Abstract. Many problems in linear algebra—such as those arising from non-Hermitian physics, transcorrelated quantum chemistry, and differential equations—can be solved on a quantum computer by processing eigenvalues of the non-normal input matrices. However, the existing Quantum Singular Value Transformation (QSVT) framework is ill-suited for this task because eigenvalues and singular values are different in general. We present a Quantum EigenValue Transformation (QEVT) framework for applying arbitrary polynomial transformations on eigenvalues of block-encoded non-normal operators and a related Quantum EigenValue Estimation (QEVE) algorithm for operators with real spectra. QEVT has query complexity to the block encoding nearly recovering that of the QSVT for a Hermitian input, and QEVE achieves the Heisenberg-limited scaling for diagonalizable input matrices. As applications, we develop a linear differential equation solver with strictly linear time query complexity for average-case diagonalizable operators, as well as a ground state preparation algorithm that upgrades previous nearly optimal results for Hermitian Hamiltonians to diagonalizable matrices with real spectra. Underpinning our algorithms is an efficient method to prepare a quantum superposition of Faber polynomials, which generalize the nearly-best uniform approximation properties of Chebyshev polynomials to the complex plane. Of independent interest, we also develop techniques to generate [Formula: see text] Fourier coefficients with [Formula: see text] gates compared to prior approaches with linear cost.

In the current era of data-intensive applications, the demand for high-performance, cost-effective storage solutions is paramount. This paper introduces a Space-Performance Cost Model for key-value store, designed to guide cost-effective storage configuration decisions. The model quantifies the trade-offs between performance and storage costs, providing a framework for optimizing resource allocation in large-scale data serving environments. Guided by this cost model, we present Tier-Base, a distributed key-value store developed by Ant Group that optimizes total cost by strategically synchronizing data between cache and storage tiers, maximizing resource utilization and effectively handling skewed workloads. To enhance cost-efficiency, TierBase incorporates several optimization techniques, including pre-trained data compression, elastic threading mechanisms, and the utilization of persistent memory. We detail TierBase's architecture, key components, and the implementation of cost optimization strategies. Extensive evaluations using both synthetic benchmarks and real-world workloads demonstrate TierBase's superior cost-effectiveness compared to existing solutions. Furthermore, case studies from Ant Group's production environments showcase TierBase's ability to achieve up to 62% cost reduction in primary scenarios, highlighting its practical impact in large-scale online data serving.

As learning systems grow more decentralized and dynamic, efficient and resilient training methods become increasingly important. Sparse Evolutionary Training (SET) is a scalable approach to neural network training that leverages sparse connectivity to reduce computational demands while maintaining performance. In this work, we introduce SET-motif, a topologically inspired extension of SET that replaces individual connection updates with a fixed motif-based structure during the pruning and reconnection phases. This design introduces structured variation across training epochs, increasing stochasticity and simulating conditions commonly found in real-world dynamic environments. Despite the added variability, SET-motif achieves up to 22% faster training while maintaining accuracy within 1.5% of standard SET, as demonstrated on FMNIST and lung X-ray classification tasks. These results demonstrate that SET-motif offers a lightweight and adaptable learning approach that aligns with the demands of scalable, robust training in multi-agent and decentralized systems.

Network path validation aims to give more control over the forwarding path of data packets in a path-aware network, which shields the network from security threats and allows end hosts to receive better services. Therefore, network path validation becomes a vital primitive for secure and reliable Internet services in the next generation networks. The path validation enables end hosts and intermediate router nodes to check whether a packet has followed the intended path. However, the existing solutions fail to protect path privacy and incur significant bandwidth and computation overhead on packet transferring, which degrades packet delivery performance. In this paper, we propose the StealthPath to protect path privacy and improve delivery efficiency. Firstly, StealthPath uses lightweight cryptographic primitives to generate nested proofs and ensures all nodes on the path to check the compliance of the forwarding path efficiently. Secondly, StealthPath hides the forwarding path in the proofs and reduces the proof size from linear to constant, which protects the path information and path length, and decreases the bandwidth consumption. Moreover, StealthPath allows on-path nodes to extract their proofs and the next hop address from proof without leaking on-path node index. Finally, StealthPath is proved to resist various attacks and preserves the path privacy. The experiments show that StealthPath saves nearly 60% header size and bandwidth, and is more efficient than state-of-the-art schemes.

