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Semantic segmentation models, like mask2former, often demand a substantial amount of manually annotated data, which is time-consuming and inefficient to acquire. Leveraging state-of-the-art text-to-image models like Midjourney and Stable Diffusion has emerged as an effective strategy for automatically generating synthetic data instead of human annotations. However, prior approaches have been constrained to synthesizing single-instance images due to the instability inherent in generating multiple instances with Stable Diffusion. To expand the domains and diversity of synthetic datasets, this paper introduces a novel paradigm named DiffuMask-Editor, which combines the Diffusion Model for Segmentation with Image Editing. By integrating multiple objects into images using Text2Image models, our method facilitates the creation of more realistic datasets that closely resemble open-world settings while simultaneously generating accurate masks. Our approach significantly reduces the laborious effort associated with manual annotation while ensuring precise mask generation. Experimental results demonstrate that synthetic data generated by DiffuMask-Editor enable segmentation methods to achieve superior performance compared to real data. Particularly in zero-shot backgrounds, DiffuMask-Editor achieves new state-of-the-art results on Unseen classes of VOC 2012. The code and models will be publicly available soon., Comment: 13 pages,4 figures

Traditional blockchain systems, such as Ethereum, typically rely on a \emph{single volatile cryptocurrency for transaction fees}. This leads to fluctuating transaction fee prices and limits the flexibility of users' payment options. To address these issues, we propose MStableChain, which leverage multiple stablecoins as native tokens for transaction fee settlements, thus ensuring stable transaction fees and flexible payment options. To address the challenges of mass adoption and practicality, we propose several core designs. To maintain compatibility with the Ethereum Virtual Machine (EVM) for mass adoption while supporting multiple native stablecoins, MStableChain employs a multi-currency units, multi-type RPCs mechanism. This mechanism enables the system to handle multiple stablecoins without altering the EVM or requiring changes to user applications. Furthermore, an oracle-based gas fee adjustment mechanism is proposed to manage exchange rates between different stablecoins, ensuring equitable transaction costs across various currencies. The system also introduces a secure, on-chain voting-based management protocol for the administrative functions related to these stablecoins. Experimental results from a prototype implementation demonstrate that MStableChain provides stable transaction fee prices, high effectiveness, and good usability., Comment: In submission to IEEE TSC

Nitrogen-vacancy (NV) centers show great potentials for nanoscale bio-sensing and bio-imaging. Nevertheless, their envisioned bio-applications suffer from intrinsic background noise due to unavoidable light scattering and autofluorescence in cells and tissues. Herein, we develop a novel all-optical modulated imaging method via physically-enabled classifier, for on-demand and direct access to NV fluorescence at pixel resolution while effectively filtering out background noise. Specifically, NV fluorescence can be modulated optically to exhibit sinusoid-like variations, providing basis for classification. We validate our method in various complex biological scenarios with fluorescence interference, ranging from cells to organisms. Notably, our classification-based approach achieves almost 10^6 times enhancement of signal-to-background ratio (SBR) for fluorescent nanodiamonds (FNDs) in neural protein imaging. We also demonstrate 4-fold contrast improvement in optically-detected magnetic resonance measurements (ODMR) of FNDs inside stained cells. Our technique offers a generic, explainable and robust solution, applicable for realistic high-fidelity imaging and sensing in challenging noise-laden scenarios.

Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to a central server. While FD has gained popularity, its vulnerability to poisoning attacks remains underexplored. To address this gap, we previously introduced FDLA (Federated Distillation Logits Attack), a method that manipulates logits communication to mislead and degrade the performance of client models. However, the impact of FDLA on participants with different identities and the effects of malicious modifications at various stages of knowledge transfer remain unexplored. To this end, we present PCFDLA (Peak-Controlled Federated Distillation Logits Attack), an advanced and more stealthy logits poisoning attack method for FD. PCFDLA enhances the effectiveness of FDLA by carefully controlling the peak values of logits to create highly misleading yet inconspicuous modifications. Furthermore, we introduce a novel metric for better evaluating attack efficacy, demonstrating that PCFDLA maintains stealth while being significantly more disruptive to victim models compared to its predecessors. Experimental results across various datasets confirm the superior impact of PCFDLA on model accuracy, solidifying its potential threat in federated distillation systems., Comment: arXiv admin note: text overlap with arXiv:2401.03685

