Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint

Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Volatility is integral for the financial market. As an emerging market, the Chinese stock market is acutely volatile. In this study, the data of the Shanghai Composite Index and Shenzhen Component Index returns were selected to conduct an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. We established the autoregressive moving average (ARMA)-GARCH model with t-distribution for the sample series to compare model effects under different distributions and orders. In contrast, we proposed threshold-GARCH (TGARCH) and exponential-GARCH (EGARCH) models to capture the features of the index. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE) and rootmean-squared error (RMSE). The results denote that the ARMA (4,4)-GARCH (1,1) model under Student’s t-distribution outperforms other models when forecasting the Shanghai Composite Index return series. For the return series of the Shenzhen Component Index, ARMA(1,1)-TGARCH(1,1) display the best forecasting performance among all models. This study could provide an effective information reference for the macro decision-making of the government, the operation of listed companies and investors’ investment decision-making

Antimicrobial resistance and the growing threat of drug-resistant tuberculosis

The purpose of this study was to investigate the associations between birth weight, chest circumference, and lung function in preschool children from e-waste exposure area. A total of 206 preschool children from Guiyu (an e-waste recycling area) and Haojiang and Xiashan (the reference areas) in China were recruited and required to undergo physical examination, blood tests, and lung function tests during the study period. Birth outcome such as birth weight and birth height were obtained by questionnaire. Children living in the e-waste-exposed area have a lower birth weight, chest circumference, height, and lung function when compare to their peers from the reference areas (all p value <0.05). Both Spearman and partial correlation analyses showed that birth weight and chest circumference were positively correlated with lung function levels including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). After adjustment for the potential confounders in further linear regression analyses, birth weight, and chest circumference were positively associated with lung function levels, respectively. Taken together, birth weight and chest circumference may be good predictors for lung function levels in preschool children

Model Stealing Attack against Multi-Exit Networks

Compared to traditional neural networks with a single exit, a multi-exit network has multiple exits that allow for early output from intermediate layers of the model, thus bringing significant improvement in computational efficiency while maintaining similar recognition accuracy. When attempting to steal such valuable models using traditional model stealing attacks, we found that conventional methods can only steal the model's classification function while failing to capture its output strategy. This results in a significant decrease in computational efficiency for the stolen substitute model, thereby losing the advantages of multi-exit networks.In this paper, we propose the first model stealing attack to extract both the model function and output strategy. We employ bayesian changepoint detection to analyze the target model's output strategy and use performance loss and strategy loss to guide the training of the substitute model. Furthermore, we designed a novel output strategy search algorithm that can find the optimal output strategy to maximize the consistency between the victim model and the substitute model's outputs. Through experiments on multiple mainstream multi-exit networks and benchmark datasets, we thoroughly demonstrates the effectiveness of our method

Toward Reproducing Network Research Results Using Large Language Models

Reproducing research results in the networking community is important for both academia and industry. The current best practice typically resorts to three approaches: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; and (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private prototypes are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). In particular, we first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report the experiment's observations and lessons and discuss future open research questions of this proposal. This work raises no ethical issue

Long-lived magmatic evolution and mineralization resulted in formation of the giant Cuonadong Sn-W-Be polymetallic deposit, southern Tibet

The Cuonadong Sn-W-Be polymetallic deposit is the first Cenozoic leucogranite-related rare-metal deposit with giant metallogenic potential in the Himalayan orogen. However, controlling factors for the supernormal enrichment of beryllium, tin and tungsten in this deposit remain vague. In this study, we carried out systematic geochronological, whole-rock geochemical, and Sr-Nd isotopic analysis for the Cuonadong leucogranites, as well as detailed ore-forming geochronological analysis. The monazite U-Th-Pb, cassiterite U-Pb and muscovite Ar-Ar dating results, together with previously reported geochronological data, indicate that the major Cuonadong leucogranites (including, from old to young, weakly-oriented two-mica, two-mica granite and muscovite) were formed during ∼21-15 Ma, whereas the Sn-W-Be mineralization mainly occurred at ∼18-14 Ma. The Cuonadong leucogranites show strong peraluminous (A/CNK=1.09-1.22) features, and have high SiO2 (71.62-75.97 wt.%) and Al2O3 (14.04-16.09 wt.%) and low MgO (0.07-0.33 wt.%), MnO (0.01-0.15 wt.%) and total Fe2O3 (0.36-1.01 wt.%) contents, and are enriched in large ion lithophile elements (e.g., Rb, U, K, and Pb). These geochemical features together with enriched Sr-Nd isotopes (εNd(t) = -15.7 to -11.7; (87Sr/86Sr)i=0.71957-0.76313) indicate that the Cuonadong leucogranites belong to S-type granite and were derived from muscovite-induced dehydration melting of metapelites of the Higher Himalayan Crystalline Sequence. Perceptible linear variations of some major elements (e.g., Na2O, K2O, MnO, Fe2O3T, TiO2 and A/CNK) with increasing Rb/Sr ratios suggest these leucogranites experienced different degrees of evolution. Quantitative simulation calculations based on the whole-rock Rb, Sr, and Ba contents imply that the Cuonadong leucogranites experienced increasingly-strong fractional crystallization of plagioclase, K-feldspar and biotite from the weakly-oriented two-mica granite to two-mica granite and muscovite granite. Importantly, intense fractional crystallization leaded to notable enrichment of Sn, W and Be, although these elements are not obviously high in the relatively primitive magma for the Cuonadong leucogranites. Significantly, evident REE tetrad effects and deviation of twin-element pair ratios (K/Rb, K/Ba, Zr/Hf, Nb/Ta, and Y/Ho) from the chondritic values demonstrate that intense interaction between melts and F-rich aqueous fluids occurred during magmatic evolution. This implies that the Cuonadong leucogranites were derived from a volatile-rich magmatic system. The abundant volatiles probably remarkably facilitated and extended the fractional crystallization though lowering the solidus and viscosity of the magma. Thus, we propose that long-lived crystal fractionation (∼21-15 Ma) and mineralization (∼18-14 Ma) collectively leaded to supernormal enrichment of Sn, W, and Be in the Cuonadong Sn-W-Be polymetallic deposit. In contrast, the enrichment of Sn, W, and Be during the partial melting was insignificant.publishedVersio