Purpose: Analyzing the medication pattern of Chinese medicine for asthma based on data mining technology. Method: We searched the relevant prescriptions for the treatment of asthma included in the Cloud Platform of Ancient and Modern Medical Cases since its establishment, screened the literature according to the inclusion and exclusion criteria, and used Microsoft Excel 2010 to carry out the frequency statistics of single medicines and drug combinations, and the Apriori algorithm of IBM SPSS Modeler 18.0 statistical software to analyze the association rules and compounding patterns of Chinese medicines. The high frequency Chinese medicines were analyzed by using IBM SPSS Statistics 26.0 for cluster analysis. Results: A total of 418 prescriptions were included, involving 395 flavors of traditional Chinese medicine (TCM), with a cumulative frequency of 5425 times, among which the top 20 drugs in terms of frequency of use had a cumulative frequency of 2280 times, and those with a frequency of use of ⩾ 115 times were, from highest to lowest, as follows: Licorice (274), Ephedra (200), Almond (179), Banxia (176), Skullcap (117), Tuckahoe (116). The four qi were dominated by warm (35.03%) and cold (34.01%); the five flavors were dominated by pleasant (30.77%) and bitter (29.60%); and the attributed meridians were dominated by the lung meridian (21.43%) and the liver meridian (18.44%).The association analysis yielded six sets of core pairs, of which those with ⩾ 15% support and ⩾ 80% confidence were (Licorice - Almond + Ephedra), (Ephedra - Assarium), (Licorice - Tangerine peel), (Licorice - White peony), ( Licorice - Cinnamon branches), (Ephedra - Assarium + Licorice). Conclusion: The prescription of Chinese medicine for bronchial asthma is based on resolving phlegm and calming asthma, warming the lungs and resolving drinks, clearing the lungs and relieving cough, tonifying the lungs and the spleen, restoring wind and relieving spasm, and dispelling blood stasis and clearing the channels.

Objective: Using data mining technology to explore the medication law of chronic cholecystitis. Methods: We searched the literature of all acupuncture prescriptions for treating menstrual headache since the establishment of the Chinese Journal Full-text Database (CNKI), Wanfang Data Resource System Library (WF), Chinese Biomedical Literature Database (CBM), and Wipo Journal Full-text Database (VIP) databases, extracted the valid prescriptions according to the exclusion criteria, import Microsoft Office Excel 2021 to establish the database of traditional Chinese medicine in the treatment of chronic cholecystitis and used Excel 2021, IBM SPSS Modeler18.0, and IBM SPSS Statistics 27.0 to analyze the database of menstrual acupoint frequency analysis, acupoint attribution analysis, association rule analysis, and cluster analysis. Results: There was a total of 501 prescriptions, designed for 193 flavours of Chinese medicine, with a total application frequency of 5292 times, and the highest frequency of single flavour use was for Radix Bupleuri, Licorice, and Radix Paeoniae Alba, which were mainly used with cold, warm, bitter, and pungent medicines, and there was a total of 10 groups of core pairs of medicines, with 3 classes obtained through clustering.

Many problems in linear algebra-such as those arising from non-Hermitian physics, transcorrelated quantum chemistry and differential equations-can be solved on a quan-tum computer by processing eigenvalues of the non-normal input matrices. However, the existing Quantum Singular Value Transformation (QSVT) framework is ill-suited to this task, as eigenvalues and singular values are different in general. We present a Quantum EigenValue Transformation (QEVT) framework for applying arbitrary polynomial transformations on eigenvalues of block-encoded non-normal operators, and a related Quantum EigenValue Estimation (QEVE) algorithm for operators with real spectra. QEVT has query complexity to the block encoding nearly recovering that of the QSVT for a Hermitian input, and QEVE achieves the Heisenberg-limited scaling for diagonalizable input matrices. As applications, we develop a linear differential equation solver with strictly linear time query complexity for average-case diagonalizable operators, as well as a ground state preparation algorithm that upgrades previous nearly optimal results for Hermitian Hamiltonians to diagonalizable matrices with real spectra. Underpinning our algorithms is an efficient method to prepare a quantum super-position of Faber polynomials, which generalize the nearly-best uniform approximation properties of Chebyshev polynomials to the complex plane. Of independent interest, we also develop techniques to generate$n$Fourier coefficients with 0 (poly log ($n$)) gates compared to prior approaches with linear cost.

