Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MA-enabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ’s actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ’s actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data.

ABSTRACT In this paper, we investigate an unmanned aerial vehicle (UAV) enabled secure multicast communication system, where a UAV serves multiple legitimate ground users in the presence of multiple coordinated eavesdroppers. Taking into account the inherent jittering characteristics of UAVs caused by the airflow, we aim to maximise the worst‐case secrecy energy efficiency (SEE) under a constrained UAV transmission power budget. The formulated optimization problem is inherently non‐convex and challenging to solve due to the combined effects of jittering uncertainties and the max–min fractional structure of the SEE metric. To address these challenges, we first simplify the original SEE maximization problem by introducing auxiliary variables. Next, considering the impact of jittering on the antenna array response, we develop a novel second‐order Taylor series expansion‐based approach to approximate beamforming gains as quadratic functions of the angle‐of‐departure errors, which can be subsequently transformed into deterministic convex constraints by using S ‐Procedure. Based on these theoretical results, we design an iterative algorithm that combines the penalty function method with the successive convex approximation to efficiently obtain a suboptimal solution. Finally, the simulation results demonstrate the effectiveness and superiority of the proposed scheme compared to several benchmark schemes, highlighting its potential for practical implementation in UAV‐enabled secure multicast communication systems.

Taste peptides have emerged as promising natural flavoring agents attributed to their unique organoleptic properties, high safety profile, and potential health benefits. However, the de novo identification of taste peptides derived from animal, plant, or microbial sources remains a time-consuming and resource-intensive process, significantly impeding their widespread application in the food industry. In this work, we present TastePepAI, a comprehensive artificial intelligence framework for customized taste peptide design and safety assessment. As the key element of this framework, a loss-supervised adaptive variational autoencoder (LA-VAE) is implemented to efficiently optimize the latent representation of sequences during training and facilitate the generation of target peptides with desired taste profiles. Notably, our model incorporates a novel taste-avoidance mechanism, allowing for selective flavor exclusion. Subsequently, our in-house developed toxicity prediction algorithm (SpepToxPred) is integrated in the framework to undergo rigorous safety evaluation of generated peptides. Using this integrated platform, we successfully identified 73 peptides exhibiting sweet, salty, and umami, significantly expanding the current repertoire of taste peptides. This work demonstrates the potential of TastePepAI in accelerating taste peptide discovery for food applications and provides a versatile framework adaptable to broader peptide engineering challenges.

This paper investigates a highly spectrally efficient transmission scheme in an integrated satellite and aerial network (ISAN). Specifically, we first propose a novel uplink access framework, where the co-design of communication and over-the-air computation (AirComp) is implemented through layer division multiplexing (LDM) in the aerial network, while the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) technology is employed in the satellite network. Then, according to the proposed framework, we mathematically formulate a joint optimization problem that aims at maximizing the system achievable sum rate, subject to the constraints of minimal accuracy requirement of AirComp and minimal quality-of-service requirements of communication service. Next, by introducing the inter-network interference-related auxiliary variable, we divide the original optimization problem into two subproblems associated with the optimization of the satellite and aerial networks. To tackle the first subproblem, we propose a beamspace-inspired analog beamforming (BF) method, and derive closed-form expressions for BF vectors and transmit powers to implement the CR-NOMA scheme in the satellite network. Meanwhile, to address the second subproblem, we propose a beamspace-inspired digital BF together with successive convex approximation and alternating optimization approaches, to obtain the BF matrices, transmit power coefficients and AirComp scaling factor, so that the LDM-based communication and computation co-design (CCCD) can be realized in the aerial network. Moreover, for complexity reduction, we propose a beamspace-inspired zero-forcing BF method to calculate the communication BF matrices, and then leverage the orthogonal beam superposition approach to obtain the computation BF matrix, thereby presenting another CCCD scheme. Finally, our simulation results confirm that since the proposed schemes can realize spectrum multiplexing for communication and AirComp services, we achieve higher system spectral efficiency and lower computation error than the benchmarks.

