The Effect of Inflation on the Yield of Fixed Income Securities: Evidence from Chinese Securities Market

Inflation has become a very common economic phenomenon in today's society, its impact on the social economy is always concerned, and the relationship between inflation and fixed income securities market is also a hot issue of finance. One of the more obvious characteristics of fixed income securities is that, in general, this kind of securities has relatively clear cash inflows, so if the price changes are not considered, the rate of return of fixed income securities is stable. But this kind of stability is the stability of the nominal yield, after considering inflation, the yield of fixed income securities is not stable. Inflation risk is a systemic risk, as long as the securities in the fixed income securities market will face this risk. When the economy is relatively healthy and stable, fixed income securities can be priced to ignore this risk and only need to be compensated at the general level of interest rates. But in times of economic uncertainty, especially in recent years, global inflation has been relatively high compared with long-term historical data, so inflationary pressures cannot be ignored. At present, central banks in major western developed countries and regions are implementing intensive monetary easing policies in response to the negative impact of COVID-19 on the economy. There is concerned by people from all walks of life that such policies will lead to global inflation. At present, there are many studies on the effect of inflation on the stock market, but few studies and empirical analyses on the effect of inflation on the fixed income securities market. China's fixed income securities market is gradually developing, so in this dissertation, I will take the Chinese market as an example to study the relationship between inflation and the rate of return of fixed income securities and the extent to which inflation affects the rate of return of fixed income securities with different maturity structures

mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar

Millimeter Wave (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWave Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.Comment: ACM Multimedia 2022, Project Page: https://chen3110.github.io/mmbody/index.htm

A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry

Mercury (Hg) is a global persistent contaminant. Modeling studies are useful means of synthesizing a current understanding of the Hg cycle. Previous studies mainly use coarse-resolution models, which makes it impossible to analyze the role of turbulence in the Hg cycle and inaccurately describes the transport of kinetic energy. Furthermore, all of them are coupled with offline biogeochemistry, and therefore they cannot respond to short-term variability in oceanic Hg concentration. In our approach, we utilize a high-resolution ocean model (MITgcm-ECCO2, referred to as “high-resolution-MITgcm”) coupled with the concurrent simulation of biogeochemistry processes from the Darwin Project (referred to as “online”). This integration enables us to comprehensively simulate the global biogeochemical cycle of Hg with a horizontal resolution of 1/5∘. The finer portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes demonstrate the effects of turbulence that are neglected in previous models. Ecological events such as algal blooms can cause a sudden enhancement of phytoplankton biomass and chlorophyll concentrations, which can also result in a dramatic change in particle-bound Hg (HgaqP) sinking flux simultaneously in our simulation. In the global estuary region, including riverine Hg input in the high-resolution model allows us to reveal the outward spread of Hg in an eddy shape driven by fine-scale ocean currents. With faster current velocities and diffusion rates, our model captures the transport and mixing of Hg from river discharge in a more accurate and detailed way and improves our understanding of Hg cycle in the ocean.</p

Online data poisoning attack against edge AI paradigm for IoT-enabled smart city

The deep integration of edge computing and Artificial Intelligence (AI) in IoT (Internet of Things)-enabled smart cities has given rise to new edge AI paradigms that are more vulnerable to attacks such as data and model poisoning and evasion of attacks. This work proposes an online poisoning attack framework based on the edge AI environment of IoT-enabled smart cities, which takes into account the limited storage space and proposes a rehearsal-based buffer mechanism to manipulate the model by incrementally polluting the sample data stream that arrives at the appropriately sized cache. A maximum-gradient-based sample selection strategy is presented, which converts the operation of traversing historical sample gradients into an online iterative computation method to overcome the problem of periodic overwriting of the sample data cache after training. Additionally, a maximum-loss-based sample pollution strategy is proposed to solve the problem of each poisoning sample being updated only once in basic online attacks, transforming the bi-level optimization problem from offline mode to online mode. Finally, the proposed online gray-box poisoning attack algorithms are implemented and evaluated on edge devices of IoT-enabled smart cities using an online data stream simulated with offline open-grid datasets. The results show that the proposed method outperforms the existing baseline methods in both attack effectiveness and overhead

Exosome delivery to the testes for dmrt1 suppression: a powerful tool for sex-determining gene studies

