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Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically distributed among diverse user groups. Existing research on fairness predominantly assumes access to the entire training data, making direct transfer to FL challenging. However, the limited existing research on fairness in FL does not effectively address two key challenges, i.e., (CH1) Current methods fail to deal with the inconsistency between fair optimization results obtained with surrogate functions and fair classification results. (CH2) Directly aggregating local fair models does not always yield a globally fair model due to non Identical and Independent data Distributions (non-IID) among clients. To address these challenges, we propose a Wasserstein Fair Federated Learning framework, namely WassFFed. To tackle CH1, we ensure that the outputs of local models, rather than the loss calculated with surrogate functions or classification results with a threshold, remain independent of various user groups. To resolve CH2, we employ a Wasserstein barycenter calculation of all local models' outputs for each user group, bringing local model outputs closer to the global output distribution to ensure consistency between the global model and local models. We conduct extensive experiments on three real-world datasets, demonstrating that WassFFed outperforms existing approaches in striking a balance between accuracy and fairness., Comment: Submitted to TKDE

Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (CFL) task presents unique challenges, particularly regarding catastrophic forgetting and non-IID input data. Existing solutions include using a replay buffer to store historical data or leveraging generative adversarial networks. Nevertheless, motivated by recent advancements in the diffusion model for generative tasks, this paper introduces DCFL, a novel framework tailored to address the challenges of CFL in dynamic distributed learning environments. Our approach harnesses the power of the conditional diffusion model to generate synthetic historical data at each local device during communication, effectively mitigating latent shifts in dynamic data distribution inputs. We provide the convergence bound for the proposed CFL framework and demonstrate its promising performance across multiple datasets, showcasing its effectiveness in tackling the complexities of CFL tasks.

Negative differential resistance (NDR) behavior observed in several transition metal oxides is crucial for developing next-generation memory devices and neuromorphic computing systems. NbO2-based memristors exhibit two regions of NDR at room temperature, making them promising candidates for such applications. Despite this potential, the physical mechanisms behind the onset and the ability to engineer these NDR regions remain unclear, hindering further development of these devices for applications. This study employed electrical transport and ultra-low frequency noise spectroscopy measurements to investigate two distinct NDR phenomena in nanoscale thin films of NbO2. By analyzing the residual current fluctuations as a function of time, we find spatially inhomogeneous and non-linear conduction near NDR-1 and a two-state switching near NDR-2, leading to an insulator-to-metal (IMT) transition. The power spectral density of the residual fluctuations exhibits significantly elevated noise magnitudes around both NDR regions, providing insights into physical mechanisms and device size scaling for electronic applications. A simple theoretical model, based on the dimerization of correlated insulators, offers a comprehensive explanation of observed transport and noise behaviors near NDRs, affirming the presence of non-linear conduction followed by an IMT connecting macroscopic device response to transport signatures at atomic level.

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated microgrids. In DER-aided blackstart, the continuous operation of these microgrids is uncertain due to the finite energy capacity of commonly used blackstart units, such as battery energy storage (BES)-based grid-forming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between microgrids and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition was developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottom-up blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

Thermal broadening of the quasi-particle peak in the spectral function is an important physical feature in many statistical systems, but difficult to calculate. Within the projective truncation approximation (PTA) of Green's function equation of motion for classical systems, we produce the spectral function with thermal broadened quasi-particles peak using an $H$-expanded basis. We demonstrate this method on two model systems, the one-variable anharmonic oscillator model and the one-dimensional classical $\phi^4$ lattice model. Comparison with exact spectral function and the molecular dynamics simulation results shows that the method is semi-quantitatively accurate. Extension of the $H$-expanded basis to PTA for quantum system is possible., Comment: 20 pages, 14 figures

The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.

Causal inference and model interpretability are gaining increasing attention, particularly in the biomedical domain. Despite recent advance, decorrelating features in nonlinear environments with human-interpretable representations remains underexplored. In this study, we introduce a novel method called causal rule generation with target trial emulation framework (CRTRE), which applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets. Extensive experiments on six healthcare datasets, including synthetic data, real-world disease collections, and MIMIC-III/IV, demonstrate the model's superior performance. Specifically, our method achieved a $\beta$ error of 0.907, outperforming DWR (1.024) and SVM (1.141). On real-world datasets, our model achieved accuracies of 0.789, 0.920, and 0.300 for Esophageal Cancer, Heart Disease, and Cauda Equina Syndrome prediction task, respectively, consistently surpassing baseline models. On the ICD code prediction tasks, it achieved AUC Macro scores of 92.8 on MIMIC-III and 96.7 on MIMIC-IV, outperforming the state-of-the-art models KEPT and MSMN. Expert evaluations further validate the model's effectiveness, causality, and interpretability.

