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Handling test-time unknown degradation is the major challenge in Blind Image Restoration (BIR), necessitating high model generalization. An effective strategy is to incorporate prior knowledge, either from human input or generative model. In this paper, we introduce Instant-reference Image Restoration (InstantIR), a novel diffusion-based BIR method which dynamically adjusts generation condition during inference. We first extract a compact representation of the input via a pre-trained vision encoder. At each generation step, this representation is used to decode current diffusion latent and instantiate it in the generative prior. The degraded image is then encoded with this reference, providing robust generation condition. We observe the variance of generative references fluctuate with degradation intensity, which we further leverage as an indicator for developing a sampling algorithm adaptive to input quality. Extensive experiments demonstrate InstantIR achieves state-of-the-art performance and offering outstanding visual quality. Through modulating generative references with textual description, InstantIR can restore extreme degradation and additionally feature creative restoration.

Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection. To address these challenges, we present a Diffusion-based Graph Anomaly Detector (DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance its proficiency by guiding it with discriminative content. This innovative approach leverages diffusion sampling to infuse the latent space with discriminative content and introduces a content-preservation mechanism that retains valuable information across different scales, significantly improving its adeptness at identifying anomalies with limited time and space complexity. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets with various metrics, demonstrated its exceptional performance.

In knowledge graph embedding, aside from positive triplets (ie: facts in the knowledge graph), the negative triplets used for training also have a direct influence on the model performance. In reality, since knowledge graphs are sparse and incomplete, negative triplets often lack explicit labels, and thus they are often obtained from various sampling strategies (eg: randomly replacing an entity in a positive triplet). An ideal sampled negative triplet should be informative enough to help the model train better. However, existing methods often ignore diversity and adaptiveness in their sampling process, which harms the informativeness of negative triplets. As such, we propose a generative adversarial approach called Diversified and Adaptive Negative Sampling DANS on knowledge graphs. DANS is equipped with a two-way generator that generates more diverse negative triplets through two pathways, and an adaptive mechanism that produces more fine-grained examples by localizing the global generator for different entities and relations. On the one hand, the two-way generator increase the overall informativeness with more diverse negative examples; on the other hand, the adaptive mechanism increases the individual sample-wise informativeness with more fine-grained sampling. Finally, we evaluate the performance of DANS on three benchmark knowledge graphs to demonstrate its effectiveness through quantitative and qualitative experiments., Comment: 30 pages, 7 figures, Journal

The escalating demands of compute-intensive applications, including artificial intelligence, urgently necessitate the adoption of sophisticated optical on-chip interconnect technologies to overcome critical bottlenecks in scaling future computing systems. This transition requires leveraging the inherent parallelism of wavelength and mode dimensions of light, complemented by high-order modulation formats, to significantly enhance data throughput. Here we experimentally demonstrate a novel synergy of these three dimensions, achieving multi-tens-of-terabits-per-second on-chip interconnects using ultra-broadband, multi-mode digital metamaterials. Employing a highly efficient edge-guided analog-and-digital optimization method, we inversely design foundry-compatible, robust, and multi-port digital metamaterials with an 8xhigher computational efficiency. Using a packaged five-mode multiplexing chip, we demonstrate a single-wavelength interconnect capacity of 1.62 Tbit s-1 and a record-setting multi-dimensional interconnect capacity of 38.2 Tbit s-1 across 5 modes and 88 wavelength channels. A theoretical analysis suggests that further system optimization can enable on-chip interconnects to reach sub-petabit-per-second data transmission rates. This study highlights the transformative potential of optical interconnect technologies to surmount the constraints of electronic links, thus setting the stage for next-generation datacenter and optical compute interconnects.

The problem of a rigid disk rolling down a ramp is a classic problem given to students in introductory mechanics courses. In contrast, systematic studies on the rolling behavior of an ellipse have only recently emerged. Unlike a rolling disk, here the geometric center remains at a constant height from the floor, the center of a rotating ellipse changes nonlinearly due to its eccentric shape. This eccentricity introduces new modes of motion beyond rolling, including rocking and hopping. Leveraging this multi-motion behavior, we design an ellipsoidal actuator which exhibits both rolling and hopping behaviors in response to changes in the applied angular velocity. Using a simple geometric framework, we successfully capture the motion of the actuator as a force-driven rigid ellipsoid on a non-slip flat surface, and identify the critical angular velocity for the rolling-to-hopping transition. Furthermore, by adding deformability to the actuator, we unlock new functionalities, enabling soft actuators that can climb slopes and work together to collectively ascend stairs.

