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Rare-earth-ion (REI) ensembles in crystals have remarkable optical and spin properties characterized by narrow homogeneous linewidths relative to the inhomogeneous ensemble broadening. This makes it possible to precisely tailor the ensemble spectral density and therefore the absorption profile by applying narrow-linewidth radiation to transfer population into auxiliary levels, a process broadly known as spectral hole burning (SHB). REI-doped crystals find applications in information processing, both classical (pattern recognition, filtering, spectral analysis) and quantum (photon storage), all protocols requiring suitable ensemble preparation by SHB as a first step. In Er$^{3+}$-doped materials, the longest reported hole lifetime is one minute, and longer lifetimes are desirable. Here, we report SHB and accumulated echo measurements in a scheelite crystal of CaWO$_4$ by pumping the electron spin transition of Er$^{3+}$ ions at microwave frequencies and millikelvin temperatures, with nuclear spin states of neighboring $^{183}$W atoms serving as the auxiliary levels. The lifetime of the holes and accumulated echoes rises steeply as the sample temperature is decreased, exceeding a month at 10 mK. Our results demonstrate that millikelvin temperatures can be beneficial for signal processing applications requiring long spectral hole lifetimes.

As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by Original Equipment Manufacturers (OEMs), conducting related research and industry developments can be challenging without a comprehensive understanding of the meaning of CAN messages. In this paper, we propose a fully automated reverse-engineering system, named ByCAN, to reverse engineer CAN messages. ByCAN outperforms existing research by introducing byte-level clusters and integrating multiple features at both byte and bit levels. ByCAN employs the clustering and template matching algorithms to automatically decode the specifications of CAN frames without the need for prior knowledge. Experimental results demonstrate that ByCAN achieves high accuracy in slicing and labeling performance, i.e., the identification of CAN signal boundaries and labels. In the experiments, ByCAN achieves slicing accuracy of 80.21%, slicing coverage of 95.21%, and labeling accuracy of 68.72% for general labels when analyzing the real-world CAN frames., Comment: Accept by IEEE Internet of Things Journal, 15 pages, 5 figures, 6 tables

Precise assessment of the mechanical properties of soft biological systems at the nanoscale is crucial for understanding physiology, pathology, and developing relevant drugs. Conventional atomic force microscopy (AFM)-based indentation methods suffer from uncertainties in local tip-sample interactions and model choice. This can be overcome by adopting spatially resolved nonlocal deformation sensing for mechanical analysis. However, the technique is currently limited to lifeless/static systems, due to the inadequate spatial or temporal resolution, or difficulties in differentiating the indentation-induced deformation from that associated with live activities and other external perturbations. Here, we develop an innovative dynamic nonlocal deformation sensing approach allowing both spatially and temporally resolved mechanical analysis, which achieves a tens of microsecond time-lag precision, a nanometer vertical deformation precision, and a sub-hundred nanometer lateral spatial resolution. Using oscillatory nanoindentation and spectroscopic analysis, the method can separate the indentation-caused signal from random noise, enabling live cell measurement. Using this method, we discover a distance-dependent phase of surface deformation during indentation, leading to the disclosure of surface tension effects (capillarity) in the mechanical response of live cells upon AFM indentation. A viscoelastic model with surface tension is used to enable simultaneous quantification of the viscoelasticity and capillarity of cell. We show that neglecting surface tension, as in conventional AFM methods, would underestimate the liquid-like characteristics and overestimate the apparent viscoelastic modulus of cells. The study lays down a foundation for understanding a broad range of elastocapillarity-related interfacial mechanics and mechanobiological processes in live cells., Comment: 30 pages (4 figures) + 25 pages (20 figures)

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it's vulnerable to the $1\%$ attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (\textsc{TbDd}) framework, crafted to counter shard collusion risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, \textsc{TbDd} discerns node types and performs targeted resharding against potential threats. The model maximizes tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Rigorous evaluations prove \textsc{TbDd}'s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

The correlations of fluctuations are key to studying fundamental quantum physics and quantum many-body dynamics. They are also useful information for understanding and combating decoherence in quantum technology. Nonlinear spectroscopy and noise spectroscopy are powerful tools to characterize fluctuations, but they can access only very few among the many types of higher-order correlations. A systematic quantum sensing approach, called quantum nonlinear spectroscopy (QNS), is recently proposed for extracting arbitrary types and orders of time-ordered correlations, using sequential weak measurement via a spin quantum sensor. However, the requirement of a central spin as the quantum sensor limits the versatility of the QNS since usually a central spin interacts only with a small number of particles in proximity and the measurement of single spins needs stringent conditions. Here we propose to employ the polarization (a pseudo-spin) of a coherent light beam as a quantum sensor for QNS. After interacting with a target system (such as a transparent magnetic material), the small Faraday rotation of the linearly polarized light can be measured, which constitutes a weak measurement of the magnetization in the target system. The correlated difference photon counts of a certain numbers of measurement shots can be made proportional to a certain type and order of correlations of the magnetic fluctuations in the material. This protocol of QNS is advantageous for studying quantum many-body systems.