Recent years have witnessed measurement-induced entanglement phase transitions attracting significant attention, yet the feedback-induced skin effect in tilted fields remains understudied. This work investigates entanglement relaxation and dynamical transitions in a fermionic chain subject to generalized monitoring and an applied tilted field. We show that the feedback-induced skin effect persists with tilting, though relaxation time increases. For small tilts, bipartite entanglement exhibits a widened metastable phase, transitioning from a logarithmic-law to an area-law phase. In contrast, strong tilts accelerate relaxation, leading to a single area-law phase. Moreover, due to the unique properties of the tilted chain, we examine the impact of the boundary on entanglement relaxation. This work not only deepens our understanding of the role of feedback in measurement-induced entanglement phase transitions but also enhances our comprehension of the non-Hermitian skin effect beyond the no-click limit., Comment: Any comments are welcome!

Architecture performance evaluation is the most time-consuming part of neural architecture search (NAS). Zero-Shot NAS accelerates the evaluation by utilizing zero-cost proxies instead of training. Though effective, existing zero-cost proxies require invoking backpropagations or running networks on input data, making it difficult to further accelerate the computation of proxies. To alleviate this issue, architecture topologies are used to evaluate the performance of networks in this study. We prove that particular architectural topologies decrease the local entropy of feature maps, which degrades specific features to a bias, thereby reducing network performance. Based on this proof, architectural topologies are utilized to quantify the suppression of local entropy decrease (SED) as a data-free and running-free proxy. Experimental results show that SED outperforms most state-of-the-art proxies in terms of architecture selection on five benchmarks, with computation time reduced by three orders of magnitude. We further compare the SED-based NAS with state-of-the-art proxies. SED-based NAS selects the architecture with higher accuracy and fewer parameters in only one second. The theoretical analyses of local entropy and experimental results demonstrate that the suppression of local entropy decrease facilitates selecting optimal architectures in Zero-Shot NAS., Comment: 8 pages, 2 figures. Corrected typos and latex template

Channel estimation and extrapolation are fundamental issues in MIMO communication systems. In this paper, we proposed the quasi-Newton orthogonal matching pursuit (QNOMP) approach to overcome these issues with high efficiency while maintaining accuracy. The algorithm consists of two stages on the super-resolution recovery: we first performed a cheap on-grid OMP estimation of channel parameters in the sparsity domain (e.g., delay or angle), then an off-grid optimization to achieve the super-resolution. In the off-grid stage, we employed the BFGS quasi-Newton method to jointly estimate the parameters through a multipath model, which improved the speed and accuracy significantly. Furthermore, we derived the optimal extrapolated solution in the linear minimum mean squared estimator criterion, revealed its connection with Slepian basis, and presented a practical algorithm to realize the extrapolation based on the QNOMP results. Special treatment utilizing the block sparsity nature of the considered channels was also proposed. Numerical experiments on the simulated models and CDL-C channels demonstrated the high performance and low computational complexity of QNOMP.

Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset's tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative $5.0$% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-Opus with $15.5$% to $27.6$% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model., Comment: NeKo work has been done in June 2024. NeKo LMs will be open source on https://huggingface.co/nvidia under the MIT license

In this paper, we study vanishing of Fourier coefficients of holomorphic $\eta$-quotients. We investigate examples of two different types: the first one involves integral weight CM newforms, while the second one involves half-integral weight $\eta$-quotients associated with sums of squares and Hurwitz class numbers.

The formation pathways of lenticular galaxies (S0s) in field environments remain a matter of debate. We utilize the cosmological hydrodynamic simulation, NewHorizon, to investigate the issue. We select two massive star-formation quenched S0s as our main sample. By closely tracing their physical and morphological evolution, we identify two primary formation channels: mergers and counter-rotating gas accretion. The former induces central gas inflow due to gravitational and hydrodynamic torques, triggering active central star formation which quickly depletes the gas of the galaxy. Counter-rotating gas accretion overall has a similar outcome but more exclusively through hydrodynamic collisions between the pre-existing and newly-accreted gas. Both channels lead to S0 morphology, with gas angular momentum cancellation being a crucial mechanism. These formation pathways quench star formation on a short timescale (< Gyr) compared to the timescales of environmental effects. We also discuss how counter-rotating gas accretion may explain the origin of S0s with ongoing star formation and the frequently observed gas-star misaligned kinematics in S0s., Comment: 19 pages, 16 figures, Accepted for publication in ApJ, Oct 27 2024

This paper examines the limiting variance of nearest neighbor matching estimators for average treatment effects with a fixed number of matches. We present, for the first time, a closed-form expression for this limit. Here the key is the establishment of the limiting second moment of the catchment area's volume, which resolves a question of Abadie and Imbens. At the core of our approach is a new universality theorem on the measures of high-order Voronoi cells, extending a result by Devroye, Gy\"orfi, Lugosi, and Walk., Comment: 25 pages

Given a database of bit strings $A_1,\ldots,A_m\in \{0,1\}^n$, a fundamental data structure task is to estimate the distances between a given query $B\in \{0,1\}^n$ with all the strings in the database. In addition, one might further want to ensure the integrity of the database by releasing these distance statistics in a secure manner. In this work, we propose differentially private (DP) data structures for this type of tasks, with a focus on Hamming and edit distance. On top of the strong privacy guarantees, our data structures are also time- and space-efficient. In particular, our data structure is $\epsilon$-DP against any sequence of queries of arbitrary length, and for any query $B$ such that the maximum distance to any string in the database is at most $k$, we output $m$ distance estimates. Moreover, - For Hamming distance, our data structure answers any query in $\widetilde O(mk+n)$ time and each estimate deviates from the true distance by at most $\widetilde O(k/e^{\epsilon/\log k})$; - For edit distance, our data structure answers any query in $\widetilde O(mk^2+n)$ time and each estimate deviates from the true distance by at most $\widetilde O(k/e^{\epsilon/(\log k \log n)})$. For moderate $k$, both data structures support sublinear query operations. We obtain these results via a novel adaptation of the randomized response technique as a bit flipping procedure, applied to the sketched strings.

