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods.Comment: 11 pages, 12 figures, ACM MM202

Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In this paper, we develop an Uncertainty-Guided Spatial Pruning (UGSP) architecture to skip redundant computation for efficient frame interpolation dynamically. Specifically, pixels with low uncertainty indicate easy regions, where the calculation can be reduced without bringing undesirable visual results. Therefore, we utilize uncertainty-generated mask labels to guide our UGSP in properly locating the easy region. Furthermore, we propose a self-contrast training strategy that leverages an auxiliary non-pruning branch to improve the performance of our UGSP. Extensive experiments show that UGSP maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks.Comment: ACM Multimedia 202

Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within the ground-truth labels by training a semantic-painted teacher model and then guide the pure-lidar network to learn the semantic-painted representation via knowledge passing modules at different granularities: class-wise passing, pixel-wise passing and instance-wise passing. Experimental results show that the proposed SPNet can seamlessly cooperate with most existing 3D detection frameworks with 1~5% AP gain and even achieve new state-of-the-art 3D detection performance on the KITTI test benchmark. Code is available at: https://github.com/jb892/SPNet.Comment: Accepted by ACMMM202

Study on Mechanism and Improvement of Triple Frequency Noise of Rotary Compressor

With the continuous improvement of social life, people have more stringent noise requirements for home air conditioners. As the kernel of an air conditioner, compressor provides power for the whole system, inevitably generating vibration and noise. Therefore, Reducing the vibration and noise of the compressor is great significance for the noise reduction of the air conditioner. Generally, vibration is mainly transferred through the suction and exhaust pipes to the air conditioning pipe system. However, due to the complicated configuration, there are intensive modals for the pipe system, especially those in low frequency range, which may lead to resonance and large acoustic radiation. This paper studies the generation and transmission mechanism of triple frequency vibration of compressor, the compressor exhaust pressure fluctuation stimulates the exhaust pipe to vibrate, and then results in vibration of the air conditioning pipe systems, and vibration generated by the rotor is transferred to intake pipe via the accumulator, and cause the pipe systems to vibrate. Based on this research, we find some main factors which influence the triple frequency vibration and noise of the compressor, which are the exhaust pressure pulsation, the natural frequency of the rotor-crankshaft system swing, the natural frequency of the accumulator swing. Then, above factors which affect the compressor vibration and noise are analyzed and improved separately, and conducted noise tests on the improved compressor at 90Hz. The results show that the compressor noise are reduced by 29.8% around 250Hz

Federated Skewed Label Learning with Logits Fusion

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods.Comment: 9 pages, 4 figures, 4 table

Evolution and conservation of polycomb repressive complex 1 core components and putative associated factors in the green lineage

International audienc

Green synthesis of biogenetic Te(0) nanoparticles by high tellurite tolerance fungus Mortierella sp. AB1 with antibacterial activity

Tellurite [Te(IV)] is a high-toxicity metalloid. In this study, a fungus with high Te(IV) resistance was isolated. Strain AB1 could efficiently reduce highly toxic Te(IV) to less toxic Te(0). The reduced products formed rod-shaped biogenetic Te(0) nanoparticles (Bio-TeNPs) intracellularly. Further TEM-element mapping, FTIR, and XPS analysis showed that the extracted Bio-TeNPs ranged from 100 to 500 nm and consisted of Te(0), proteins, lipids, aromatic compounds, and carbohydrates. Moreover, Bio-TeNPs exhibited excellent antibacterial ability against Shigella dysenteriae, Escherichia coli, Enterobacter sakazakii, and Salmonella typhimurium according to inhibition zone tests. Further growth and live/dead staining experiments showed that E. coli and S. typhimurium were significantly inhibited by Bio-TeNPs, and cells were broken or shriveled after treatment with Bio-TeNPs based on SEM observation. Additionally, the antioxidant and cytotoxicity tests showed that the Bio-TeNPs exhibited excellent antioxidant capacity with no cytotoxicity. All these results suggested that strain AB1 showed great potential in bioremediation and Bio-TeNPs were excellent antibacterial nanomaterials with no cytotoxicity.Peer reviewe