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Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve relocalization on prior maps. Current range image-based networks use single-column convolution to maintain feature invariance to shifts in image columns caused by LiDAR viewpoint change.However, this raises the issues such as "restricted receptive fields" and "excessive focus on local regions", degrading the performance of networks. To address the aforementioned issues, we propose a lightweight circular convolutional Transformer network denoted as CCTNet, which boosts performance by capturing structural information in point clouds and facilitating crossdimensional interaction of spatial and channel information. Initially, a Circular Convolution Module (CCM) is introduced, expanding the network's perceptual field while maintaining feature consistency across varying LiDAR perspectives. Then, a Range Transformer Module (RTM) is proposed, which enhances place recognition accuracy in scenarios with movable objects by employing a combination of channel and spatial attention mechanisms. Furthermore, we propose an Overlap-based loss function, transforming the place recognition task from a binary loop closure classification into a regression problem linked to the overlap between LiDAR frames. Through extensive experiments on the KITTI and Ford Campus datasets, CCTNet surpasses comparable methods, achieving Recall@1 of 0.924 and 0.965, and Recall@1% of 0.990 and 0.993 on the test set, showcasing a superior performance. Results on the selfcollected dataset further demonstrate the proposed method's potential for practical implementation in complex scenarios to handle movable objects, showing improved generalization in various datasets.

Variational quantum circuits are one of the promising ways to exploit the advantages of quantum computing in the noisy intermediate-scale quantum technology era. The design of the quantum circuit architecture might greatly affect the performance capability of the quantum algorithms. The quantum architecture search is the process of automatically designing quantum circuit architecture, aiming at finding the optimal quantum circuit composition architecture by the algorithm for a given task, so that the algorithm can learn to design the circuit architecture. Compared to manual design, quantum architecture search algorithms are more effective in finding quantum circuits with better performance capabilities. In this paper, based on the deep reinforcement learning, we propose an approach for quantum circuit architecture search. The sampling of the circuit architecture is learnt through reinforcement learning based controller. Layer-based search is also used to accelerate the computational efficiency of the search algorithm. Applying to data classification tasks we show that the method can search for quantum circuit architectures with better accuracies. Moreover, the circuit has a smaller number of quantum gates and parameters., Comment: Comments are welcome

Nonvolatile optical manipulation of material properties on demand is a highly sought-after feature in the advancement of future optoelectronic applications. While the discovery of such metastable transition in various materials holds good promise for achieving this goal, their practical implementation is still in the nascent stage. Here, we unravel the nature of the ultrafast laser-induced hidden state in 1T-TaS2 by systematically characterizing the electronic structure evolution throughout the reversible transition cycle. We identify it as a mixed-stacking state involving two similarly low-energy interlayer orders, which is manifested as the charge density wave phase disruption. Furthermore, our comparative experiments utilizing the single-pulse writing, pulse-train erasing and pulse-pair control explicitly reveal the distinct mechanism of the bidirectional transformations -- the ultrafast formation of the hidden state is initiated by a coherent phonon which triggers a competition of interlayer stacking orders, while its recovery to the initial state is governed by the progressive domain coarsening. Our work highlights the deterministic role of the competing interlayer orders in the nonvolatile phase transition in the layered material 1T-TaS2, and promises the coherent control of the phase transition and switching speed. More importantly, these results establish all-optical engineering of stacking orders in low-dimensional materials as a viable strategy for achieving desirable nonvolatile electronic devices.

Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, especially with sparse rewards, remains a significant challenge. The training of a DRL agent can be often trapped in a bottleneck without further progress. In this paper, we propose RICE, an innovative refining scheme for reinforcement learning that incorporates explanation methods to break through the training bottlenecks. The high-level idea of RICE is to construct a new initial state distribution that combines both the default initial states and critical states identified through explanation methods, thereby encouraging the agent to explore from the mixed initial states. Through careful design, we can theoretically guarantee that our refining scheme has a tighter sub-optimality bound. We evaluate RICE in various popular RL environments and real-world applications. The results demonstrate that RICE significantly outperforms existing refining schemes in enhancing agent performance., Comment: Accepted by ICML 2024

Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (non-IID) local data. Existing MHPFL methods focus on achieving client-level personalization, but cannot address batch-level data heterogeneity. To bridge this important gap, we propose a model-heterogeneous personalized Federated learning approach with Adaptive Feature Mixture (pFedAFM) for supervised learning tasks. It consists of three novel designs: 1) A sharing global homogeneous small feature extractor is assigned alongside each client's local heterogeneous model (consisting of a heterogeneous feature extractor and a prediction header) to facilitate cross-client knowledge fusion. The two feature extractors share the local heterogeneous model's prediction header containing rich personalized prediction knowledge to retain personalized prediction capabilities. 2) An iterative training strategy is designed to alternately train the global homogeneous small feature extractor and the local heterogeneous large model for effective global-local knowledge exchange. 3) A trainable weight vector is designed to dynamically mix the features extracted by both feature extractors to adapt to batch-level data heterogeneity. Theoretical analysis proves that pFedAFM can converge over time. Extensive experiments on 2 benchmark datasets demonstrate that it significantly outperforms 7 state-of-the-art MHPFL methods, achieving up to 7.93% accuracy improvement while incurring low communication and computation costs.

True random numbers play a critical role in secure cryptography. The generation relies on a stable and readily extractable entropy source. Here, from solution-processed structurally metastable 1T' MoTe2, we prove stable output of featureless, stochastic, and yet stable conductance noise at a broad temperature (down to 15 K) with minimal power consumption (down to 0.05 micro-W). Our characterizations and statistical analysis of the characteristics of the conductance noise suggest that the noise arises from the volatility of the stochastic polarization of the underlying ferroelectric dipoles in the 1T' MoTe2. Further, as proved in our experiments and indicated by our Monte Carlo simulation, the ferroelectric dipole polarization is a reliable entropy source with the stochastic polarization persistent and stable over time. Exploiting the conductance noise, we achieve the generation of true random numbers and demonstrate their use in common cryptographic applications, for example, password generation and data encryption. Besides, particularly, we show a privacy safeguarding approach to sensitive data that can be critical for the cryptography of neural networks. We believe our work will bring insights into the understanding of the metastable 1T' MoTe2 and, more importantly, underpin its great potential in secure cryptography.

Vector data management systems (VDMSs) have become an indispensable cornerstone in large-scale information retrieval and machine learning systems like large language models. To enhance the efficiency and flexibility of similarity search, VDMS exposes many tunable index parameters and system parameters for users to specify. However, due to the inherent characteristics of VDMS, automatic performance tuning for VDMS faces several critical challenges, which cannot be well addressed by the existing auto-tuning methods. In this paper, we introduce VDTuner, a learning-based automatic performance tuning framework for VDMS, leveraging multi-objective Bayesian optimization. VDTuner overcomes the challenges associated with VDMS by efficiently exploring a complex multi-dimensional parameter space without requiring any prior knowledge. Moreover, it is able to achieve a good balance between search speed and recall rate, delivering an optimal configuration. Extensive evaluations demonstrate that VDTuner can markedly improve VDMS performance (14.12% in search speed and 186.38% in recall rate) compared with default setting, and is more efficient compared with state-of-the-art baselines (up to 3.57 times faster in terms of tuning time). In addition, VDTuner is scalable to specific user preference and cost-aware optimization objective. VDTuner is available online at https://github.com/tiannuo-yang/VDTuner., Comment: Accepted by ICDE 2024

Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i.e., user prompt). Given its broad applications, the robustness of SAM against adversarial attacks is a critical concern. While recent works have explored adversarial attacks against a pre-defined prompt/click, their threat model is not yet realistic: (1) they often assume the user-click position is known to the attacker (point-based attack), and (2) they often operate under a white-box setting with limited transferability. In this paper, we propose a more practical region-level attack where attackers do not need to know the precise user prompt. The attack remains effective as the user clicks on any point on the target object in the image, hiding the object from SAM. Also, by adapting a spectrum transformation method, we make the attack more transferable under a black-box setting. Both control experiments and testing against real-world SAM services confirm its effectiveness.