We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc. Unlike previous methods which struggle with limited decomposability, unsatisfactory quality, and long optimization times, StdGEN features decomposability, effectiveness and efficiency; i.e., it generates intricately detailed 3D characters with separated semantic components such as the body, clothes, and hair, in three minutes. At the core of StdGEN is our proposed Semantic-aware Large Reconstruction Model (S-LRM), a transformer-based generalizable model that jointly reconstructs geometry, color and semantics from multi-view images in a feed-forward manner. A differentiable multi-layer semantic surface extraction scheme is introduced to acquire meshes from hybrid implicit fields reconstructed by our S-LRM. Additionally, a specialized efficient multi-view diffusion model and an iterative multi-layer surface refinement module are integrated into the pipeline to facilitate high-quality, decomposable 3D character generation. Extensive experiments demonstrate our state-of-the-art performance in 3D anime character generation, surpassing existing baselines by a significant margin in geometry, texture and decomposability. StdGEN offers ready-to-use semantic-decomposed 3D characters and enables flexible customization for a wide range of applications. Project page: https://stdgen.github.io, Comment: 13 pages, 10 figures

Spin-orbit torque (SOT) can be used to efficiently manipulate the magnetic state of magnetic materials, which is an essential element for memory and logic applications. Due to symmetry constraints, only in-plane spins can be injected into the ferromagnet from the underlying SOT layer for conventional SOT materials such as heavy metals and topological materials. Through the use of materials with low symmetries, or other symmetry breaking approaches, unconventional spin currents with out-of-plane polarization has been demonstrated and enabled field-free deterministic switching of perpendicular magnetization. Despite this progress, the SOT efficiency of these materials has typically remained low. Here, we report a giant SOT efficiency of 0.85 in sputtered Ni4W/CoFeB heterostructure at room temperature, as evaluated by second harmonic Hall measurements. In addition, due to the low crystal symmetry of Ni4W, unconventional out-of-plane and Dresselhaus-like spin components were observed. Macro-spin simulation suggests our spin Hall tensor to provide about an order of magnitude improvement in the magnetization switching efficiency, thus broadening the path towards energy efficient spintronic devices using low-symmetry materials.

In addition to describing our universe, gravitational theories profoundly inspire the study of emergent properties of exotic phases of matter. While the Anti-de Sitter/conformal field theory (AdS/CFT) correspondence is one of the most celebrated examples, the field of fractonic matter -- driven in part by gapless phases resembling linearized gravity -- has also seen rapid developments. Despite the deep implications of both areas, connections between them remain sparse, primarily due to the difficulty in constructing explicit models that encapsulate both fields' essential features. Here we demonstrate the efficacy of the recently proposed Hyperbolic Fracton Model as a concrete model for AdS/CFT duality. Using explicit numerical and analytical calculations on the discrete hyperbolic lattice, we show that the boundary state exhibits conformal field theory properties. Our main result is that bulk defects induce an emergent temperature for the boundary state, proportional to the defect perimeter, in quantitative agreement with the expected behaviour of a black hole in AdS spacetime. The Hyperbolic Fracton Model thus emerges as a unique lattice model of holographic principle equipped with a well-defined bulk Hamiltonian, and offers a promising gateway for studying a wide range of holographic phenomena., Comment: 6 pages, 7 figures

Experimental determination of the structure of the superconducting order parameter in the kagome lattice compound CsV$_3$Sb$_5$ is an essential step towards understanding the nature of the superconducting pairing in this material. Here we report measurements of the temperature dependence of the in-plane magnetic penetration depth, $\lambda(T)$, in crystals of CsV$_3$Sb$_5$ down to $\sim 60\,\mathrm{mK}$. We find that $\lambda(T)$ is consistent with a fully-gapped state but with significant gap anisotropy. The magnitude of the gap minima are in the range $\sim 0.2 - 0.3 T_\mathrm{c}$ for the measured samples, markedly smaller than previous estimates. We discuss different forms of potential anisotropy and how these can be linked to the V and Sb Fermi surface sheets. We highlight a significant discrepancy between the calculated and measured values of $\lambda(T=0)$ which we suggest is caused by spatially suppressed superconductivity.