Hand motion tracking is essential in many fields, e.g., immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

The widespread application of ultra-dense and multivariate Internet of Things (IoT) benefits from Network Function Virtualization (NFV) that provides flexible frameworks and effective management. NFV leverages the virtualization technologies to integrate the existing network functions of devices into standard servers, storages, and switches. Then, the network functions are achieved in software form to displace the private, dedicated and closed network devices. However, NFV also brings instability and challenges to the network management where the network dynamics, lack of visibility, and high frequency and abundant types of faults will increase the difficulty. Therefore, diagnosing the faults embedded in the generic NFV framework is crucial for the effective adoption of NFV to the IoT environment and thus ensuring the user services. This paper summarizes the differences and connections of fault diagnosis between the NFV framework and traditional networks, and introduces the challenges faced by NFV. Moreover, we provide a comprehensive survey of the state-of-the-art fault detection methods for the NFV framework. After an in-depth discussion of the fault propagation characteristics, we further present a detailed taxonomy of the fault localization approaches. Finally, we highlight the future research directions to provide ample space for improvement in applying NFV to the IoT environment.

Privacy-preserving range query, which allows the server to implement secure and efficient range query on encrypted data, has been widely studied in recent years. Existing privacy-preserving range query schemes can realize effective range query, but usually suffer from the low efficiency and security. In order to solve the above issues, we propose an Efficient and Privacy-preserving encode-based Range Query over encrypted cloud data (namely basic EPRQ), which encodes the data and range by using Range Encode (REncoder), and then encrypts the codes via Additional Symmetric-Key Hidden Vector Encryption (ASHVE) technology. The basic EPRQ can achieve effective range query while ensuring privacy protection. Then, we split the codes to reduce the storage cost. We further propose an improved scheme, EPRQ+, which constructs a binary tree-based index to achieve faster-than-linear retrieval. Finally, our formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are feasible in practice, where EPRQ+ scheme improves the storage efficiency by about 4 times and the query efficiency by about 8 times compared to the basic EPRQ.

The Public Key Infrastructure (PKI) system is the cornerstone of today's security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users' privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to$O(n\sqrt{n})$with constant rounds, as opposed to$O(n\log n)$with$O(n)$round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.

With the popularity of cloud storage, increasing users begin to outsource data to the cloud. In order to resist possible data analysis for centralized outsourced data and improve the fault tolerance, users prefer to distribute data to cloud servers of different cloud service providers. However, once the data have been outsourced, it will be out of user’s control and many security issues may occur, such as outsourced data being illegally tamper with, or rarely accessed data being secretly deleted. In this article, we propose a decentralized self-auditing scheme for multi-cloud storage, called DSAS. First, based on the symmetric balanced incomplete block design, DSAS achieves integrity verification for outsourced data via the interactions of cloud servers and the auditing costs are shared by the participating CSs. Second, DSAS can locate misbehavior cloud server with low computation costs, and resist denial of service attack initiated by malicious cloud servers which attempts to destroy the audit. Third, DSAS can recover the corrupted data without fetching data, and support the revocation of cloud servers and batch auditing. Finally, security proof and function evaluation show that DSAS has comprehensive security and functionality, and performance simulations and experiment results show that DSAS is efficient.