Improving resource utilization in datacenters is vital for reducing costs for cloud service providers (CSPs). Increasing resource utilization must be balanced with maintaining quality of service (QoS) for latency-critical applications. In cloud environments, users often request excessive resources for applications to ensure QoS. To address this issue, CSPs use resource overcommitment - offering users resources that exceed the actual capacity of physical infrastructure. However, if not properly managed, such strategies may result in performance degradation or even request failure. Therefore, to achieve optimal resource utilization while maintaining QoS to applications, it is critical to implement a fine-grained overcommitment strategy.We propose HAPPIES, a History-aware management system with a precise prediction for machine resource demand. HAPPIES uses historical usage to extract resource characteristics and build application portraits that describe their resource demands. Compared to the existing strategy, this is a more aggressive overcommitment strategy that achieves higher resource utilization. We simulated experiments on 3,021 nodes and deployed over 14,000 applications on them. Results show that HAPPIES significantly outperforms Kubernetes Least Request and Peak Oracle in load balancing. Not only does it reduce the number of nodes experiencing high utilization, but it also decreases the peak usage of the most heavily utilized nodes. Therefore, HAPPIES scheduling reduces the risk of a machine being used beyond capacity.

We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. Firstly, reacting to increased load with a response-based approach can temporarily degrade QoS because many web applications need a few minutes to warm up. Therefore, precise workload prediction is critical for predictive scaling. However, our analysis of real-world applications underscores the substantial challenges arising from the limited precision and robustness of existing single prediction algorithms in the context of predictive auto-scaling. Secondly, guaranteeing the QoS of online applications within a cost-effective structure is crucial, as it is inherently linked to corporate profitability. Nevertheless, our study shows that mainstream auto-scaling methods exhibit various limitations, either being unsuitable for online environments or inadequately ensuring QoS.

This paper investigates a location-based downlink transmission scheme to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, the satellite network employs multicast communication to disseminate information to multiple satellite users, while the terrestrial network incorporates non-orthogonal multiple access (NOMA) with intelligent reflecting surface (IRS) technology to serve terrestrial users. Given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by optimizing the transmit power and beamforming (BF) weight vector at the base station, the IRS phase shift vector, and the power allocation factor, while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first propose a low-complexity BF algorithm based on the LoI-CSI, which simplifies the optimization problem while guaranteeing the QoS requirement of the satellite network. Then, assuming that terrestrial links experience Rician fading, we derive an approximate yet accurate OP of the terrestrial network, which is explored to calculate the phase shift vector. Furthermore, we propose a novel power allocation method that employs an exponential-type approximation of the first-order Marcum Q-function, to obtain the power allocation coefficient. Finally, simulation results confirm the theoretical formulas’ validity and reveal the proposed algorithms’ superiority in system performance.

This paper investigates a low-complexity downlink transmission to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by jointly optimizing the transmit power and beamforming (BF) weight vector at the base station (BS), and the phase shift vector at the intelligent reflecting surface (IRS), while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first use the LoI-CSI and propose a low-complexity BF algorithm to obtain the transmit power and BF weight vector. Then, employing IRS and non-orthogonal multiple access (NOMA) in the terrestrial network, we derive its closed-form expression for the OP of the terrestrial network, which is explored to calculate the IRS phase shift vector. Finally, simulation results confirm the validity of the theoretical formulas and reveal the proposed algorithms superiority in system performance.

Protein function prediction based on amino acid sequence alone is an extremely challenging but important task, especially in metagenomics/metatranscriptomics field, in which novel proteins have been uncovered exponentially from new microorganisms. Many of them are extremely low homology to known proteins and cannot be annotated with homology-based or information integrative methods. To overcome this problem, we proposed a Homology Independent protein Function annotation method (HiFun) based on a unified deep-learning model by reassembling the sequence as protein language. The robustness of HiFun was evaluated using the benchmark datasets and metrics in the CAFA3 challenge. To navigate the utility of HiFun, we annotated 2 212 663 unknown proteins and discovered novel motifs in the UHGP-50 catalog. We proved that HiFun can extract latent function related structure features which empowers it ability to achieve function annotation for non-homology proteins. HiFun can substantially improve newly proteins annotation and expand our understanding of microorganisms' adaptation in various ecological niches. Moreover, we provided a free and accessible webservice at http://www.unimd.org/HiFun, requiring only protein sequences as input, offering researchers an efficient and practical platform for predicting protein functions.