Game solving is the process of finding the theoretical outcome for a game, assuming that all player choices are optimal. This paper focuses on a technique that can reduce the heuristic search space significantly for 7x7 Killall-Go. In Go and Killall-Go, live patterns are stones that are protected from opponent capture. Mutual life, also referred to as seki, is when both players' stones achieve life by sharing liberties with their opponent. Whichever player attempts to capture the opponent first will leave their own stones vulnerable. Therefore, it is critical to recognize seki patterns to avoid putting oneself in jeopardy. Recognizing seki can reduce the search depth significantly. In this paper, we enumerate all seki patterns up to a predetermined area size, then store these patterns into a seki table. This allows us to recognize seki during search, which significantly improves solving efficiency for the game of Killall-Go. Experiments show that a day-long, unsolvable position can be solved in 482 seconds with the addition of a seki table. For general positions, a 10% to 20% improvement in wall clock time and node count is observed., Comment: Accepted by the Computers and Games conference (CG 2024)

Existing research on non-line-of-sight (NLoS) ultraviolet (UV) channel modeling mainly focuses on scenarios where the signal propagation process is not affected by any obstacle and the radiation intensity (RI) of the light source is uniformly distributed. To eliminate these restrictions, we propose a single-collision model for the NLoS UV channel incorporating a cuboid-shaped obstacle, where the RI of the UV light source is modeled as the Lambertian distribution. For easy interpretation, we categorize the intersection circumstances between the receiver field-of-view and the obstacle into six cases and provide derivations of the weighting factor for each case. To investigate the accuracy of the proposed model, we compare it with the associated Monte Carlo photon tracing model via simulations and experiments. Results verify the correctness of the proposed model. This work reveals that obstacle avoidance is not always beneficial for NLoS UV communications and provides guidelines for relevant system design., Comment: Submitted to IEEE International Conference on Communications (ICC) 2025

Efficient wideband spectrum sensing (WSS) is essential for managing spectrum scarcity in wireless communications. However, existing compressed sensing (CS)-based WSS methods require high sampling rates and power consumption, particularly with high-precision analog-to-digital converters (ADCs). Although 1-bit CS with low-precision ADCs can mitigate these demands, most approaches still depend on multi-user cooperation and prior sparsity information, which are often unavailable in WSS scenarios. This paper introduces a non-cooperative WSS method using multicoset sampling with 1-bit ADCs to achieve sub-Nyquist sampling without requiring sparsity knowledge. We analyze the impact of 1-bit quantization on multiband signals, then apply eigenvalue decomposition to isolate the signal subspace from noise, enabling spectrum support estimation without signal reconstruction. This approach provides a power-efficient solution for WSS that eliminates the need for cooperation and prior information.

Unauthorized sensing activities pose an increasing threat to individual privacy, particularly in the industrial, scientific, and medical (ISM) band where regulatory frameworks remain limited. This paper presents a novel signal process methodology to monitor and counter unauthorized sensing activities. Specifically, we model the pedestrian trajectories as a random process. Then, we leverage the Cram\'er-Rao bound (CRB) to evaluate sensing performance and model it as sampling error of such a random process. Through simulation, we verify the accuracy of monitoring unauthorized sensing activities in urban scenarios, and validate the effectiveness of corresponding mitigation strategies., Comment: Submitted to ICC 2025

Narrow-gap rare-earth Zintl phases frequently exhibit fascinating physical phenomena due to their various crystal structures, complex magnetic properties, and tunable transport behaviors. Here we report the synthesis, magnetic, thermodynamic, and transport properties of a Eu-containing Zintl arsenide, Eu$_{11}$Zn$_6$As$_{12}$, which consists of infinite chains of Eu cations and anionic frameworks constructed from corner-sharing ZnAs$_4$ tetrahedra. Eu$_{11}$Zn$_6$As$_{12}$ exhibits complicated magnetic behavior owing to intricate exchange interactions mediated by the discrete anionic fragments. Two long-range magnetic transitions at 22 K ($T_\mathrm{N}$) and 9 K ($T^*$), as well as exceptionally strong ferromagnetic fluctuations around 29 K ($T_\mathrm{F}$), are indicated by the susceptibility, heat capacity and resistivity measurements. Besides, Eu$_{11}$Zn$_6$As$_{12}$ displays metallic behavior, attributable to the hole carriers doped by slight Eu vacancies or the mixed valence of Eu$^{2+}$ and Eu$^{3+}$. A prominent resistivity peak occurs around $T_\mathrm{N}$, which is rapidly suppressed by the applied field, leading to a prominent negative magnetoresistance effect. A resistivity hysteresis is observed below 5 K, caused by a small net ferromagnetic component. Our study presents the distinct magnetic and transport properties of Eu$_{11}$Zn$_6$As$_{12}$, and further experiments are required to elucidate the origin of these novel behaviors. Moreover, our findings demonstrate that Eu-based Zintl phases are a fertile ground to study the interplay between magnetism and charge transport.