In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous MoE framework that integrates both Feed-Forward Network~(FFN) and zero-computation experts. Specifically, we introduce three types of zero-computation experts: the zero expert, copy expert, and constant expert, which correspond to discard, skip, and replace operations, respectively. This design offers three key advantages: (i) Low Computing Overhead: Unlike the uniform mixing mechanism for all tokens within vanilla MoE, MoE++ allows each token to engage with a dynamic number of FFNs, be adjusted by constant vectors, or even skip the MoE layer entirely. (ii) High Performance: By enabling simple tokens to utilize fewer FFN experts, MoE++ allows more experts to focus on challenging tokens, thereby unlocking greater performance potential than vanilla MoE. (iii) Deployment Friendly: Given that zero-computation experts have negligible parameters, we can deploy all zero-computation experts on each GPU, eliminating the significant communication overhead and expert load imbalance associated with FFN experts distributed across different GPUs. Moreover, we leverage gating residuals, enabling each token to consider the pathway taken in the previous layer when selecting the appropriate experts. Extensive experimental results demonstrate that MoE++ achieves better performance while delivering 1.1-2.1x expert forward throughput compared to a vanilla MoE model of the same size, which lays a solid foundation for developing advanced and efficient MoE-related models., Comment: 23 pages, Code: https://github.com/SkyworkAI/MoE-plus-plus

Metasurfaces have revolutionized nonlinear and quantum light manipulation in the past decade, enabling the design of materials that can tune polarization, frequency, and direction of light simultaneously. However, tuning of metasurfaces is traditionally achieved by changing their microscopic structure, which does not allow \emph{in situ} tuning and dynamic optimization of the metasurfaces. In this Letter, we explore the concept of twisted bilayer and tetralayer nonlinear metasurfaces, which offer rich tunability in its effective nonlinear susceptibilities. Using gold-based metasurfaces, we demonstrate that a number of different singularities of nonlinear susceptibilities can exist in the parameter space of twist angle and interlayer gap between different twisted layers. At the singularities, reflected/transmitted light from the nonlinear process (such as second-harmonic generation) can either become circularly polarized (for C points), or entirely vanish (for V points). By further breaking symmetries of the system, we can independently tune all aspects of the reflected and transmitted nonlinear emission, achieving unidirectional emission with full-Poincar\'e polarization tunability, a dark state (V-V point), or any other bidirectional output. Our work paves the way for multidimensional control of polarization and directionality in nonlinear light sources, opening new avenues in ultrafast optics, optical communication, sensing, and quantum optics.

Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible application programming interfaces (APIs), limiting their benefits for downstream tasks. To explore the feasibility of training a text-to-image generation model comparable to advanced models using publicly available resources, we introduce EvolveDirector. This framework interacts with advanced models through their public APIs to obtain text-image data pairs to train a base model. Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability. However, it requires large-scale samples of 10 million or more. This incurs significant expenses in time, computational resources, and especially the costs associated with calling fee-based APIs. To address this problem, we leverage pre-trained large vision-language models (VLMs) to guide the evolution of the base model. VLM continuously evaluates the base model during training and dynamically updates and refines the training dataset by the discrimination, expansion, deletion, and mutation operations. Experimental results show that this paradigm significantly reduces the required data volume. Furthermore, when approaching multiple advanced models, EvolveDirector can select the best samples generated by them to learn powerful and balanced abilities. The final trained model Edgen is demonstrated to outperform these advanced models. The code and model weights are available at https://github.com/showlab/EvolveDirector.

We used FAST to conduct deep HI imaging of the entire M 106 group region, and have discovered a few new HI filaments and clouds. Three HI clouds/filaments are found in a region connecting DDO 120 and NGC 4288, indicating an interaction between these two galaxies. The HI features in this region suggest that DDO 120 is probably the origin of the HI stream extending from the northern end of NGC 4288 to M 106. This structure is similar to the SMC-LMC stream, but much longer, about 190 kpc. Furthermore, based on the distance measurements, we have determined the satellite galaxy members of M 106. With an absolute magnitude cutoff of M_B=-10, we obtained a sample of 11 member satellite galaxies for M 106. Using the observed HI mass with FAST, we studied the properties of satellite galaxies in M 106 and found that satellite galaxies with lower stellar masses exhibit more significant deviations from the star-forming main sequence (SFMS) in their specific star formation rates. Furthermore, the relationship between the HI mass of satellite galaxies and optical diameter generally follows the field galaxies relation. We discuss the possible mechanisms leading to the quenching in the M 106 group based on the new data from FAST, Comment: 18 pages,11 figures and 3 tables.Accepted by mnras