Machine learning based phishing website detectors (ML-PWD) are a critical part of today's anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial evasions, evidenced by both academic studies and analyses of real-world adversarial phishing webpages. However, existing works mostly focused on assessing adversarial phishing webpages against ML-PWD, while neglecting a crucial aspect: investigating whether they can deceive the actual target of phishing -- the end users. In this paper, we fill this gap by conducting two user studies (n=470) to examine how human users perceive adversarial phishing webpages, spanning both synthetically crafted ones (which we create by evading a state-of-the-art ML-PWD) as well as real adversarial webpages (taken from the wild Web) that bypassed a production-grade ML-PWD. Our findings confirm that adversarial phishing is a threat to both users and ML-PWD, since most adversarial phishing webpages have comparable effectiveness on users w.r.t. unperturbed ones. However, not all adversarial perturbations are equally effective. For example, those with added typos are significantly more noticeable to users, who tend to overlook perturbations of higher visual magnitude (such as replacing the background). We also show that users' self-reported frequency of visiting a brand's website has a statistically negative correlation with their phishing detection accuracy, which is likely caused by overconfidence. We release our resources.

Reservoir computing is a recurrent neural network that has been applied across various domains in machine learning. The implementation of reservoir computing, however, often demands heavy computations for activating the reservoir. Configuring physical reservoir networks and harnessing the nonlinearity from the underlying devices for activation is an emergent solution to address the computational challenge. Herein, we analyze the feasibility of employing the nonlinearity from solution-processed molybdenum disulfide (MoS2) devices for reservoir activation. The devices, fabricated using liquid-phase exfoliated MoS2, exhibit a high-order nonlinearity achieved by Stark modulation of the MoS2 material. We demonstrate that this nonlinearity can be fitted and employed as the activation function to facilitate reservoir computing implementation. Notably, owing to the high-order nonlinearity, the network exhibits long-term synchronization and robust generalization abilities for approximating complex dynamical systems. Given the remarkable reservoir activation capability, coupled with the scalability of the device fabrication, our findings open the possibility for the physical realization of lightweight, efficient reservoir computing for, for instance, signal classification, motion tracking, and pattern recognition of complex time series as well as secure cryptography. As an example, we show the network can be appointed to generate chaotic random numbers for secure data encryption.

Polynomial multiplication is a fundamental problem in symbolic computation. There are efficient methods for the multiplication of two univariate polynomials. However, there is rarely efficiently nontrivial method for the multiplication of two multivariate polynomials. Therefore, we consider a new multiplication mechanism that involves a) reversibly reducing multivariate polynomials into univariate polynomials, b) calculating the product of the derived univariate polynomials by the Toom-Cook or FFT algorithm, and c) correctly recovering the product of multivariate polynomials from the product of two univariate polynomials. This work focuses on step a), expecting the degrees of the derived univariate polynomials to be as small as possible. We propose iterative Kronecker substitution, where smaller substitution exponents are selected instead of standard Kronecker substitution. We also apply the Chinese remainder theorem to polynomial reduction and find its advantages in some cases. Afterwards, we provide a hybrid reduction combining the advantages of both reduction methods. Moreover, we compare these reduction methods in terms of lower and upper bounds of the degree of the product of two derived univariate polynomials, and their computational complexities. With randomly generated multivariate polynomials, experiments show that the degree of the product of two univariate polynomials derived from the hybrid reduction can be reduced even to approximately 3% that resulting from the standard Kronecker substitution, implying an efficient subsequent multiplication of two univariate polynomials., Comment: 15 pages

Multi-scale deformable attention (MSDeformAttn) has emerged as a key mechanism in various vision tasks, demonstrating explicit superiority attributed to multi-scale grid-sampling. However, this newly introduced operator incurs irregular data access and enormous memory requirement, leading to severe PE underutilization. Meanwhile, existing approaches for attention acceleration cannot be directly applied to MSDeformAttn due to lack of support for this distinct procedure. Therefore, we propose a dedicated algorithm-architecture co-design dubbed DEFA, the first-of-its-kind method for MSDeformAttn acceleration. At the algorithm level, DEFA adopts frequency-weighted pruning and probability-aware pruning for feature maps and sampling points respectively, alleviating the memory footprint by over 80%. At the architecture level, it explores the multi-scale parallelism to boost the throughput significantly and further reduces the memory access via fine-grained layer fusion and feature map reusing. Extensively evaluated on representative benchmarks, DEFA achieves 10.1-31.9x speedup and 20.3-37.7x energy efficiency boost compared to powerful GPUs. It also rivals the related accelerators by 2.2-3.7x energy efficiency improvement while providing pioneering support for MSDeformAttn., Comment: Accepted to DAC 2024