We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planetary origin of the anomaly and revealed two possible solutions, due to an inner--outer degeneracy. The two solutions provide different measured planet parameters: $(s, q)_{\rm inner} = [1.0883 \pm 0.0027, (3.125 \pm 0.248)\times 10^{-4}]$ for the inner solutions and $(s, q)_{\rm outer} = [1.0327 \pm 0.0054, (3.350 \pm 0.316)\times 10^{-4}]$ for the outer solutions. Using Bayesian analysis with constraints provided by the short event timescale ($t_{\rm E} \sim 9.1$~day) and the small angular Einstein radius ($\theta_{\rm E}\sim 0.16$~mas for the inner solution and $\sim 0.10$~mas for the outer solutio), we determined that the lens is a planetary system consisting of a host near the boundary between a star and a brown dwarf and a planet with a mass lower than that of Uranus. The discovery of the planetary system highlights the crucial role of the microlensing technique in detecting planets that orbit substellar brown dwarfs or very low-mass stars., Comment: 8 pages, 10 figures

We present a novel OCR-free document understanding framework based on pretrained Multimodal Large Language Models (MLLMs). Our approach employs multi-scale visual features to effectively handle various font sizes within document images. To address the increasing costs of considering the multi-scale visual inputs for MLLMs, we propose the Hierarchical Visual Feature Aggregation (HVFA) module, designed to reduce the number of input tokens to LLMs. Leveraging a feature pyramid with cross-attentive pooling, our approach effectively manages the trade-off between information loss and efficiency without being affected by varying document image sizes. Furthermore, we introduce a novel instruction tuning task, which facilitates the model's text-reading capability by learning to predict the relative positions of input text, eventually minimizing the risk of truncated text caused by the limited capacity of LLMs. Comprehensive experiments validate the effectiveness of our approach, demonstrating superior performance in various document understanding tasks., Comment: NeurIPS 2024

Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation can be realized using oblivious transfer as a primitive function. In this paper, we present an experimental implementation of a quantum-secure quantum oblivious transfer (QOT) protocol using an adapted quantum key distribution system combined with a bit commitment scheme, surpassing previous approaches only secure in the noisy storage model. We demonstrate the first practical application of the QOT protocol by solving the private set intersection, a prime example of secure multiparty computation, where two parties aim to find common elements in their datasets without revealing any other information. In our experiments, two banks can identify common suspicious accounts without disclosing any other data. This not only proves the experimental functionality of QOT, but also showcases its real-world commercial applications.

We proposed a haptic dial based on magnetorheological fluid (MRF) which enhances performance by increasing the MRF-exposed area through concave shaft and housing structure. We developed a breakout-style game to show that the proposed haptic dial allows users to efficiently interact with virtual objects., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

We present our approach to using AI-generated content (AIGC) and multiple media to develop an immersive, game-based, interactive story experience. The narrative of the story, "Memory Remedy", unfolds through flashbacks, allowing the audience to gradually uncover the story and the complex relationship between the robot protagonist and the older adults. This exploration explores important themes such as the journey of life, the profound influence of memories, and the concept of post-human emotional care. By engaging with this AIGC-based interactive story, audiences are encouraged to reflect on the potential role of robotic companionship in the lives of older adults in the future; and to encourage deeper reflection on the complex relationship between artificial intelligence and humanity., Comment: The 17th International Symposium on Visual Information Communication and Interaction (VINCI 2024), December 11--13, 2024, Hsinchu, Taiwan

Diffusion models have been proven highly effective at generating high-quality images. However, as these models grow larger, they require significantly more memory and suffer from higher latency, posing substantial challenges for deployment. In this work, we aim to accelerate diffusion models by quantizing their weights and activations to 4 bits. At such an aggressive level, both weights and activations are highly sensitive, where conventional post-training quantization methods for large language models like smoothing become insufficient. To overcome this limitation, we propose SVDQuant, a new 4-bit quantization paradigm. Different from smoothing which redistributes outliers between weights and activations, our approach absorbs these outliers using a low-rank branch. We first consolidate the outliers by shifting them from activations to weights, then employ a high-precision low-rank branch to take in the weight outliers with Singular Value Decomposition (SVD). This process eases the quantization on both sides. However, na\"{\i}vely running the low-rank branch independently incurs significant overhead due to extra data movement of activations, negating the quantization speedup. To address this, we co-design an inference engine Nunchaku that fuses the kernels of the low-rank branch into those of the low-bit branch to cut off redundant memory access. It can also seamlessly support off-the-shelf low-rank adapters (LoRAs) without the need for re-quantization. Extensive experiments on SDXL, PixArt-$\Sigma$, and FLUX.1 validate the effectiveness of SVDQuant in preserving image quality. We reduce the memory usage for the 12B FLUX.1 models by 3.5$\times$, achieving 3.0$\times$ speedup over the 4-bit weight-only quantized baseline on the 16GB laptop 4090 GPU, paving the way for more interactive applications on PCs. Our quantization library and inference engine are open-sourced., Comment: Quantization Library: https://github.com/mit-han-lab/deepcompressor Inference Engine: https://github.com/mit-han-lab/nunchaku Website: https://hanlab.mit.edu/projects/svdquant Demo: https://svdquant.mit.edu Blog: https://hanlab.mit.edu/blog/svdquant

As the context limits of Large Language Models (LLMs) increase, the range of possible applications and downstream functions broadens. In many real-world tasks, decisions depend on details scattered across collections of often disparate documents containing mostly irrelevant information. Long-context LLMs appear well-suited to this form of complex information retrieval and reasoning, which has traditionally proven costly and time-consuming. However, although the development of longer context models has seen rapid gains in recent years, our understanding of how effectively LLMs use their context has not kept pace. To address this, we conduct a set of retrieval experiments designed to evaluate the capabilities of 17 leading LLMs, such as their ability to follow threads of information through the context window. Strikingly, we find that many models are remarkably threadsafe: capable of simultaneously following multiple threads without significant loss in performance. Still, for many models, we find the effective context limit is significantly shorter than the supported context length, with accuracy decreasing as the context window grows. Our study also highlights the important point that token counts from different tokenizers should not be directly compared -- they often correspond to substantially different numbers of written characters. We release our code and long-context experimental data.

