With the method of remote sensing technology and field survey,the authors studied the diversity of tree communities in western mountainous area of Beijing in spring of 2008.This paper extracted Normalized Difference Vegetation Index(NDVI),calculated the NDVI change rate with time(△NDVI),used the health index,Margalef richness index,Shannon-Wiener diversity index and Simpson diversity index with the field survey data,and analyzed the relationship between △NDVI,the health index and the diversity index.The results showed that:(1)It existed positive correlation between health index and diversity index,the higher the diversity index is,the better the health index of community is.(2)It existed positive correlation between △NDVI and health index,the higher the community health index is,the greater the added value of NDVI of per unit time is,the more obvious the growth changes of vegetation community is.(3)△NDVI could be representate the health level of vegetation,reflecte richness and diversity of community.The higher the value of △NDVI is,the better the health of community is,the higher the richness,diversity index of community is.This study combined remote sensing technology and field surveys well and studied and validated the relationship between △NDVI and health and diversity index of communities,thus it can provide some reference for further study of community diversity.
In the electric power communication network architecture, the Internet of Things business requirements of mutual perception and coordination of each link, and the traditional security protection structure based on boundary isolation, it is a difficult point to achieve a high degree of compatibility and coupling. Therefore, when PCE performs path calculation in the multi-domain FlexE network, it is necessary to consider the delay of the transmission path. The low-delay transmission of the service is ensured. This paper analyzes the network-slicing requirements of typical business scenarios in the power grid. In view of the hierarchical architecture of the power Internet of Things, this paper analyzes its requirements for 5G technology and network slicing, and the overall architecture of the integrated energy Internet. Finally, according to the current business needs, this paper proposes a power slicing scheme that meets the needs of the power grid's various business scenarios. On this basis, an end-to-end network architecture based on power slicing is designed. The results verify that the safe and efficient slicing strategy based on deep reinforcement learning has better performance than the traditional machine learning model, which can enhance the monitoring effect of the terminal network security of the power Internet of Things. It can also improve the terminal security of the power Internet of Things at this stage and can detect the potential risks of terminal security monitoring.
Abstract In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can securely achieve the outsource data storage and fast, secure data processing in a single cloud server different from the existing multi-server outsourced computation model. Specifically, we first present a general secure computation framework for LightCom under the cloud server equipped with multiple Trusted Processing Units (TPUs), which face the side-channel attack. Under the LightCom, we design two specified fast processing toolkits, which allow the user to achieve the commonly-used secure integer computation and secure floating-point computation against the side-channel information leakage of TPUs, respectively. Furthermore, our LightCom can also guarantee access pattern protection during the data processing and achieve private user information retrieve after the computation. We prove that the proposed LightCom can successfully achieve the goal of single cloud outsourced data processing to avoid the extra computation server and trusted computation server, and demonstrate the utility and the efficiency of LightCom using simulations.
By the methods of burrow depth,mark-recapture and burrow account,this paper investigated the Phrynocephalus vlangalii population density on zoige desert in September 2002.The results showed that the population density obtained by these methods was 190.4,76.8 and 250.7 ind·1 000 m~(-2),respectively.Burrow depth method was proved to be highly reliable after comparing and analyzing the results,because Phrynocephalus is a kind of cold-blood animal not able to live in the environment where temperature is below the lethal temperature(-2.5℃) for a long time but inhabit in the burrows deeper than the maximum depth of frozen earth to live through the long cold winter,which is a behavior mechanism of P.vlangalii to protect itself from low temperature.It's suggested that burrow depth method could be applicable to other species of Phryhnocephalus distributed in China.
Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks. Despite this, the auto-regressive nature of LLM decoding, which generates only a single token per forward propagation, fails to fully exploit the parallel computational power of GPUs, leading to considerable latency. To address this, we introduce a novel speculative decoding method named FIRP which generates multiple tokens instead of one at each decoding step. We achieve this by predicting the intermediate hidden states of future tokens (tokens have not been decoded yet) and then using these pseudo hidden states to decode future tokens, specifically, these pseudo hidden states are predicted with simple linear transformation in intermediate layers of LLMs. Once predicted, they participate in the computation of all the following layers, thereby assimilating richer semantic information. As the layers go deeper, the semantic gap between pseudo and real hidden states is narrowed and it becomes feasible to decode future tokens with high accuracy. To validate the effectiveness of FIRP, we conduct extensive experiments, showing a speedup ratio of 1.9x-3x in several models and datasets, analytical experiments also prove our motivations.
RNA-protein interactions (RPIs) play a very important role in a wide range of post-transcriptional regulations, and identifying whether a given RNA-protein pair can form interactions or not is a vital prerequisite for dissecting the regulatory mechanisms of functional RNAs. Currently, expensive and time-consuming biological assays can only determine a very small portion of all RPIs, which calls for computational approaches to help biologists efficiently and correctly find candidate RPIs. Here, we integrated a successful computing algorithm, conjoint triad feature (CTF), and another method, chaos game representation (CGR), for representing RNA-protein pairs and by doing so developed a prediction model based on these representations and random forest (RF) classifiers. When testing two benchmark datasets, RPI369 and RPI2241, the combined method (CTF+CGR) showed some superiority compared with four existing tools. Especially on RPI2241, the CTF+CGR method improved prediction accuracy (ACC) from 0.91 (the best record of all published works) to 0.95. When independently testing a newly constructed dataset, RPI1449, which only contained experimentally validated RPIs released between 2014 and 2016, our method still showed some generalization capability with an ACC of 0.75. Accordingly, we believe that our hybrid CTF+CGR method will be an important tool for predicting RPIs in the future.
Data privacy is becoming one of the most critical concerns in cloud computing. Several proposals based on Intel SGX such as VC3 [1] and M2R [2] have been introduced in the literature to protect data privacy during job execution in the cloud. However, a comprehensive formal proof of their security guarantees is still lacking. In this paper, we propose ObliDC, a general UC-secure SGX-based oblivious distributed computing framework. First, we model the life-cycle of a distributed computing job as data-flow graphs. Under the assumption of malicious, adaptive adversaries in the cloud, we then formally define data privacy of a distributed computing job by introducing a notion named ODC-privacy, which encompasses both semantic security (to protect data confidentiality during computation and transmission) and oblivious traffic (to prevent data leakage from traffic analysis). ObliDC is composed of four two-party protocols -- job deployment, job initialization, job execution, and results return, which allow for modular construction of concrete privacy-preserving job protocols in different distributed computing frameworks. Finally, inspired by a formal abstraction for trusted processors proposed by R. Pass et al. [3], we formally prove the security of ObliDC under the universal composability (UC) framework.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='translate(288.889 -214.211)'/%3e%3cuse xlink:href='%23a' transform='translate(108 342.749)'/%3e%3cuse xlink:href='%23a' transform='translate(469.189 342.749)'/%3e%3c/svg%3e)