DIffuse X-ray Explorer (DIXE) is a proposed high-resolution X-ray spectroscopic sky surveyor on the China Space Station (CSS). DIXE will focus on studying hot baryons in the Milky Way. Galactic hot baryons like the X-ray emitting Milky Way halo and eROSITA bubbles are best observed in the sky survey mode with a large field of view. DIXE will take advantage of the orbital motion of the CSS to scan a large fraction of the sky. High-resolution X-ray spectroscopy, enabled by superconducting microcalorimeters based on the transition-edge sensor (TES) technology, will probe the physical properties (e.g., temperature, density, elemental abundances, kinematics) of the Galactic hot baryons. This will complement the high-resolution imaging data obtained with the eROSITA mission. Here we present the preliminary design of DIXE. The payload consists mainly of a detector assembly and a cryogenic cooling system. The key components of the detector assembly are a microcalorimeter array and frequency-domain multiplexing readout electronics. To provide a working temperature for the detector assembly, the cooling system consists of an adiabatic demagnetization refrigerator and a mechanical cryocooler system., Comment: 12 pages, 6 figures, the full version is published by Journal of Low Temperature Physics

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. In this paper, we introduce FedCache 2.0, a novel personalized FEL architecture that simultaneously addresses these challenges. FedCache 2.0 incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) FedCache 2.0 significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) FedCache 2.0 can train splendid personalized on-device models with at least $\times$28.6 improvement in communication efficiency., Comment: 17 pages, 7 figures, 14 tables

In this work, we study the implications of GW230529 on gravity theories and the charge of black holes. The GW230529, which was initially released in O4a, is most likely neutron star-black hole (NSBH) mergers. We reanalyze the data from the GW230529 event to obtain bounds on the Einstein-dilation-Gauss-Bonnet (EdGB) gravity parameter $\sqrt{\alpha_{\rm EdGB}}$ and the electric charge of compact binary systems. The event places a $90\%$ credible upper bounds on $\sqrt{\alpha_{\rm EdGB}}$ of $\lesssim 0.298$ km. After including high order corrections of EdGB gravity, the bounds improve to $\sqrt{\alpha_{\rm EdGB}} \lesssim 0.260$ km. Analyses of GW230529 also yield a $90\%$ credible upper bounds on the combination of charge-to-mass ratio of the binary components $\zeta \lesssim 0.024$. The constraints are more stringent than those derived from previously observed single gravitational wave merger event., Comment: 7 pages, 4 figures, PRD accepted

As a distributed machine learning paradigm, federated learning (FL) is collaboratively carried out on privately owned datasets but without direct data access. Although the original intention is to allay data privacy concerns, "available but not visible" data in FL potentially brings new security threats, particularly poisoning attacks that target such "not visible" local data. Initial attempts have been made to conduct data poisoning attacks against FL systems, but cannot be fully successful due to their high chance of causing statistical anomalies. To unleash the potential for truly "invisible" attacks and build a more deterrent threat model, in this paper, a new data poisoning attack model named VagueGAN is proposed, which can generate seemingly legitimate but noisy poisoned data by untraditionally taking advantage of generative adversarial network (GAN) variants. Capable of manipulating the quality of poisoned data on demand, VagueGAN enables to trade-off attack effectiveness and stealthiness. Furthermore, a cost-effective countermeasure named Model Consistency-Based Defense (MCD) is proposed to identify GAN-poisoned data or models after finding out the consistency of GAN outputs. Extensive experiments on multiple datasets indicate that our attack method is generally much more stealthy as well as more effective in degrading FL performance with low complexity. Our defense method is also shown to be more competent in identifying GAN-poisoned data or models. The source codes are publicly available at \href{https://github.com/SSssWEIssSS/VagueGAN-Data-Poisoning-Attack-and-Its-Countermeasure}{https://github.com/SSssWEIssSS/VagueGAN-Data-Poisoning-Attack-and-Its-Countermeasure}., Comment: 18 pages, 16 figures