The advent of AI-Generated Content (AIGC) has spurred research into automated video generation to streamline conventional processes. However, automating storytelling video production, particularly for customized narratives, remains challenging due to the complexity of maintaining subject consistency across shots. While existing approaches like Mora and AesopAgent integrate multiple agents for Story-to-Video (S2V) generation, they fall short in preserving protagonist consistency and supporting Customized Storytelling Video Generation (CSVG). To address these limitations, we propose StoryAgent, a multi-agent framework designed for CSVG. StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process. Notably, our framework includes agents for story design, storyboard generation, video creation, agent coordination, and result evaluation. Leveraging the strengths of different models, StoryAgent enhances control over the generation process, significantly improving character consistency. Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency, while a novel storyboard generation pipeline is proposed to maintain subject consistency across shots. Extensive experiments demonstrate the effectiveness of our approach in synthesizing highly consistent storytelling videos, outperforming state-of-the-art methods. Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.

The efficiency of locally generating unitary designs, which capture statistical notions of quantum pseudorandomness, lies at the heart of wide-ranging areas in physics and quantum information technologies. While there are extensive potent methods and results for this problem, the evidently important setting where continuous symmetries or conservation laws (most notably U(1) and SU(d)) are involved is known to present fundamental difficulties. In particular, even the basic question of whether any local symmetric circuit can generate 2-designs efficiently (in time that grows at most polynomially in the system size) remains open with no circuit constructions provably known to do so, despite intensive efforts. In this work, we resolve this long-standing open problem for both U(1) and SU(d) symmetries by explicitly constructing local symmetric quantum circuits which we prove to converge to symmetric unitary 2-designs in polynomial time using a combination of representation theory, graph theory, and Markov chain methods. As a direct application, our constructions can be used to efficiently generate near-optimal random covariant quantum error-correcting codes, confirming a conjecture in [PRX Quantum 3, 020314 (2022)]., Comment: 8 + 48 pages

Voltage support capability is critical for grid-forming (GFM) inverters with current-limiting control (CLC) during grid faults. Despite the findings on the voltage support for symmetrical grid faults, its applicability to more common but complex asymmetrical grid faults has yet to be verified rigorously. This letter fills the gap in the voltage support capability analysis for asymmetrical grid faults by establishing and analyzing positive- and negative-sequence equivalent circuit models, where the virtual impedance is adopted to emulate various CLCs. It is discovered that matching the phase angle of the virtual impedance, emulated by the CLC, with that of the composed impedance from the capacitor to the fault location can maximize the voltage support capability of GFM inverters under asymmetrical grid faults. Rigorous theoretical analysis and experimental results verify this conclusion.

Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limitations is notoriously difficult. We present an approach to safely expand an initial set while always guaranteeing that the set is control invariant. Specifically, we define an expansion law for the boundary of a set and check for control invariance using Linear Programs (LPs). To verify control invariance on a continuous domain, we leverage recently proposed Lipschitz constants of LPs to transform the problem of continuous verification into a finite number of LPs. Using concepts from differentiable optimization, we derive the safe expansion law of the control invariant set and show how it can be interpreted as a second invariance problem in the space of possible boundaries. Finally, we show how the obtained set can be used to obtain a minimally invasive safety filter in a Control Barrier Function (CBF) framework. Our work is supported by theoretical results as well as numerical examples.

In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.

On chip acousto-optic (AO) modulation represents a significant advancement in the development of highly integrated information processing systems. However, conventional photonic devices face substantial challenges in achieving efficient conversion due to the limited overlap between acoustic waves and optical waves. In this study, we address this limitation by demonstrating an enhanced conversion effect of photonic crystal nanobeam cavities (PCNBCs) in AO modulation on a polymer-loaded lithium niobate integrated platform. Attributed to the high ratio of quality factor (Q) to mode volume (V) and optimal light-sound overlap within the nanocavity, PCNBCs-based AO modulator exhibits a significantly enhanced extinction ratio of 38 dB with a threshold RF power below -50 dBm, which is two orders of magnitude lower than that based on micro-ring resonator (MRRs). In addition, robust digital amplitude shift keying modulations using selected RF and optical channels of the PCNBCs-enhanced AO modulators. These findings validate the compelling properties of the PCNBCs photonic platform, establishing it as a promising candidate for on-chip integrated microwave photonics, optical transceivers, and computing applications., Comment: 25 pages, 6 figures