Reconfigurable Intelligent Surfaces (RIS) show great promise in the realm of 6th generation (6G) wireless systems, particularly in the areas of localization and communication. Their cost-effectiveness and energy efficiency enable the integration of numerous passive and reflective elements, enabling near-field propagation. In this paper, we tackle the challenges of RIS-aided 3D localization and synchronization in multipath environments, focusing on the near-field of mmWave systems. Specifically, our approach involves formulating a maximum likelihood (ML) estimation problem for the channel parameters. To initiate this process, we leverage a combination of canonical polyadic decomposition (CPD) and orthogonal matching pursuit (OMP) to obtain coarse estimates of the time of arrival (ToA) and angle of departure (AoD) under the far-field approximation. Subsequently, distances are estimated using $l_{1}$-regularization based on a near-field model. Additionally, we introduce a refinement phase employing the spatial alternating generalized expectation maximization (SAGE) algorithm. Finally, a weighted least squares approach is applied to convert channel parameters into position and clock offset estimates. To extend the estimation algorithm to ultra-large (UL) RIS-assisted localization scenarios, it is further enhanced to reduce errors associated with far-field approximations, especially in the presence of significant near-field effects, achieved by narrowing the RIS aperture. Moreover, the Cram\'er-Rao Bound (CRB) is derived and the RIS phase shifts are optimized to improve the positioning accuracy. Numerical results affirm the efficacy of the proposed estimation algorithm.

Conjugated polymer fibers can be used to manufacture various soft fibrous optoelectronic devices, significantly advancing wearable devices and smart textiles. Recently, conjugated polymer-based fibrous electronic devices have been widely used in energy conversion, electrochemical sensing, and human-machine interaction. However, the insufficient mechanical properties of conjugated polymer fibers, the difficulty in solution processing semiconductors with rigid main chains, and the challenges in large-scale continuous production have limited their further development in the wearable field. We regulated the pi - pi stacking interactions in conjugated polymer molecules below their critical liquid crystal concentration by applying fluid shear stress. We implemented secondary orientation, leading to the continuous fabrication of anisotropic semiconductor fibers. This strategy enables conjugated polymers with rigid backbones to synergistically enhance the mechanical and semiconductor properties of fibers through liquid crystal spinning. Furthermore, conjugated polymer fibers, exhibiting excellent electrochemical performance and high mechanical strength (600 MPa) that essentially meet the requirements for industrialized preparation, maintain stability under extreme temperatures, radiation, and chemical reagents. Lastly, we have demonstrated logic circuits using semiconductor fiber organic electrochemical transistors, showcasing its application potential in the field of wearable fabric-style logic processing. These findings confirm the importance of the liquid crystalline state and solution control in optimizing the performance of conjugated polymer fibers, thus paving the way for developing a new generation of soft fiber semiconductor devices.

Time-delay interferometry (TDI) is essential for space-based gravitational wave (GW) missions to effectively suppress laser frequency noise and achieve targeting sensitivity. The principle of the TDI is to synthesize multiple laser link measurements between spacecraft and create virtual equal-arm interferometry. This process blends instrumental noises and tunes the response function to GW, yielding data characterized by TDI combinations. Extracting signals requires modeling GW signals under TDI operations in the frequency domain. In this work, we introduce a versatile framework, SATDI, which integrates simulation and analysis for TDI. The simulation aims to implement TDI to instrumental noises and GW signals, investigate influential factors in noise suppressions, and explore GW characterizations across different TDI configurations. The analysis component focuses on developing robust algorithms for modeling TDI responses to extract GWs and accurately determine source parameters. LISA is selected as the representative space mission to demonstrate the effectiveness of our framework. We simulate and analyze data containing GW signals from massive black hole binary coalescence, examining data from both first-generation and second-generation TDI Michelson configurations. The results not only validate the framework but also illustrate the influence of different factors on parameter estimation., Comment: 17 pages, 10 figures

Time delay interferometry (TDI) is a key technique employed in gravitational wave (GW) space missions to mitigate laser frequency noise by combining multiple laser links and establishing an equivalent equal arm interferometry. The null frequencies will be introduced in noise spectra and GW response when the periodical signal/noise is canceled in synthesized laser links. These frequencies are characteristic frequencies (CFs) of a TDI which related to its geometry of combination. In this work, we propose a second-generation TDI configuration referred to as hybrid Relay, whose CFs are only one-quarter that of the fiducial second-generation Michelson observables. We examine the performance of novel TDI configuration in laser noise cancellation and clock noise suppression and justify its essential capabilities. To assess its robustness for signal extraction, we simulate data containing GW signals from massive black hole binaries and perform parameter inferences with comparisons against the fiducial Michelson TDI configuration. The results demonstrate that the newly proposed TDI solution could be more robust than Michelson in fulfilling data analysis., Comment: 14 pages, 10 figures

Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take advantage of inter- and intra-magnification information contained in WSIs. In this work, we present GRASP, a novel graph-structured multi-magnification framework for processing WSIs in digital pathology. Our approach is designed to dynamically emulate the pathologist's behavior in handling WSIs and benefits from the hierarchical structure of WSIs. GRASP, which introduces a convergence-based node aggregation instead of traditional pooling mechanisms, outperforms state-of-the-art methods over two distinct cancer datasets by a margin of up to 10% balanced accuracy, while being 7 times smaller than the closest-performing state-of-the-art model in terms of the number of parameters. Our results show that GRASP is dynamic in finding and consulting with different magnifications for subtyping cancers and is reliable and stable across different hyperparameters. The model's behavior has been evaluated by two expert pathologists confirming the interpretability of the model's dynamic. We also provide a theoretical foundation, along with empirical evidence, for our work, explaining how GRASP interacts with different magnifications and nodes in the graph to make predictions. We believe that the strong characteristics yet simple structure of GRASP will encourage the development of interpretable, structure-based designs for WSI representation in digital pathology. Furthermore, we publish two large graph datasets of rare Ovarian and Bladder cancers to contribute to the field., Comment: Early version: To be updated

Federated learning (FL) has been widely adopted for collaborative training on decentralized data. However, it faces the challenges of data, system, and model heterogeneity. This has inspired the emergence of model-heterogeneous personalized federated learning (MHPFL). Nevertheless, the problem of ensuring data and model privacy, while achieving good model performance and keeping communication and computation costs low remains open in MHPFL. To address this problem, we propose a model-heterogeneous personalized Federated learning with Mixture of Experts (pFedMoE) method. It assigns a shared homogeneous small feature extractor and a local gating network for each client's local heterogeneous large model. Firstly, during local training, the local heterogeneous model's feature extractor acts as a local expert for personalized feature (representation) extraction, while the shared homogeneous small feature extractor serves as a global expert for generalized feature extraction. The local gating network produces personalized weights for extracted representations from both experts on each data sample. The three models form a local heterogeneous MoE. The weighted mixed representation fuses generalized and personalized features and is processed by the local heterogeneous large model's header with personalized prediction information. The MoE and prediction header are updated simultaneously. Secondly, the trained local homogeneous small feature extractors are sent to the server for cross-client information fusion via aggregation. Overall, pFedMoE enhances local model personalization at a fine-grained data level, while supporting model heterogeneity.

Computational experiments have emerged as a valuable method for studying complex systems, involving the algorithmization of counterfactuals. However, accurately representing real social systems in Agent-based Modeling (ABM) is challenging due to the diverse and intricate characteristics of humans, including bounded rationality and heterogeneity. To address this limitation, the integration of Large Language Models (LLMs) has been proposed, enabling agents to possess anthropomorphic abilities such as complex reasoning and autonomous learning. These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM. Nonetheless, the absence of explicit explainability in LLMs significantly hinders their application in the social sciences. Conversely, computational experiments excel in providing causal analysis of individual behaviors and complex phenomena. Thus, combining computational experiments with LLM-based Agent holds substantial research potential. This paper aims to present a comprehensive exploration of this fusion. Primarily, it outlines the historical development of agent structures and their evolution into artificial societies, emphasizing their importance in computational experiments. Then it elucidates the advantages that computational experiments and LLM-based Agents offer each other, considering the perspectives of LLM-based Agent for computational experiments and vice versa. Finally, this paper addresses the challenges and future trends in this research domain, offering guidance for subsequent related studies.