MOTIVATION: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level. RESULTS: Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. AVAILABILITY AND IMPLEMENTATION: PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to provide stable and continued services. However, compared with monolithic systems, software systems based on the layered microservice architecture are inherently complex and commonly involve entities at different levels of granularity. Therefore, for effective service monitoring, these systems have a special requirement of multi-granular RCA. Furthermore, as a large proportion of anomalies in microservice systems pertain to problematic code, to timely troubleshoot these anomalies, these systems have another special requirement of RCA at the finest code-level. Microservice systems rely on telemetry data to perform service monitoring and RCA of service anomalies. The majority of existing RCA approaches are only based on a single type of telemetry data and as a result can only support uni-granular RCA at either application-level or service-level. Although there are attempts to combine metric and tracing data in RCA, their objective is to improve RCA's efficiency or accuracy rather than to support multi-granular RCA. In this article, we propose a new RCA solutionTrinityRCLthat is able to localize the root causes of anomalies at multiple levels of granularity including application-level, service-level, host-level, and metric-level, with the unique capability of code-level localization by harnessing all three types of telemetry data to construct a causal graph representing the intricate, dynamic, and nondeterministic relationships among the various entities related to the anomalies. By implementing and deployingTrinityRCLin a real production environment, we evaluateTrinityRCLagainst two baseline methods and the results show thatTrinityRCLhas a significant performance advantage in terms of accuracy at the same level of granularity with comparable efficiency and is particularly effective to support large-scale systems with massive telemetry data.

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

Satellite and unmanned aerial vehicle (UAV) integrated networks (SUINs) are considered as a promising method to offer various Internet of Remote Things (IoRT) applications. In this article, we investigate the downlink transmission of SUINs where the satellite-to-UAV link uses the free-space optical (FSO) technology with an equal gain combining (EGC) scheme while the links from UAV to IoRT devices exploit radio frequency (RF) with the space-division multiple access (SDMA) technique. Specifically, considering that only statistical channel state information (CSI) is available, we first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the system, which is constrained by the total transmit power budget and IoRT devices’ rate requirements. Then, a beamforming (BF) scheme based on the alternating direction method of multipliers (ADMM) is proposed to solve the nonconvex problem. Furthermore, a zero-forcing (ZF)-based suboptimal approach is also presented to reduce the implementation complexity. Finally, by assuming that the FSO link and RF links are subject to Gamma–Gamma fading and Nakagami-$m$fading, respectively, we derive closed-form ESR expressions for the considered network with the proposed BF schemes. Simulation results are provided to confirm the accuracy of the theoretical analysis. Moreover, it is revealed that our proposed EGC scheme for FSO communication and BF schemes for RF transmission can both achieve better performance than the existing works.

Integrating non-orthogonal multiple access (NOMA) in intelligent reflecting surface (IRS) is expectedly an effective solution to enhance system's spectrum efficiency. In this paper, we investigate joint beamforming and power allocation for uplink NOMA transmission in an IRS-assisted cognitive satellite and terrestrial network operating at millimeter wave frequency band. Specifically, based only on imperfect channel state information in terms of the angular information of both primary users (PUs) and secondary users, we formulate an optimization problem to maximize the sum rate of the PUs in terrestrial network. To handle the resulting intractable optimization problem, we first transform the uncertainty channel vectors into a deterministic form with the aid of angular discretization. Then, by combining successive convex approximation with Taylor expansion and S-procedure methods, we propose an optimization scheme to jointly optimize the beamforming weight vector and power coefficients. Finally, simulation results show that the proposed scheme can achieve outstanding sum rate performance compared to state-of-the-art schemes.

High-throughput satellites (HTSs) play an important role in future millimeter-wave (mmWave) aeronautical communication to meet high speed and broad bandwidth requirements. This paper investigates the outage performance of an aeronautical broadband satellite communication system’s forward link, where the feeder link from the gateway to the HTS uses free-space optical (FSO) transmission and the user link from the HTS to aircraft operates at the mmWave band. In the user link, spot beam technology is exploited at the HTS and a massive antenna array is deployed at the aircraft. We first present a location-based beamforming (BF) scheme to maximize the expected output signal-to-noise ratio (SNR) of the forward link with the amplify-and-forward (AF) protocol, which turns out to be a phased array. Then, by supposing that the FSO feeder link follows Gamma-Gamma fading whereas the mmWave user link experiences shadowed Rician fading, we take the influence of the phase error into account, and derive the closed-form expression of the outage probability (OP) for the considered system. To gain further insight, a simple asymptotic OP expression at a high SNR is provided to show the diversity order and coding gain. Finally, numerical simulations are conducted to confirm the validity of the theoretical analysis and reveal the effects of phase errors on the system outage performance.