Neutral atom array has emerged as a promising platform for quantum computation owing to its high-fidelity two-qubit gate, arbitrary connectivity and overwhelming scalability. Nevertheless, fault-tolerant quantum computing on the neutral atom platform requires consideration of the types of errors that neutral atoms are prone to. One typical and major error is leakage error from Rydberg state when implementing multi-qubit gate. Such leakage error is harmful by propagating multiple pauli errors in quantum circuit. Researchers have proposed erasure conversion protocol, which utilizes fast leakage detection to convert leakage error to benign erasure error. This method has a favorable error distance d, but is limited to certain atom species. Here, we propose a new method to deal with such leakage error in measurement-based quantum computation (MBQC), to which we refer as "Leakage Tracking". We remove the demand for mid-circuit leakage detection but infer the probabilities and locations of pauli errors through gate sequence and final leakage detection. We show that this method has an error distance de = d and reaches a high threshold 1.7% per CZ gate for pure leakage error and perfect final leakage detection. In presence of atom loss and other pauli errors, we show the advantage in error distance over erasure conversion when the ratio of leakage error is close to one., Comment: 11 pages, 5 figures

Attaining superconducting critical temperatures (Tc) beyond the limit around 14 K observed thus far in spinel compounds AB2X4 (A, B = transition metals, X = O/chalcogen) could elucidate interaction intricacies and inform materials design. This work spotlights CuIr2S4, which exhibits a distinct metal-insulator transition below 230 K, as an unconventional candidate for activation under high pressure. Through transport, diffraction, and spectroscopy experiments conducted at pressures up to 224 GPa, we unveil pressure-tuning that suppressed CuIr2S4's transition, yielding two superconducting phases with an un-precedented Tc for spinels. Initially, 3.8 K onset rose monotonically, reaching 18.2 K at 133 GPa. Unexpectedly, a distinct phase with Tc = 2.2 K distinctly emerged at higher pressures, intimating unconventional couplings. Our findings suggest that both geometric frustration and electron-electron interactions play crucial roles in the superconductivity observed in CuIr2S4. The findings stretch perceived temperature limits in spinels and provide structure-property insights to guide the optimiza-tion of quantum materials interactions for tailored targeted functionalities., Comment: 12 pages, 7 gifures

Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP$^2$DP$^2$) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics.

We report transport studies on the layered van der Waals topological crystalline insulator Ta$_2$Pd$_3$Te$_5$. The temperature-dependent resistance at high temperature is dominated by a bulk insulating gap and tend to saturate at low temperatures. Low temperature magnetotransport shows that Ta$_2$Pd$_3$Te$_5$ exhibits weak antilocatization (WAL) effect in both perpendicular orientation and parallel orientation, suggesting an contribution of the WAL effect from both topological edge states and bulk states. By measuring the anisotropic magnetoconductance and then subtracting the contribution of bulk states, the WAL effect associated with topological edge states can be revealed and analyzed quantitatively based on the two-dimensional Hikami-Larkin-Nagaoka model. Our results have important implications in understanding the WAL phenomena in Ta$_2$Pd$_3$Te$_5$., Comment: 8 pages, 5 figures

We propose MegaPortrait. It's an innovative system for creating personalized portrait images in computer vision. It has three modules: Identity Net, Shading Net, and Harmonization Net. Identity Net generates learned identity using a customized model fine-tuned with source images. Shading Net re-renders portraits using extracted representations. Harmonization Net fuses pasted faces and the reference image's body for coherent results. Our approach with off-the-shelf Controlnets is better than state-of-the-art AI portrait products in identity preservation and image fidelity. MegaPortrait has a simple but effective design and we compare it with other methods and products to show its superiority., Comment: Technical Report

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we first overview benefits and concerns when applying FL to wireless networks. Next, we provide a new perspective on existing personalized FL frameworks by analyzing the relationship between cooperation and personalization in these frameworks. Additionally, we discuss the possibility of tuning the cooperation level with a choice-based approach. Our choice-based FL approach is a flexible and safe FL framework that allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation. In this way, the choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks.

In-Context Learning (ICL) is a phenomenon where task learning occurs through a prompt sequence without the necessity of parameter updates. ICL in Multi-Headed Attention (MHA) with absolute positional embedding has been the focus of more study than other sequence model varieties. We examine implications of architectural differences between GPT-2 and LLaMa as well as LlaMa and Mamba. We extend work done by Garg et al. (2022) and Park et al. (2024) to GPT-2/LLaMa hybrid and LLaMa/Mamba hybrid models - examining the interplay between sequence transformation blocks and regressive performance in-context. We note that certain architectural changes cause degraded training efficiency/ICL accuracy by converging to suboptimal predictors or converging slower. We also find certain hybrids showing optimistic performance improvements, informing potential future ICL-focused architecture modifications. Additionally, we propose the "ICL regression score", a scalar metric describing a model's whole performance on a specific task. Compute limitations impose restrictions on our architecture-space, training duration, number of training runs, function class complexity, and benchmark complexity. To foster reproducible and extensible research, we provide a typed, modular, and extensible Python package on which we run all experiments., Comment: 18 pages, 16 figures

In this paper, we summarize the historical development of finite-time thermodynamics and review the current state of research over the past two decades in this field, focusing on fundamental constraints of finite-time thermodynamic cycles, optimal control and optimization of thermodynamic processes, the operation of unconventional heat engines, and experimental progress., Comment: 4 pages, 131 references, comments are welcome