Exosomes are endosome-derived extracellular vesicles about 100 nm in diameter. They are emerging as prom ising delivery platforms due to their advantages in biocompatibility and engineerability. However, research into and applications for engineered exosomes are still limited to a few areas of medicine in mammals. Here, we expanded the scope of their applications to sex-determining gene studies in early vertebrates. An integrated strategy for constructing the exosome-based delivery system was developed for efficient regulation of dmrt1, which is one of the most widely used sex-determining genes in metazoans. By combining classical methods in molecular biology and the latest technology in bioinformatics, isomiR-124a was identified as a dmrt1 inhibitor and was loaded into exosomes and a testis-targeting peptide was used to modify exosomal surface for efficient delivery. Results showed that isomiR-124a was efficiently delivered to the testes by engineered exosomes and revealed that dmrt1 played important roles in maintaining the regular structure and function of testis in juvenile fish. This is the first de novo development of an exosome-based delivery system applied in the study of sex determining gene, which indicates an attractive prospect for the future applications of engineered exosomes in exploring more extensive biological conundrums.info:eu-repo/semantics/publishedVersio

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research

The Effect of Inflation on the Yield of Fixed Income Securities: Evidence from Chinese Securities Market

Inflation has become a very common economic phenomenon in today's society, its impact on the social economy is always concerned, and the relationship between inflation and fixed income securities market is also a hot issue of finance. One of the more obvious characteristics of fixed income securities is that, in general, this kind of securities has relatively clear cash inflows, so if the price changes are not considered, the rate of return of fixed income securities is stable. But this kind of stability is the stability of the nominal yield, after considering inflation, the yield of fixed income securities is not stable. Inflation risk is a systemic risk, as long as the securities in the fixed income securities market will face this risk. When the economy is relatively healthy and stable, fixed income securities can be priced to ignore this risk and only need to be compensated at the general level of interest rates. But in times of economic uncertainty, especially in recent years, global inflation has been relatively high compared with long-term historical data, so inflationary pressures cannot be ignored. At present, central banks in major western developed countries and regions are implementing intensive monetary easing policies in response to the negative impact of COVID-19 on the economy. There is concerned by people from all walks of life that such policies will lead to global inflation. At present, there are many studies on the effect of inflation on the stock market, but few studies and empirical analyses on the effect of inflation on the fixed income securities market. China's fixed income securities market is gradually developing, so in this dissertation, I will take the Chinese market as an example to study the relationship between inflation and the rate of return of fixed income securities and the extent to which inflation affects the rate of return of fixed income securities with different maturity structures

PRG: A Distance Measurement Algorithm Based on Phase Regeneration

With the booming development of the Internet of things (IoT) industry, the demand of positioning technology in various IoT application scenarios is also greatly increased. To meet the positioning requirements of the IoT application, we propose a distance measurement method based on phase regeneration that can provide positioning capability for IoT applications in indoor and outdoor environments. The PRG algorithm consists of two phases: coarse ranging phase and fine ranging phase. Fingerprint positioning algorithm based on Gradient Boost Decision Tree (GBDT) is used to determine coarse distance. The host machine measures the difference between the transmitted carrier phase and the received regenerative carrier phase to fix the fine distance and then the coarse distance is used to determine the carrier phase integer ambiguity. Finally, high precision ranging is realized. Simulation results show that the PRG method can achieve range finding with decimeter level precision under the 10 MHz subcarrier frequency

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p &lt; 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p &lt; 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade

Research on Data Poisoning Attack against Smart Grid Cyber–Physical System Based on Edge Computing

Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a smart grid, addressing data poisoning is of utmost importance. However, this attack requires solving an expensive two-level optimization problem, which can be challenging to implement in resource-constrained edge environments of the smart grid. To mitigate this issue, it is crucial to enhance efficiency and reduce the costs of the attack. This paper proposes an online data poisoning attack framework based on the online regression task model. The framework achieves the goal of manipulating the model by polluting the sample data stream that arrives at the cache incrementally. Furthermore, a point selection strategy based on sample loss is proposed in this framework. Compared to the traditional random point selection strategy, this strategy makes the attack more targeted, thereby enhancing the attack’s efficiency. Additionally, a batch-polluting strategy is proposed in this paper, which synchronously updates the poisoning points based on the direction of gradient ascent. This strategy reduces the number of iterations required for inner optimization and thus reduces the time overhead. Finally, multiple experiments are conducted to compare the proposed method with the baseline method, and the evaluation index of loss over time is proposed to demonstrate the effectiveness of the method. The results show that the proposed method outperforms the existing baseline method in both attack effectiveness and overhead