Photomultiplier tubes (PMTs) with large-area cathodes are increasingly being used in cosmic-ray experiments to enhance detection efficiency. The optical modules (OMs) of the High-Energy Underwater Neutrino Telescope (HUNT) have employed a brand new N6205 20-inch microchannel plate photomultiplier tube (MCP-PMT) developed by the North Night Vision Science & Technology (Nanjing) Research Institute Co. Ltd. (NNVT). In order to make the 20-inch PMT fit into the 23-inch diameter pressure-resistant glass sphere, NNVT improved the internal structure of PMT and shortened the height of PMT by more than 10~cm. The first batch of these PMTs has been delivered for preliminary research work. This paper describes a specific PMT testing platform built for the first batch of 15 MCP-PMTs, and some performance parameters of PMT, such as peak-to-valley ratio, TTS and nonliniearity, are measured. The measurement results show that the new PMT still has good performance and can meet the requirements of HUNT project.

On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers. However, training models for on-device deployment face significant challenges due to the decentralized and privacy-sensitive nature of users' data, along with end-side constraints related to network connectivity, computation efficiency, etc. Existing training paradigms, such as cloud-based training, federated learning, and transfer learning, fail to sufficiently address these practical constraints that are prevalent for devices. To overcome these challenges, we propose Privacy-Enhanced Training-as-a-Service (PTaaS), a novel service computing paradigm that provides privacy-friendly, customized AI model training for end devices. PTaaS outsources the core training process to remote and powerful cloud or edge servers, efficiently developing customized on-device models based on uploaded anonymous queries, enhancing data privacy while reducing the computation load on individual devices. We explore the definition, goals, and design principles of PTaaS, alongside emerging technologies that support the PTaaS paradigm. An architectural scheme for PTaaS is also presented, followed by a series of open problems that set the stage for future research directions in the field of PTaaS., Comment: 7 pages, 3 figures

A hierarchical triple merger (HTM) constitutes a type of event in which two successive black hole (BH) mergers occur sequentially within the observational window of gravitational wave (GW) detectors, which has important role in testing general relativity and studying BH population. In this work, we conduct an analysis to determine the feasibility of identifying HTMs from a large GW event catalog using the third-generation ground-based GW detectors. By comparing the Bhattacharyya coefficient that measures the overlap between the posterior distributions of the remnant and progenitor BH parameters, we find that the overlap between the event pair can serve as a preliminary filter, which balances between computational demand and the probability of false alarms. Following this initial, time-efficient, yet less accurate screening, a subset of potential HTM candidates will be retained. These candidates will subsequently be subjected to a more precise, albeit time-intensive, method of joint parameter estimation for verification. Ultimately, this process will enable us to robustly identify HTMs., Comment: 13 pages, 8 figures, ApJ accepted

Principal symmetric ideals were recently introduced by Harada, Seceleanu, and Sega, with a focus on their homological properties. They are ideals generated by the orbit of a single polynomial under permutations of variables in a polynomial ring. In this paper we seek to determine when a product of two principal symmetric ideals is principal symmetric and when all the powers of a principal symmetric ideal are again principal symmetric ideals. We characterize the ideals that have the latter property as being generated by polynomials invariant up to a scalar multiple under permutation of variables. Recognizing principal symmetric ideals is an open question for the purpose of which we produce certain obstructions. We also demonstrate that the Hilbert functions of symmetric monomial ideals are not all given by symmetric monomial ideals, in contrast to the non-symmetric case., Comment: A flaw in the proof of Theorem 3.5 in version 1 = Theorem 3.6 in version 2 was remedied

Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning. By optimizing local models with knowledge distillation, FD circumvents the necessity of uploading large-scale model parameters to the central server, simultaneously preserving the raw data on local clients. Despite the growing popularity of FD, there is a noticeable gap in previous works concerning the exploration of poisoning attacks within this framework. This can lead to a scant understanding of the vulnerabilities to potential adversarial actions. To this end, we introduce FDLA, a poisoning attack method tailored for FD. FDLA manipulates logit communications in FD, aiming to significantly degrade model performance on clients through misleading the discrimination of private samples. Through extensive simulation experiments across a variety of datasets, attack scenarios, and FD configurations, we demonstrate that LPA effectively compromises client model accuracy, outperforming established baseline algorithms in this regard. Our findings underscore the critical need for robust defense mechanisms in FD settings to mitigate such adversarial threats., Comment: 13 pages, 3 figures, 5 tables

Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature of real-world data, particularly its tendency to grow in volume and diversify in classes over time. This oversight results in FL methods suffering from catastrophic forgetting, where the trained models inadvertently discard previously learned information upon assimilating new data. In response to this challenge, we propose a novel Federated Class-Incremental Learning (FCIL) method, named \underline{Fed}erated \underline{C}lass-Incremental \underline{L}earning with New-Class \underline{A}ugmented \underline{S}elf-Di\underline{S}tillation (FedCLASS). The core of FedCLASS is to enrich the class scores of historical models with new class scores predicted by current models and utilize the combined knowledge for self-distillation, enabling a more sufficient and precise knowledge transfer from historical models to current models. Theoretical analyses demonstrate that FedCLASS stands on reliable foundations, considering scores of old classes predicted by historical models as conditional probabilities in the absence of new classes, and the scores of new classes predicted by current models as the conditional probabilities of class scores derived from historical models. Empirical experiments demonstrate the superiority of FedCLASS over four baseline algorithms in reducing average forgetting rate and boosting global accuracy., Comment: 9 pages, 2 figures, 4 tables

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale by the weakest end devices. To address this issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedAgg recursively organizes computing nodes among all tiers based on Bridge Sample Based Online Distillation Protocol (BSBODP), which enables every pair of parent-child computing nodes to mutually transfer and distill knowledge extracted from generated bridge samples. This design enhances the performance by exploiting the potential of larger models, with privacy constraints of FL and flexibility requirements of EECC both satisfied. Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53\% accuracy gains and remarkable improvements in convergence rate., Comment: Accepted by IEEE International Conference on Computer Communications (INFOCOM), 2024

In our previous work [1], using indirect evaluation methods we concluded that the uncertainties of the second and the third density virial coefficient, B and C, of helium-4 at 5 K calculated by various authors had been overestimated. To check the reliability of these values and appraisal of uncertainties from ab initio calculations still further, a refractive-index gas thermometry method was developed to determine simultaneously thermodynamic temperatures and density virial coefficients. Using this technique, high accuracy experimental values of B and C of helium-4 and new values of T-T90 were obtained for the range 5 K to 25 K. A direct comparison with the ab initio calculation density virial coefficients was made. Results support the conclusion of our previous work, i.e., the ab initio calculation uncertainties u(B) [J. Chem. Phys. 136, 224303 (2012)] and u(C) [J. Chem. Phys. 134, 134106 (2011)] of helium-4 were overestimated by a factor of severalfold., Comment: The structure of the article needs to be modified and the reference data needs to be updated

In this work, we propose indirect evaluation methods to check the accuracy and the uncertainty of ab initio calculated virial coefficients. To this end, we have used single-pressure refractive-index gas thermometry (SPRIGT) to estimate the impact of the second density virial coefficient B of helium-4 on temperature measurements between 5 K to 25 K. Our results, in good agreement with values of B obtained from recent ab initio calculations by Czachorowski et al. [Phys. Rev. A 102, 042810 (2020)], suggest uncertainties u(B) estimated previously by other authors were too conservative. Concerning the value of the third density virial coefficient C of helium-4, calculated ab initio by Garberoglio et al. [J. Chem. Phys. 134, 134106 (2011)], our results suggest their uncertainty u(C) is between 1.5 and 10.2 times too high., Comment: The structure of the article needs to be modified and the reference data needs to be updated

KS 1947+300 is a Be/X-ray binary. Despite its nearly circular orbit, it displays both giant and regular less intense X-ray outbursts. According to the viscous decretion disk model, such low eccentric binaries should not show periodic outbursts. We have studied the long-term optical variability of KS 1947+300 and its relationship with X-ray activity. Our objective is to investigate the origin of this variability. KS 1947+300 exhibits changes in brightness and H$\alpha$ emission on time scales from months to years. The optical and IR variability shows small amplitude changes during the active X-ray state, and a long, smooth decrease during the quiescent state. The fact that the amplitude of variability increased with wavelength suggests that the long-term decrease of the optical emission is due to the weakening of the circumstellar disk. Structural changes in the disk may also be the origin of the periodic signals with periods $\sim200$ days detected in the ZTF-{\it g} and {\it r} band light curves. We speculate that these changes are related to the mechanism that ejects matter from the photosphere of the Be star into the disk. We have also studied the X-ray variability that manifested as a series of type I outbursts after the two giant outburst in 2000 and 2013 and found that the intensity and peak orbital phase differ from outburst to outburst. The type I outbursts in KS 1947+300 are not strictly periodic. This irregularity could result from precession of the interacting points between the neutron star and the disk, namely the disk apastron and the two nodes of the disk. The long-term changes in optical continuum and line emission and the X-ray variability patterns are attributed to an evolving and distorted decretion disk., Comment: 18 pages, 10 figures