Molybdenum disulfide (MoS2) has drawn great interest for tunable photonics and optoelectronics advancement. Its solution processing, though scalable, results in randomly networked ensembles of discrete nanosheets with compromised properties for tunable device fabrication. Here, we show via density-functional theory calculations that the electronic structure of the individual solution-processed nanosheets can be modulated by external electric fields collectively. Particularly, the nanosheets can form Stark ladders, leading to variations in the underlying optical transition processes and thus, tunable macroscopic optical properties of the ensembles. We experimentally confirm the macroscopic electro-optical modulation employing solution-processed thin-films of MoS2 and ferroelectric P(VDF-TrFE), and prove that the localized polarization fields of P(VDF-TrFE) can modulate the optical properties of MoS2, specifically, the optical absorption and photoluminescence on a macroscopic scale. Given the scalability of solution processing, our results underpin the potential of electro-optical modulation of solution-processed MoS2 for scalable tunable photonics and optoelectronics. As an illustrative example, we successfully demonstrate solution-processed electro-absorption modulators., Comment: Manuscript 14 pages, 5 figures. Supplementary Materials 10 pages 5 figures

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics., Comment: To be published in Unmanned Systems

We propose an algorithm to detect mini-jet clusters in high-energy nuclear collisions, by selecting a high-transverse-momentum ($p_T$) particle as a seed and assigning a clustering radius ($R$) in the pseudorapidity and azimuthal-angle space. Our PYTHIA simulations for $p$+$p$ collisions show that a scheme with a seeding $p_T$ of around 0.5 GeV/$c$ and $R$ of approximately 0.6 satisfactorily identifies mini-jet clusters. The correlation between clusters obtained in PYTHIA calculations using the algorithm exhibits the proper behavior of hard-scattering-like processes, suggesting its usefulness in isolating mini-jet-like clusters from non-hard-scattering soft processes when applied to actual nuclear-collision data, thereby allowing a closer examination of both the mini-jet and the soft mechanisms., Comment: 12 pages, 13 figures

Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performance. To improve the performance, unknown-input observers (UIOs) have been proposed. Most of the results available to design UIOs are based on explicit system models, which can be difficult or impossible to obtain in real-world applications. Data-driven techniques, on the other hand, have become a viable alternative for the design and analysis of unknown systems using only data. In this context, a novel data-driven distributed unknown-input observer (D-DUIO) for an unknown linear system is developed, which leverages solely some data collected offline, without any prior knowledge of the system matrices. In the paper, first, the design of a DUIO is investigated by resorting to a traditional model-based approach. By resorting to a Lyapunov equation, it is proved that under some conditions, the state estimates at all nodes of the DUIO achieve consensus and collectively converge to the state of the system. Moving to a data-driven approach, it is shown that the input/output/state trajectories of the system are compatible with the equations of a D-DUIO, and this allows, under suitable assumptions, to express the matrices of a possible DUIO in terms of the matrices of pre-collected data. Then, necessary and sufficient conditions for the existence of the proposed D-DUIO are given. Finally, the efficacy of the D-DUIO is illustrated by means of numerical examples.

Mode-pairing quantum key distribution (MP-QKD) can surpass the repeaterless rate-transmittance bound (Pirandola-Laurenza-Ottaviani-Banchi bound) without requiring global phase locking, exhibiting remarkable flexibility. However, MP-QKD necessitates equal communication distances in two channels, which is a challenging requirement in practical applications. To address this limitation, we extend the original MP-QKD to asymmetric cases. Our decoy-state estimation confirms that asymmetric channel transmittances and asymmetric intensities do not compromise the security of the protocol. We focus on the pulse-intensity relationship, a key factor for optimizing the performance of asymmetric MP-QKD. Unlike previous asymmetric protocols, the intensities of different bases in asymmetric MP-QKD cannot be decoupled. We introduce an optimal-pulse-intensity method, adaptable to various scenarios, to enhance key rates by calculating ideal pulse intensities. Simulation results in various representative scenarios indicate that our method effectively reduces the impact of asymmetric channel distances on MP-QKD performance, enhancing its practical applicability.

Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance and applicability. However, most existing methods focus on subtracting feature processing consumption to reduce the parameters and calculations without refining the immediate features, which leads to inadequate information in the restoration. In this paper, we propose a lightweight network termed DDistill-SR, which significantly improves the SR quality by capturing and reusing more helpful information in a static-dynamic feature distillation manner. Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off. During the training phase, the RDU learns to linearly combine multiple reparameterizable blocks by analyzing varied input statistics to enhance layer-level representation. In the inference phase, the RDU is equally converted to simple dynamic convolutions that explicitly capture robust dynamic and static feature maps. Then, the information distillation block is constructed by several RDUs to enforce hierarchical refinement and selective fusion of spatial context information. Furthermore, we propose a dynamic distillation fusion (DDF) module to enable dynamic signals aggregation and communication between hierarchical modules to further improve performance. Empirical results show that our DDistill-SR outperforms the baselines and achieves state-of-the-art results on most super-resolution domains with much fewer parameters and less computational overhead. We have released the code of DDistill-SR at https://github.com/icandle/DDistill-SR., Comment: Accepted by IEEE Transactions on Multimedia (TMM)

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client's model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines., Comment: Accepted by Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)

Creating heterostructures with graphene/graphite is a practical method for charge-doping $\alpha$-RuCl$_3$, but not sufficient to cause the insulator-to-metal transition. In this study, detailed scanning tunneling microscopy/spectroscopy measurements on $\alpha$-RuCl$_3$ with various lattice deformations reveal that both in-plane and out-of-plane lattice distortions may collapse the Mott-gap in the case of monolayer $\alpha$-RuCl$_3$ in proximity to graphite, but have little impact on its bulk form alone. In the Mott-Hubbard framework, the transition is attributed to the lattice distortion-facilitated substantial modulation of the electron correlation parameter. Observation of the orbital textures on a highly compressed monolayer $\alpha$-RuCl$_3$ flake on graphite provides valuable evidence that electrons are efficiently transferred from the heterointerface into Cl3$p$ orbitals under the lattice distortion. It is believed that the splitting of Ru $t_{2g}$ bands within the trigonal distortion of Ru-Cl-Ru octahedra bonds generated the electrons transfer pathways. The increase of the Cl3$p$ states enhance the hopping integral in the Mott-Hubbard bands, resulting in the Mott-transition. These findings suggest a new route for implementing the insulator-to-metal transition upon doping in $\alpha$-RuCl$_3$ by deforming the lattice in addition to the formation of heterostructure., Comment: 9 pages, 5 figures, Accepted for publication in Physical Review B

This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are interrelated and that OS traces offer the ideal dataset for a foundation model to grasp the intricacies of diverse OS components and their behavior in varying environments and workloads. We discuss a wide range of possibilities that then arise, from employing foundation models as policy agents to utilizing them as generators and predictors to assist traditional OS control algorithms. Our hope is that this paper spurs further research into OS foundation models and creating the next generation of operating systems for the evolving computing landscape., Comment: Machine Learning for Systems Workshop at 37th NeurIPS Conference, 2023, New Orleans, LA, USA

Recent progress on the signatures of pressure-induced high temperature superconductivity in Ruddlesden Popper (RP) nickelates (Lan+1NinO3n+1) has attracted growing interest in both theoretical calculations and experimental efforts. The fabrication of high-quality single crystalline RP nickelate thin films is critical for possible reducing the superconducting transition pressure and advancing applications in microelectronics in the future. In this study, we report the observations of an active phase transition in RP nickelate films induced by misfit strain. We found that RP nickelate films favor the perovskite structure (n = infinite) under tensile strains, while compressive strains stabilize the La3Ni2O7 (n = 2) phase. The selection of distinct phases is governed by the strain dependent formation energy and electronic configuration. In compressively strained La3Ni2O7, we experimentally determined splitting energy is ~0.2 eV and electrons prefer to occupy in-plane orbitals. First principles calculations unveil a robust coupling between strain effects and the valence state of Ni ions in RP nickelates, suggesting a dual driving force for the inevitable phase co-existence transition in RP nickelates. Our work underscores the sensitivity of RP nickelate formation to epitaxial strain, presenting a significant challenge in fabricating pure-phase RP nickelate films. Therefore, special attention to stacking defects and grain boundaries between different RP phases is essential when discussing the pressure-induced superconductivity in RP nickelates., Comment: 29 pages, 5 figures, one supplementary materials

This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based state feedback stabilizing controller can be obtained by solving a semi-definite program (SDP) on-the-fly, which automatically adapts to the changes of switching dynamics. However, when noise is present, the persistency of excitation condition based on the closed-loop data may be undermined, rendering the SDP infeasible. To address this issue, an auxiliary function-based switching control law is proposed, which only requires intermittent SDP solutions when its feasibility is guaranteed. By analyzing the relationship between the controller and the system switching times, it is shown that the proposed controller guarantees input-to-state practical stability (ISpS) of the closed-loop switched linear system, provided that the noise is bounded and the dynamics switches slowly enough. Two numerical examples are presented to verify the effectiveness of the proposed controller.