CVT: A Crowdsourcing Video Transcoding Scheme Based on Blockchain Smart Contracts

Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios

SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning

Recent years have witnessed significant success in Self-Supervised Learning (SSL), which facilitates various downstream tasks. However, attackers may steal such SSL models and commercialize them for profit, making it crucial to protect their Intellectual Property (IP). Most existing IP protection solutions are designed for supervised learning models and cannot be used directly since they require that the models' downstream tasks and target labels be known and available during watermark embedding, which is not always possible in the domain of SSL. To address such a problem especially when downstream tasks are diverse and unknown during watermark embedding, we propose a novel black-box watermarking solution, named SSL-WM, for protecting the ownership of SSL models. SSL-WM maps watermarked inputs by the watermarked encoders into an invariant representation space, which causes any downstream classifiers to produce expected behavior, thus allowing the detection of embedded watermarks. We evaluate SSL-WM on numerous tasks, such as Computer Vision (CV) and Natural Language Processing (NLP), using different SSL models, including contrastive-based and generative-based. Experimental results demonstrate that SSL-WM can effectively verify the ownership of stolen SSL models in various downstream tasks. Furthermore, SSL-WM is robust against model fine-tuning and pruning attacks. Lastly, SSL-WM can also evade detection from evaluated watermark detection approaches, demonstrating its promising application in protecting the IP of SSL models

Nanodelivery of nucleic acids

Funding: This work was supported by the European Research Council (ERC) Starting Grant (ERC-StG-2019-848325 to J. Conde) and the Fundação para a Ciência e a Tecnologia FCT Grant (PTDC/BTM-MAT/4738/2020 to J. Conde). J.S. acknowledges US National Institute of Health (NIH) grants (R01CA200900, R01HL156362 and R01HL159012), the US DoD PRCRP Idea Award with Special Focus (W81XWH1910482), the Lung Cancer Discovery Award from the American Lung Association and the Innovation Discovery Grants award from the Mass General Brigham. H.L., D.Y. and X.Z. were supported by the National Key R&D Program of China (no. 2020YFA0710700), the National Natural Science Foundation of China (nos 21991132, 52003264, 52021002 and 52033010) and the Fundamental Research Funds for the Central Universities (no. WK2060000027).There is growing need for a safe, efficient, specific and non-pathogenic means for delivery of gene therapy materials. Nanomaterials for nucleic acid delivery offer an unprecedented opportunity to overcome these drawbacks; owing to their tunability with diverse physico-chemical properties, they can readily be functionalized with any type of biomolecules/moieties for selective targeting. Nucleic acid therapeutics such as antisense DNA, mRNA, small interfering RNA (siRNA) or microRNA (miRNA) have been widely explored to modulate DNA or RNA expression Strikingly, gene therapies combined with nanoscale delivery systems have broadened the therapeutic and biomedical applications of these molecules, such as bioanalysis, gene silencing, protein replacement and vaccines. Here, we overview how to design smart nucleic acid delivery methods, which provide functionality and efficacy in the layout of molecular diagnostics and therapeutic systems. It is crucial to outline some of the general design considerations of nucleic acid delivery nanoparticles, their extraordinary properties and the structure–function relationships of these nanomaterials with biological systems and diseased cells and tissues.publishersversionpublishe

Acox2 is a regulator of lysine crotonylation that mediates hepatic metabolic homeostasis in mice

Acyl-CoA oxidase 2 (Acox2) is an enzyme involved in peroxisomal bile acid synthesis and branched-chain fatty acid degradation. Acox2 knockout (−/−) mice spontaneously developed liver cancer with marked lymphocytic infiltrate. Tandem-affinity purification coupled with mass spectrometry analysis revealed that Acox2 interacted with methylcrotonoyl-CoA carboxylase followed by co-immunoprecipitation confirmation. Here we reported that non-histone lysine crotonylation (Kcr) levels were downregulated in Acox2 −/− mice livers. Interestingly, Kcr signals were concentrated in the nucleus of tumor cells but mostly located in the cytoplasm of adjacent normal liver cells of Acox2 −/− mice. Quantitative analysis of the global crotonylome further revealed that 54% (27/50) of downregulated non-histone Kcr sites were located in mitochondrial (11/50) and peroxisomal (17/50) enzymes including Ehhadh, Scp2, Hsd17b4, Crot, Etfa, Cpt1a, Eci1/2, Hadha, Etfdh, and Idh2. Subsequent site-directed mutagenesis and transcriptome analysis revealed that Ehhadh K 572 cr might have site-specific regulatory roles by downregulating TOP3B expression that lead to increased DNA damage in vitro. Our findings suggested Acox2 is a regulator of Kcr that might play critical role on hepatic metabolic homeostasis