This article consists only of a collection of slides from the author's conference presentation.

This paper proposes a beamforming (BF) scheme with non-orthogonal multiple access (NOMA) for a cognitive satellite-terrestrial network (CSTN), where the satellite network shares the radio frequency bandwidth with the terrestrial network. By assuming that the satellite adopts multicast technology to serve several satellite terminals (STs), while the base station (BS) employs the combination of BF and NOMA to significantly enhance the spectrum efficiency, we aim at maximizing the sumrate of the considered CSTN under the constraints of per-antenna power budget and the quality of service (QoS) requirements for desired cellular users (CUs) and STs. Then, based on the S-procedure and Taylor approximation approach, we present a method to convert the nonconvex problem to a solvable one with linear constraints, and obtain the optimal BF weight vectors through iterative procedure. Finally, numerical results demonstrate the validity and superiority of our proposed scheme.

This paper investigates the secure transmission in satellite communication systems, where a geostationary orbit (GEO) satellite serves an earth station while multiple eavesdroppers attempt to intercept the confidential message. Assuming that only the imperfect channel state information (CSI) of the wiretap channels are available, we propose a secure beamforming scheme to maximize the secrecy energy efficiency (SEE) of the earth station while satisfying the signal-noise-ratio (SNR) requirement at earth station, the secrecy constraints at eavesdroppers, and per-antenna power constraints at satellite antenna feeds. Since the formulated optimization problem is mathematically intractable, we propose a two-stage beamforming scheme to convert the original nonconvex problem into a solvable one and obtain the beamforming weight vectors. Numerical results are finally provided to verify the effectiveness of our proposed scheme.

BACKGROUND: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. RESULTS: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. CONCLUSIONS: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.

This paper addresses the secure transmission problem of an integrated satellite-terrestrial relay networks (ISTRN), where multiple malicious eavesdroppers attempt to overhear the confidential signals from the satellite to a terrestrial user. Here, the relay is employed with multiple antenna and the decode-and-forward protocol is used. By supposing that the perfect instantaneous channel state information (CSI) of satellite-relay link and the statistical CSI of relay-user and relay-eavesdropper links are available at the relay, an optimization problem is first formulated to maximize the secrecy rate of the ISTRN. Then, the secure transmission scheme is designed to solve the optimization problem and obtain the closed-form beamforming weight vector. To further insight the secrecy performance of the proposed scheme, we derive the analytical expressions of the secrecy outage probability (SOP) of the considered ISTRN, where the satellite-relay link undergoes the shadowed-Rician fading, and the links of relay to user as well as eavesdroppers undergo the correlated Rayleigh fading. Finally, numerical results are given to confirm the superiority of the proposed scheme and the validity of the SOP analysis.

This study investigates the secrecy performance of a hybrid satellite‐terrestrial relay network (HSTRN) in the presence of multiple eavesdroppers, where the satellite link undergoes Shadowed‐Rician fading, while the terrestrial link follows Rayleigh fading. The authors suppose the direct link between the satellite and the intended user is unavailable due to heavy shadowing and adopt a multi‐antenna relay using either a decode‐and‐forward (DF) or an amplify‐and‐forward (AF) protocol to assist the transmission. By employing perfect channel state information of each link at the relay, the authors first apply receive maximal ratio combining beamforming (BF) and transmit zero‐forcing BF schemes to obtain the output signal‐to‐noise ratios (SNRs) of the intended user and eavesdroppers. Then, based on the Meijer‐G function and the moment generating function, the authors derive the analytical expressions of the ergodic secrecy rate for the considered HSTRN for both DF and AF protocols. Finally, Monte‐Carlo simulations are conducted to validate the theoretical performance analysis and reveal the effects of certain representative parameters on the system secrecy performance.

Here, the authors propose a beamforming (BF) scheme for the downlink transmission of a hybrid satellite–terrestrial network. The authors first formulate a constrained optimisation problem to maximise the minimal quality‐of‐service of the terrestrial users subject to the interference constraint of the satellite user and the transmit power budget of the base station (BS). Then, by using the uplink–downlink duality theory, the authors design an iterative algorithm to obtain the optimal BF weight vectors as well as the corresponding power coefficients of the considered system. Furthermore, the analytical downlink BF weight vectors are obtained through the equivalence between signal‐to‐leakage‐and‐noise ratio and minimum mean square error. Simulation results are provided to demonstrate the effectiveness of the proposed BF method.