Secure data aggregation for smart grid aims to protect the privacy of individual data and guarantee the utility of big data. To protect user’s privacy in data aggregation, public-key-based homomorphic encryption and masking-value-based methods are adopted in existing works. However, public-key-based homomorphic encryption causes unaffordable computation costs for smart meters (SMs), while the masking-value-based works suffer from inefficient communication. Therefore, we propose a lightweight and communication efficient data aggregation (LCEDA) scheme for smart grid. First, LCEDA allows SMs to freely form the aggregation zone at lower communication and computation costs. Second, in order to ensure forward security of individual data, LCEDA achieves efficient update of masking value share, which greatly saves the complexity and computation costs compared with the existing schemes. Third, LCEDA supports dynamic enrollment and revocation of SMs to solve the malfunction and migration of SMs and improve the scalability of the LCEDA scheme. Finally, the security of LCEDA is analyzed, and extensive performance evaluations and experiments demonstrate that LCEDA is more efficient and practical.

An n-qubit quantum circuit performs a unitary operation on an exponentially large, 2n-dimensional, Hilbert space, which is a major source of quantum speed-ups. We develop a new “Quantum singular value transformation” algorithm that can directly harness the advantages of exponential dimensionality by applying polynomial transformations to the singular values of a block of a unitary operator. The transformations are realized by quantum circuits with a very simple structure - typically using only a constant number of ancilla qubits - leading to optimal algorithms with appealing constant factors. We show that our framework allows describing many quantum algorithms on a high level, and enables remarkably concise proofs for many prominent quantum algorithms, ranging from optimal Hamiltonian simulation to various quantum machine learning applications. We also devise a new singular vector transformation algorithm, describe how to exponentially improve the complexity of implementing fractional queries to unitaries with a gapped spectrum, and show how to efficiently implement principal component regression. Finally, we also prove a quantum lower bound on spectral transformations.

In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing information diffusion models are built on the assumption that each individual contagion spreads independently, regardless of their interactions. Gaining insights into such interaction is crucial to understand the contagion adoption behaviors, and thus can make better predictions. In this paper, we study the contagion adoption behavior under a set of interactions, specifically, the interactions among users, contagions' contents, and sentiments, which are learned from social network structures and texts. We develop an effective and efficient interaction-aware diffusion (IAD) framework, incorporating these interactions into a unified model. We also present a generative process to distinguish user roles, a co-training method to determine contagions' categories and a new topic model to obtain topic-specific sentiments. Evaluation on the large-scale Weibo dataset demonstrates that our proposal can learn how different users, contagion categories, and sentiments interact with each other efficiently. With these interactions, we can make a more accurate prediction than the state-of-art baselines. Moreover, we can better understand how the interactions influence the propagation process and thus can suggest useful directions for information promotion or suppression in viral marketing.

Large-scale and heterogeneity of the Internet of Things (IoT) sensing networks introduce a big challenge to device connectivity. There exist some isolated nodes in randomly deployed IoT sensing networks running on a tree-typed topology due to limitations of some network parameters, which reduces network connectivity. In this paper, a connectivity enhancement scheme for the sensing networks of the IoT is proposed based on link transformation. Under constraints of network depth and the number of child nodes, we boost capability of an in-network node to connect more isolated nodes by reducing its or ancestors' depth. Furthermore, three-level node shifting is utilized to take full advantage of network locality, thus highly improving ability of a potential parent node to accept connection request of an isolated node. Finally, when failing to reduce depth of a node and disabling to shift out a child node of a parent node, the scheme exploits node swapping to improve present link status, thus enabling further some isolated nodes to join into the sensing networks. Our simulation results show that the proposed scheme can raise proportion of joined nodes and effectively enhance connectivity of the sensing networks in the IoT.

For the networked multi-agent system with arbitrary-order time-delayed agent dynamics, the parametric H∞design method of the decentralized PID controller is proposed in this paper. The closed-loop framework representation is first given for the multi-agent system with the decentralized PID controller imposed on each agent. Based on this close-loop framework, the H∞performance criterion of the entire system is transformed into several local H∞performance constraints of the subsystem which is related to the eigenvalues of the Laplacian matrix. Thus, the design problem of the decentralized H∞PID controller is converted to the stabilization problem of the PID controller simultaneously for a family of complex quasipolynomials. Then, two parametric approaches are given to determine the region of the PID control parameters that can guarantee the stability of the complex quasipolynomial. Finally, the decentralized H∞PID controller is derived by finding the intersection of the stabilizing PID regions for all resultant quasipolynomials.