Due to the diverse functionalities of different thermal machines, their optimization relies on a case-by-case basis, lacking unified results. In this work, we propose a general approach to determine power-efficiency trade-off relation (PETOR) for any thermal machine. For cases where cycle (of duration $\tau$) irreversibility satisfies the typical $1/\tau$-scaling, we provide a unified PETOR which is applicable to heat engines, refrigerators, heat exchangers and heat pumps. It is shown that, some typical PETORs, such as those for low-dissipation Carnot cycles (including heat engine and refrigerator cycles) and the steady-state heat engines operating between finite-sized reservoirs are naturally recovered., Comment: 4+2 pages, 2 figures, comments are welcome

Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To address these challenges, this study proposes an automated and computationally efficient GD&T extraction method by fine-tuning Florence-2, an open-source vision-language model (VLM). The model is trained on a dataset of 400 drawings with ground truth annotations provided by domain experts. For comparison, two state-of-the-art closed-source VLMs, GPT-4o and Claude-3.5-Sonnet, are evaluated on the same dataset. All models are assessed using precision, recall, F1-score, and hallucination metrics. Due to the computational cost and impracticality of fine-tuning large closed-source VLMs for domain-specific tasks, GPT-4o and Claude-3.5-Sonnet are evaluated in a zero-shot setting. In contrast, Florence-2, a smaller model with 0.23 billion parameters, is optimized through full-parameter fine-tuning across three distinct experiments, each utilizing datasets augmented to different levels. The results show that Florence-2 achieves a 29.95% increase in precision, a 37.75% increase in recall, a 52.40% improvement in F1-score, and a 43.15% reduction in hallucination rate compared to the best-performing closed-source model. These findings highlight the effectiveness of fine-tuning smaller, open-source VLMs like Florence-2, offering a practical and efficient solution for automated GD&T extraction to support downstream manufacturing tasks., Comment: Paper has been submitted to the 9th International Conference on Innovation in Artificial Intelligence (ICIAI 2025)

We propose a novel, highly efficient, second-order accurate, long-time unconditionally stable numerical scheme for a class of finite-dimensional nonlinear models that are of importance in geophysical fluid dynamics. The scheme is highly efficient in the sense that only a (fixed) symmetric positive definite linear problem (with varying right hand sides) is involved at each time-step. The solutions to the scheme are uniformly bounded for all time. We show that the scheme is able to capture the long-time dynamics of the underlying geophysical model, with the global attractors as well as the invariant measures of the scheme converge to those of the original model as the step size approaches zero. In our numerical experiments, we take an indirect approach, using long-term statistics to approximate the invariant measures. Our results suggest that the convergence rate of the long-term statistics, as a function of terminal time, is approximately first order using the Jensen-Shannon metric and half-order using the L1 metric. This implies that very long time simulation is needed in order to capture a few significant digits of long time statistics (climate) correct. Nevertheless, the second order scheme's performance remains superior to that of the first order one, requiring significantly less time to reach a small neighborhood of statistical equilibrium for a given step size.

As a promising technology, physical layer security (PLS) enhances security by leveraging the physical characteristics of communication channels. However, the conventional PLS approach leads to a considerable disparity in the effort legitimate users need to secure data compared to eavesdroppers. To address this issue, we propose a physical layer deception (PLD) framework, which applies random deceptive ciphering and orthogonal frequency-division multiplexing (OFDM) to defend against eavesdropping proactively. While ensuring the same level of confidentiality as traditional PLS methods, the PLD approach additionally introduces a deception mechanism, even when the eavesdropper possesses the same knowledge about the transmitter end as the legitimate receiver. Through thorough theoretical analyses and numerical simulations, we prove the superiority of our method over the conventional PLS approach., Comment: Submitted to ICC 2025 (appendices are excluded from the submitted version due to length limit)

The pyrochlore lattice magnet has been one of the most fruitful platforms for the experimental and theoretical search for spin liquids. Besides the canonical case of spin ice, works in recent years have identified a variety of new quantum and classical spin liquids from the generic nearest-neighbor anisotropic spin Hamiltonian on the pyrochlore lattice. However, a general framework for the thorough classification and characterization of these exotic states of matter has been lacking, and so is an exhaustive list of all possible spin liquids that this model can support and what is the corresponding structure of their emergent field theory. In this work, we develop such a theoretical framework to allocate interaction parameters stabilizing different classical spin liquids and derive their corresponding effective generalized emerging Gauss's laws at low temperatures. Combining this with Monte Carlo simulations, we systematically identify all classical spin liquids for the general nearest-neighbor anisotropic spin Hamiltonian on the pyrochlore lattice. We uncover new spin liquid models with exotic forms of generalized Gauss's law and multipole conservation laws. Furthermore, we present an atlas of all spin liquid regimes in the phase diagram, which illuminates the global picture of how different classical spin liquids are connected in parameter space and transition into each other. Our work serves as a treasure map for the theoretical study of classical and quantum spin liquids, as well as for the experimental search and rationalization of exotic pyrochlore lattice magnets., Comment: 25 pages, 10 figures, 5 tables