This letter proposes a beamforming (BF) scheme to enhance wireless information and power transfer in terrestrial cellular networks coexisting with satellite networks. By assuming that the energy receivers are the potential eavesdroppers overhearing signals intended for information receivers (IRs), we first formulate a constrained optimization problem to maximize the minimal achievable secrecy rate of the IRs subject to the constraints of energy harvest requirement, interference threshold, and transmit power budget. Through exploiting the sequential convex approximation method, we convert the original problem into a linear one with a series of linear matrix inequality and second-order cone constraints. An iterative algorithm is then proposed to obtain the BF weight vectors. Finally, simulation results demonstrate the effectiveness and superiority of the proposed scheme.

In this paper, we present a robust beamforming (BF) scheme to improve the physical layer security (PLS) of a cognitive satellite terrestrial network (CSTN) at millimeter wave (mmWave) frequency. By employing the standard recommendations and the mmWave propagation model, a PLS framework is first defined for the CSTN in the presence of multiple eavesdroppers (Eves). A constrained optimization problem is then formulated to maximize the worst-case achievable secrecy rate of the cellular user subject to an allowable interference level for the satellite user. By expressing the imperfect Eve''s channel state information (CSI) as a combination of many given angle-of-arrival (AOA) based discrete sets, we propose a method to transform the worst-case optimization problem into a min-max problem and then develop an iterative BF scheme to yield an analytical solution for the weight vectors. Finally, simulation results confirming the effectiveness and superiority of the proposed BF scheme are provided.

Presents a collection of slides covering the following: software architecture; Big Data; data analysis; and SQL.

In this paper, we study the energy efficiency of secure communication in an underlay cognitive radio network (CRN). We first formulate an optimization problem to maximize the secrecy energy efficiency (SEE) while meeting the quality-of-service (QoS) requirement for the primary user and the transmit power constraint at each base station. Since the problem is non-convex and very difficult to solve, we then convert the original fractional form into a subtractive one, and adopt the difference of two-convex functions (D.C.) approximation method to obtain an equivalent convex problem. Furthermore, a two-layer iterative algorithm is presented to solve the problem and obtain the optimal beamforming (BF) weight vectors. Finally, numerical results are provided to demonstrate the superiority of the proposed scheme.

The integration of cognitive radio (CR) into satellite networks is recognized as an effective strategy to enhance the efficiency of radio spectrum. This paper investigates the ergodic capacity of a multiple antenna cognitive satellite terrestrial network, where the secondary terrestrial system can coexist with the primary satellite system as long as the interference imposed from the secondary user (SU) to the primary user (PU) is below a predefined threshold. Specifically, the Meijer-G function based analytical expression for the ergodic capacity of the secondary network is derived, which not only provides an efficient means to evaluate the system performance but also characterize the impact of various channel parameters on the network. Finally, simulation results are provided to demonstrate the validity of the theoretical analysis.

This paper is concerned with the energy efficiency of secure transmission in an underlay cognitive relay network (CRN), where a secondary source communicates with a secondary destination via a multi-antenna relay in the presence of an eavesdropper. We first establish an optimization problem to maximize the secrecy energy efficiency (SEE) under the constraints of data rate and transmit power of the cognitive transmission as well as the interference limitation to the primary user. Then, we recast the original non-convex problem in fractional form into an equivalent subtractive one with an additional rank-one constraint. Moreover, we incorporate the rank-one constraint into the objective function as the penalty term and apply the difference of two-convex functions (D.C.) approach to obtain an equivalent convex problem. Finally, we present an iterative algorithm to obtain the optimal solution for the SEE maximization problem in the CRN. Numerical results are provided to demonstrate the effectiveness of the proposed scheme.

In recent ten years, lots of new applications emerged, such as AI, big data and cloud. Though the workloads of these applications are very diverse, they demand huge resource of data center. In contrast, the silicon technology moves slower and slower because the Moore's law is going to the end. Consequently, the data center building from commodity hardware cannot provide enough cost-efficiency and power-efficiency. To meet the increasingly resource needs of emerging applications, the scale of data center is become much larger and larger. It consumes huge power and cost of hardware. From the business perspective, the slow development of hardware technology limits the value creation of emerging applications.