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods., Comment: 9 pages, 4 figures, 4 tables

Context. 1A 0535+262 is a high-mass X-ray binary that went into a giant X-ray outburst in 2020. During this event, the X-ray luminosity reached the highest value measured over the last 30 years. Aims. Our aim is to study the long-term variability of 1A 0535+262 before and after the 2020 major X-ray outburst and to uncover the mechanism that led to the X-ray outburst. Methods. We used the long-term photometric light curve and the equivalent widths of the H$\alpha$ and He I $\lambda$6678 lines to monitor the state of the Be star's circumstellar disk. The H$\alpha$ line profiles show evidence for V/R variability, which we revealed by fitting the H$\alpha$ spectral line profiles with two Gaussian functions. In addition, we divided our data into four periods according to the intensity of the X-ray, optical, and infrared emission. Results. The H$\alpha$ line profiles show single-peaked profiles in most cases. This is consistent with the previously reported orbital inclination of ${i}$ = $37^{\circ} \pm 2^{\circ}$. Unlike the H$\alpha$ lines, the He I $\lambda6678$ lines show a maximal intensity in October 2020, which is one month before the giant X-ray outburst in 2020. Based on the behavior of the equivalent widths of the H$\alpha$ and He I $\lambda6678$ lines, and the ${V}$-band magnitude, we find two mass ejection processes from the Be star to the Be disk on MJD 55820 and MJD 56600. The V/R quasi-period is about two\, years during 2011--2015, which is different from 1994 to 1995. Furthermore, the periods I$\to$II$\to$III$\to$IV in the $(B-V)$ color index versus $V$-band magnitude diagram constitute a cycle. From the behavior of the V/R ratio of H$\alpha$ lines, and the variability of the $V$ band, we believe that the precession of the density perturbation inside the disk is retrograde., Comment: 15 pages, 7 figures. arXiv admin note: text overlap with arXiv:2208.00151

Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN) but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target's context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness., Comment: Accepted for publication in the International Journal of Computer Vision (IJCV)

The mass ($M_{\rm TOV}$) and radius ($R_{\rm TOV}$) of the maximum-mass nonrotating neutron star (NS) play a crucial role in constraining the elusive equation of state of cold dense matter and in predicting the fate of remnants from binary neutron star (BNS) mergers. In this study, we introduce a novel method to deduce these parameters by examining the mergers of second-generation (2G) black holes (BHs) with NSs. These 2G BHs are assumed to originate from supramassive neutron stars (SMNSs) formed in BNS mergers. Since the properties of the remnant BHs arising from the collapse of SMNSs follow a universal relation governed by $M_{\rm TOV}$ and $R_{\rm TOV}$, we anticipate that by analyzing a series ($\sim 100$ detections) of mass and spin measurements of the 2G BHs using the third-generation ground-based gravitational-wave detectors, $M_{\rm TOV}$ and $R_{\rm TOV}$ can be determined with a precision of $\sim 0.01M_\odot$ and $\sim 0.6$ km, respectively., Comment: 11 pages, 5 figures, ApJ published

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which causes unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache., Comment: Accepted by IEEE Transactions on Mobile Computing (TMC)

Optical force responses underpin nanophotonic actuator design, which requires a large number of force simulations to optimize structures. Commonly used computation methods, such as the finite-difference time-domain (FDTD) method, are resource intensive and require large amounts of calculation time when multiple structures need to be compared during optimization. This research demonstrates that performing optical force calculations on periodic structures using the rigorous coupled-wave analysis method is typically on the order of 10 times faster than FDTD with sufficient accuracy to suit optical design purposes. Moreover, this speed increase is available on consumer grade laptops with a CUDA-compatible GPU avoiding the need for a high performance computing resource., Comment: 12 pages, 5 figures