The recent discovery of high-temperature superconductivity in the Ruddlesden-Popper phase La3Ni2O7 under high pressure marks a significant breakthrough in the field of 3d transition-metal oxide superconductors. For an emerging novel class of high-Tc superconductors, it is crucial to find more analogous superconducting materials with a dedicated effort toward broadening the scope of nickelate superconductors. Here, we report on the observation of high-Tc superconductivity in the high-pressure tetragonal I4/mmm phase of La2PrNi2O7 above ~10 GPa, which is distinct from the reported orthorhombic Fmmm phase of La3Ni2O7 above 14 GPa. For La2PrNi2O7, the onset and the zero-resistance temperatures of superconductivity reach Tconset = 78.2 K and Tczero = 40 K at 15 GPa. This superconducting phase shares the samilar structural symmetry as many cuprate superconductors, providing a fresh platform to investigate underlying mechanisms of nickelate superconductors., Comment: 19 pages and 6 figures

The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-of-service (DoS) attacks and false data injection (FDI) attacks can be easily launched and have become prominent threats. While resilient control against DoS attacks has received substantial research efforts, countermeasures developed against FDI attacks have been relatively limited, particularly when explicit system models are not available. To address this gap, the present paper focuses on the design of data-driven controllers for unknown linear systems subject to FDI attacks on the actuators, utilizing input-state data. To this end, a general FDI attack model is presented, which imposes minimally constraints on the switching frequency of attack channels and the magnitude of attack matrices. A dynamic state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving two data-based semi-definite programs (SDPs) on-the-fly to yield a tight approximation of the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack, the proposed SDPs are recursively feasible and controller achieves exponential stability. Numerical examples showcase its effectiveness in mitigating the impact of FDI attacks.

Quantum materials whose atoms are arranged on a lattice of corner-sharing triangles, $\textit{i.e.}$, the kagome lattice, have recently emerged as a captivating platform for investigating exotic correlated and topological electronic phenomena. Here, we combine ultra-low temperature angle-resolved photoemission spectroscopy (ARPES) with scanning tunneling microscopy and density functional theory calculations to reveal the fascinating electronic structure of the bilayer-distorted kagome material $\textit{Ln}$Ti${_3}$Bi${_4}$, where $\textit{Ln}$ stands for Nd and Yb. Distinct from other kagome materials, $\textit{Ln}$Ti${_3}$Bi${_4}$ exhibits two-fold, rather than six-fold, symmetries, stemming from the distorted kagome lattice, which leads to a unique electronic structure. Combining experiment and theory we map out the electronic structure and discover double flat bands as well as multiple van Hove singularities (VHSs), with one VHS exhibiting higher-order characteristics near the Fermi level. Notably, in the magnetic version NdTi${_3}$Bi${_4}$, the ultra-low base temperature ARPES measurements unveil an unconventional band splitting in the band dispersions which is induced by the ferromagnetic ordering. These findings reveal the potential of bilayer-distorted kagome metals $\textit{Ln}$Ti${_3}$Bi${_4}$ as a promising platform for exploring novel emergent phases of matter at the intersection of strong correlation and magnetism.

As a privacy-preserving collaborative machine learning paradigm, federated learning (FL) has attracted significant interest from academia and the industry alike. To allow each data owner (a.k.a., FL clients) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computation and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on feature Extractor Sharing (pFedES). It incorporates a small homogeneous feature extractor into each client's heterogeneous local model. Clients train them via the proposed iterative learning method to enable the exchange of global generalized knowledge and local personalized knowledge. The small local homogeneous extractors produced after local training are uploaded to the FL server and for aggregation to facilitate easy knowledge sharing among clients. We theoretically prove that pFedES can converge over wall-to-wall time. Extensive experiments on two real-world datasets against six state-of-the-art methods demonstrate that pFedES builds the most accurate model, while incurring low communication and computation costs. Compared with the best-performing baseline, it achieves 1.61% higher test accuracy, while reducing communication and computation costs by 99.6% and 82.9%, respectively., Comment: 12 pages, 10 figures. arXiv admin note: text overlap with arXiv:2310.13283

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (I) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.