This paper presents a joint beamforming scheme for secure communication in cognitive hybrid satellite- terrestrial network (HSTN). In this network, a multibeam satellite communication network termed as the primary network under the intercept of an eavesdropper shares spectrum with a terrestrial network termed as the secondary network. Specifically by considering that the powers of both the primary and secondary transmitters are limited to the certain values, we aim to maximize the secrecy rate for the primary user (PU), while the quality- of-service (QoS) of the secondary user (SU) is satisfied, and set up a constrained optimization problem. Since the optimization problem is nonconvex and its solution is very difficult to be obtained, we propose a reformulation technique to convert the objective function into a second order cone constrain. Thus, standard numerical packages and randomization techniques can be used to calculate the beamforming (BF) vectors of the satellite and base station (BS). Simulation results are provided to verify that with joint BF algorithm, the network can maintain a sufficient QoS of the SU, while providing high secrecy rate of the PU.

In this paper, we propose a joint pilot and data power allocation scheme aiming to improve the energy efficiency of time division duplexing (TDD) massive multi-user multiple-input multiple-output (MU-MIMO) communication systems for both uplink and downlink transmission. The proposed scheme uses a maximum-ratio combining (MRC) detector in the uplink together with a maximum-ratio transmission (MRT) precoder in the downlink. By using minimum mean square error (MMSE) channel estimation, the total uplink and downlink transmit power is minimized under per-user signal to interference-plus-noise ratio (SINR) requirement and per-user power consumption constraints. Lower bounds of the average SINR are derived and used in the power allocation algorithm in order to simplify the optimization problem. The tightness of the derived SINR lower bounds and the advantage of the proposed power saving scheme as compared to equal power allocation among all users are validated by computer simulation.

This paper investigates the physical layer security of a satellite network, whose downlink spectral resource is shared with a terrestrial cellular network. We propose to employ a multi-antenna base station (BS) as a source of green interference to enhance secure transmission in the satellite network. By taking the mutual interference between these two networks into account, we first formulate a constrained optimization problem to maximize the instantaneous rate of the terrestrial user while satisfying the interference probability constraint of the satellite user. Then, with the assumption that imperfect channel state information (CSI) and statistical CSI of the link between the BS and satellite user are available at the BS, we present two beamforming (BF) schemes, namely, hybrid zero-forcing and partial zero-forcing to solve the optimization problem and obtain the BF weight vectors in a closed form. Moreover, we analyze the secrecy performance of primary satellite network by considering two practical scenarios, namely: Scenario I, the eavesdroppers CSI is unknown at the satellite and Scenario II, the eavesdroppers CSI is known at the satellite. Specifically, we derive the analytical expressions for the secrecy outage probability for Scenario I and the average secrecy rate for Scenario II. Finally, numerical results are provided to confirm the superiority of the proposed BF schemes and the validity of the performance analysis, as well as demonstrate the impacts of various parameters on the secrecy performance of the satellite network.

In this paper, we address the issue of joint beamforming (BF) and power control for a device-to-device (D2D) communication underlaying cellular network, where the wireless channels of the D2D link and the base station to user equipment link experience Rician and correlated Rayleigh fading, respectively. Based on the property of the integral network, we first formulate a constrained optimization problem to minimize the total transmit power of the devices in the network, while meeting the quality-of-service requirement of both the D2D and cellular users and suppressing the mutual interference to a certain level. Then, by adopting the available statistical channel state information and proposing an approximation method to relax the constraints, a support-vector-machine-based algorithm is presented to solve the optimization problem for the transmit powers and BF weight vectors of each user. Furthermore, we derive the analytical expressions for the cumulative density function and the generalized moments of the output signal-to-interference-plus-noise ratios, thereby developing some novel theoretical formulas for the ergodic capacity and the average symbol error rate of each user in the network. Finally, computer simulation results are provided to demonstrate the validity and efficiency of the proposed scheme and its performance analysis.