As promising quantum sensors, nitrogen-vacancy (NV) centers in diamond have been widely used in frontier studies in condensed matter physics, material sciences, and life sciences. In practical applications, weak laser excitation is favorable as it reduces the side effects of laser irradiation, for example, phototoxicity and heating. Here we report a combined theoretical and experimental study of optically detected magnetic resonance (ODMR) of NV-center ensembles under weak 532-nm laser excitation. In this regime, both the width and splitting of ODMR spectra decrease with increasing laser power. This power dependence is reproduced with a model considering laser-induced charge neutralization of NV--N+ pairs, which alters the local electric field environment. These results are important for understanding and designing NV-based quantum sensing in light-sensitive applications.

We for the first time propose, implement, and evaluate a practical Split Unlearning framework by enabling SISA-based machine unlearning (SP'21) in Split Learning (SL). We introduce SplitWiper and SplitWiper+, which leverage the inherent "Sharded" structure of SL and address the issues where existing SL methods compromise the "Isolated" principle of SISA due to the tight coupling between clients and the server. This coupling involves frequent bidirectional data flows and iterative training across all clients. We resolve these challenges by designing a new one-way-one-off propagation scheme. Our design decouples the propagation of neural signals between clients and between clients and the server, enabling SISA-based unlearning in SplitWiper, even with absent clients. SplitWiper+ further enhances client label privacy against the server under this scheme by using differential privacy. Our extensive experiments across diverse data distributions and tasks demonstrate that SplitWiper, which involves only the requesting clients, achieves 0% unlearning accuracy and is over 108% more effective in retaining accuracy than existing SL methods, while maintaining constant overhead through its one-way-one-off propagation, reducing computational and communication costs by over 99%. SplitWiper+ preserves over 90% of label privacy when sharing masked labels with servers.

As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. In this paper, we consider the long-tailed distribution with the presence of Byzantine nodes in the FL scenario. A novel two-layer aggregation method is proposed for the rejection of malicious models and the advisable selection of valuable models containing tail class data information. We introduce the concept of think tank to leverage the wisdom of all participants. Preliminary experiments validate that the think tank can make effective model selections for global aggregation.

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data heterogeneity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

Location privacy is critical in vehicular networks, where drivers' trajectories and personal information can be exposed, allowing adversaries to launch data and physical attacks that threaten drivers' safety and personal security. This survey reviews comprehensively different localization techniques, including widely used ones like sensing infrastructure-based, optical vision-based, and cellular radio-based localization, and identifies inadequately addressed location privacy concerns. We classify Location Privacy Preserving Mechanisms (LPPMs) into user-side, server-side, and user-server-interface-based, and evaluate their effectiveness. Our analysis shows that the user-server-interface-based LPPMs have received insufficient attention in the literature, despite their paramount importance in vehicular networks. Further, we examine methods for balancing data utility and privacy protection for existing LPPMs in vehicular networks and highlight emerging challenges from future upper-layer location privacy attacks, wireless technologies, and network convergences. By providing insights into the relationship between localization techniques and location privacy, and evaluating the effectiveness of different LPPMs, this survey can help inform the development of future LPPMs in vehicular networks.

Learning various motor skills for quadrupedal robots is a challenging problem that requires careful design of task-specific mathematical models or reward descriptions. In this work, we propose to learn a single capable policy using deep reinforcement learning by imitating a large number of reference motions, including walking, turning, pacing, jumping, sitting, and lying. On top of the existing motion imitation framework, we first carefully design the observation space, the action space, and the reward function to improve the scalability of the learning as well as the robustness of the final policy. In addition, we adopt a novel adaptive motion sampling (AMS) method, which maintains a balance between successful and unsuccessful behaviors. This technique allows the learning algorithm to focus on challenging motor skills and avoid catastrophic forgetting. We demonstrate that the learned policy can exhibit diverse behaviors in simulation by successfully tracking both the training dataset and out-of-distribution trajectories. We also validate the importance of the proposed learning formulation and the adaptive motion sampling scheme by conducting experiments.

Extracting useful signals is key to both classical and quantum technologies. Conventional noise filtering methods rely on different patterns of signal and noise in frequency or time domains, thus limiting their scope of application, especially in quantum sensing. Here, we propose a signal-nature-based (not signal-pattern-based) approach which singles out a quantum signal from its classical noise background by employing the intrinsic quantum nature of the system. We design a novel protocol to extract the Quantum Correlation signal and use it to single out the signal of a remote nuclear spin from its overwhelming classical noise backgrounds, which is impossible to be accomplished by conventional filter methods. Our work demonstrates the quantum/classical nature as a new degree of freedom in quantum sensing. The further generalization of this quantum nature-based method opens a new direction in quantum research.