In the last several years hundreds of thousands of SSDs have been deployed in the data centers of Baidu, China's largest Internet search company. Currently only 40\% or less of the raw bandwidth of the flash memory in the SSDs is delivered by the storage system to the applications. Moreover, because of space over-provisioning in the SSD to accommodate non-sequential or random writes, and additionally, parity coding across flash channels, typically only 50-70\% of the raw capacity of a commodity SSD can be used for user data. Given the large scale of Baidu's data center, making the most effective use of its SSDs is of great importance. Specifically, we seek to maximize both bandwidth and usable capacity.

Various key-value (KV) stores are widely employed for data management to support Internet services as they offer higher efficiency, scalability, and availability than relational database systems. The log-structured merge tree (LSM-tree) based KV stores have attracted growing attention because they can eliminate random writes and maintain acceptable read performance. Recently, as the price per unit capacity of NAND flash decreases, solid state disks (SSDs) have been extensively adopted in enterprise-scale data centers to provide high I/O bandwidth and low access latency. However, it is inefficient to naively combine LSM-tree-based KV stores with SSDs, as the high parallelism enabled within the SSD cannot be fully exploited. Current LSM-tree-based KV stores are designed without assuming SSD's multi-channel architecture.

This article consists of a collection of slides from the author's conference presentation on the special features, system design and architectures, processing capabilities, and targeted markets for Baidu's family of software defined accelerator products (SDA) for large scale deep neural network (DNN) systems.

This paper addresses robust beamforming (BF) design for the uplink transmission of wireless networks, where the base station (BS) equipped with a very large number of antennas communicates with multiple users on the same frequency band simultaneously. Based on the assumption that the wireless channels undergo correlated Rayleigh fading, we first formulate an optimization problem to maximize the output signal-to-interference-plus-noise ratio (SINR) of the intended users. Then, by using the fact that channel uncertainty is norm-bounded and imperfect channel state information (CSI) is available at the BS, we transform the optimization problem to a support vector machine (SVM) regression one, and obtain the robust solution for the BF weight vectors by means of quadratic programming (QP) technique or iterative reweighted least squares (IRWLS) procedure. The computational cost of the proposed robust BF scheme depends on the number of channel vector samples rather than that of the antennas, thus it is suitable for the wireless systems with large-scale antennas. Finally, the efficiency and superiority of the proposed new scheme are confirmed through computer simulation.

In this paper, we address the problem of beamforming (BF) design in a cognitive relay network (CRN), where the cognitive network not only shares the spectrum with the primary one but also acts as a relay to assist the primary signal transmission. Considering that all of the wireless channels are subject to the correlated Rayleigh fading distribution, we first formulate a constrained optimization problem to minimize the total transmit power of the cognitive base station (CBS) with multiple antennas, while guaranteeing the quality-of-service (QoS) of the primary and secondary users and keeping the mutual interference below an acceptable level. Then, by adopting the available partial channel state information (CSI), and proposing an approximation method to relax the constraints, a support vector machine (SVM) based algorithm is presented to solve the optimization problem for the BF weight vectors. The benefit of the new approach is that the slow change of the partial CSI can be taken into account. Finally, computer simulation results are provided to confirm the superiority of the proposed BF design strategy.

NAND flash based solid state drives (SSDs) have been widely adopted as storage devices in modern data centers to provide high performance I/O services. Recently, researchers proposed several schemes to improve energy efficiency of the system by off-loading specific computation tasks from generic processors to local processing elements in SSD controllers. However, it is inefficient to directly apply these approaches to the web-scale data analysis system equipped with modern SSDs using FPGA based controllers. More important, the design schemess proposed in prior work cannot work with our target system. In order to overcome the limitation, we present our Active SSD design, considering unique features of computation tasks in web-scale data analysis. In addition, we address an important issue about interference between normal data processing and local computation in Active SSDs. The detailed architecture of our Active SSD is described, and a prototype is implemented. Moreover, the modification to the whole system is also introduced to enable the Active SSD. Experimental results based on real applications show that the energy efficiency can be significantly improved with our design.

In the large scale data processing of Internet, data compression and decompression is a very important technology which can significantly improve the valid capacity of the disk and the valid bandwidth of IO, which can reduce the costs of IDC and accelerate application programs. This paper describes a low-cost FPGA hardware architecture of GZIP compression and decompression, which has been applied to IDC services successfully. Depending on different applications, the disk IO utilization has been improved by 300 to 500 percent, and the programs are accelerated by 30% to 200%, while 1 to 3 CPU-core resources could be released.