Short, partially complementary, single-stranded (ss)DNA strands can form nanostructures with a wide variety of shapes and mechanical properties. It is well known that semiflexible, linear dsDNA can undergo an isotropic to nematic (IN) phase transition and that sufficiently bent structures can form a biaxial nematic phase. Here we use numerical simulations to explore how the phase behaviour of linear DNA constructs changes as we tune the mechanical properties of the constituent DNA by changing the nucleotide sequence. The IN phase transition can be suppressed in so-called DNA 'nunchakus': structures consisting of two rigid dsDNA arms, separated by a sufficiently flexible spacer. In this paper, we use simulations to explore what phase behavior to expect for different linear DNA constructs. To this end, we first performed numerical simulations exploring the structural properties of a number of different DNA oligonucleotides, using the oxDNA package. We then used the structural information generated in the oxDNA simulations to construct more coarse-grained models of the rodlike, bent-core, and nunchaku DNA. These coarse-grained models were used to explore the phase behavior of suspensions of the various DNA constructs. The approach explored in this paper makes it possible to 'design' the phase behavior of DNA constructs by a suitable choice of the constituent nucleotide sequence.

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value., Comment: accepted by IEEE TCOM in Jan. 2023

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

'Soft' patchy surfactant micelles have become an additional building tool in self-assembling systems. The triblock copolymer, Pluronic F108, forms spherical micelles in aqueous solutions upon heating leading to a simple phase diagram with a micellar crystalline solid at higher temperatures and concentrations. Here we report the strong influence of end-functionalising the chain ends either with an azide or azide-DNA complex on the systems' phase behaviour. We find that the azide(N3)- functionalisation renders the chain ends weakly hydrophobic at lower temperatures, causing them to self-assemble into flower-micelles. This hydrophobicity increases with increasing temperature and poses a competing self-assembling mechanism to the solvent induces hydrophobic interactions between the middle-blocks of F108 at higher temperatures and leads to a macroscopic phase separation that is absent in the pure F108 system. However, when we attached short, hydrophilic single-stranded (ss)DNA to the azide groups via click chemistry the chain ends became 'sticky' due to DNA hybridisation below the melting temperature of the complementary ssDNA ends while reverting to hydrophilic behaviour above. We characterise their structural and rheological properties via Dynamic Light Scattering (DLS) and DLS-based passive microrheology with an improved time-frequency domain inversion step. We present the structural behaviour of dilute and semi-dilute solutions of the original F108 system and compare the results with solutions containing either the F108- azide (F108-N3) or partially DNA-functionalised F108-azide chains. Our DLS and microrheology studies inform us on how the attachment of azide groups on F108 changes the mechanical and structural properties of micellar fluids pioneering further characterisation and design of these hybrid systems., Comment: Ready to submit to Polymers - open access journal of polymer science published semimonthly online by MDPI. 23 pages, 8 figures

Uplink sensing in perceptive mobile networks (PMNs), which uses uplink communication signals for sensing the environment around a base station, faces challenging issues of clock asynchronism and the requirement of a line-of-sight (LOS) path between transmitters and receivers. The channel state information (CSI) ratio has been applied to resolve these issues, however, current research on the CSI ratio is limited to Doppler estimation in a single dynamic path. This paper proposes an advanced parameter estimation scheme that can extract multiple dynamic parameters, including Doppler frequency, angle-of-arrival (AoA), and delay, in a communication uplink channel and completes the localization of multiple moving targets. Our scheme is based on the multi-element Taylor series of the CSI ratio that converts a nonlinear function of sensing parameters to linear forms and enables the applications of traditional sensing algorithms. Using the truncated Taylor series, we develop novel multiple-signal-classification grid searching algorithms for estimating Doppler frequencies and AoAs and use the least-square method to obtain delays. Both experimental and simulation results are provided, demonstrating that our proposed scheme can achieve good performances for sensing both single and multiple dynamic paths, without requiring the presence of a LOS path.

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire for encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this paper, we propose a seamless transition framework that transits Web2 to Web3, named WebttCom, after exploring the connotation of Web3 and the key differences between Web2 and Web3 applications. We also provide a full-stack implementation as a use case to support the proposed framework, followed by interviews with five participants that show four positive and one natural response. We confirm that the proposed framework WebttCom addresses the defined research question, and the implementation well satisfies the framework WebttCom in terms of strong necessity, usability, and completeness based on the interview results., Comment: Code snippets, quantative and qualitative evaluations