In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require developers to detect, capture, and manage exceptions according to high standards, but many developers struggle with these tasks, leading to fragile code. This problem is particularly evident in open source projects and impacts the overall quality of the software ecosystem. To address this challenge, we explore the use of large language models (LLMs) to improve exception handling in code. Through extensive analysis, we identify three key issues: Insensitive Detection of Fragile Code, Inaccurate Capture of Exception Types, and Distorted Handling Solutions. These problems are widespread across real world repositories, suggesting that robust exception handling practices are often overlooked or mishandled. In response, we propose Seeker, a multi agent framework inspired by expert developer strategies for exception handling. Seeker uses agents: Scanner, Detector, Predator, Ranker, and Handler to assist LLMs in detecting, capturing, and resolving exceptions more effectively. Our work is the first systematic study on leveraging LLMs to enhance exception handling practices, providing valuable insights for future improvements in code reliability., Comment: 26 pages, 7 figures. Submitted ICLR 2025

Consider the communication-constrained estimation of discrete distributions under $\ell^p$ losses, where each distributed terminal holds multiple independent samples and uses limited number of bits to describe the samples. We obtain the minimax optimal rates of the problem in most parameter regimes. An elbow effect of the optimal rates at $p=2$ is clearly identified. To show the optimal rates, we first design estimation protocols to achieve them. The key ingredient of these protocols is to introduce adaptive refinement mechanisms, which first generate rough estimate by partial information and then establish refined estimate in subsequent steps guided by the rough estimate. The protocols leverage successive refinement, sample compression and thresholding methods to achieve the optimal rates in different parameter regimes. The optimality of the protocols is shown by deriving compatible minimax lower bounds.

Quantum secret sharing (QSS) plays a significant role in multiparty quantum communication and is a crucial component of future quantum multiparty computing networks. Therefore, it is highly valuable to develop a QSS protocol that offers both information-theoretic security and validation in real optical systems under a finite-key regime. In this work, we propose a three-user QSS protocol based on phase-encoding technology. By adopting symmetric procedures for the two players, our protocol resolves the security loopholes introduced by asymmetric basis choice without prior knowledge of the identity of the malicious player. Kato's concentration inequality is exploited to provide security against coherent attacks with the finite-key effect. Moreover, the practicality of our protocol has been validated under a 30-dB channel loss with a transmission distance of 5-km fiber. Our protocol achieves secure key rates ranging from 432 to 192 bps by choosing different pulse intensities and basis selection probabilities. Offering enhanced security and practicality, our protocol stands as an essential element for the realization of quantum multiparty computing networks., Comment: 14 pages, 5 figures, 5 tables

Multi-task ranking models have become essential for modern real-world recommendation systems. While most recommendation researches focus on designing sophisticated models for specific scenarios, achieving performance improvement for multi-task ranking models across various scenarios still remains a significant challenge. Training all tasks naively can result in inconsistent learning, highlighting the need for the development of multi-task optimization (MTO) methods to tackle this challenge. Conventional methods assume that the optimal joint gradient on shared parameters leads to optimal parameter updates. However, the actual update on model parameters may deviates significantly from gradients when using momentum based optimizers such as Adam, and we design and execute statistical experiments to support the observation. In this paper, we propose a novel Parameter Update Balancing algorithm for multi-task optimization, denoted as PUB. In contrast to traditional MTO method which are based on gradient level tasks fusion or loss level tasks fusion, PUB is the first work to optimize multiple tasks through parameter update balancing. Comprehensive experiments on benchmark multi-task ranking datasets demonstrate that PUB consistently improves several multi-task backbones and achieves state-of-the-art performance. Additionally, experiments on benchmark computer vision datasets show the great potential of PUB in various multi-task learning scenarios. Furthermore, we deployed our method for an industrial evaluation on the real-world commercial platform, HUAWEI AppGallery, where PUB significantly enhances the online multi-task ranking model, efficiently managing the primary traffic of a crucial channel., Comment: Accepted by ICDM'24

The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed to capture semantics at both the token and statement levels simultaneously, which is crucial for achieving more accurate multi-granular vulnerability detection. Additionally, CodeBERT-HLS efficiently processes longer code token sequences, making it more suited to real-world vulnerability detection. Comprehensive experiments demonstrate that our method enhances the performance of vulnerability detection at both coarse- and fine-grained levels. Specifically, in coarse-grained vulnerability detection, StagedVulBERT achieves an F1 score of 92.26%, marking a 6.58% improvement over the best-performing methods. At the fine-grained level, our method achieves a Top-5% accuracy of 65.69%, which outperforms the state-of-the-art methods by up to 75.17%., Comment: 18 pages,13 figures

Long document summarization poses a significant challenge in natural language processing due to input lengths that exceed the capacity of most state-of-the-art pre-trained language models. This study proposes a hierarchical framework that segments and condenses information from long documents, subsequently fine-tuning the processed text with an abstractive summarization model. Unsupervised topic segmentation methods identify semantically appropriate breakpoints. The condensation stage utilizes an unsupervised generation model to generate condensed data, and our current experiments employ ChatGPT(v3.5). The summarization stage fine-tunes the abstractive summarization model on the condensed data to generate the final results. This framework enables long documents to be processed on models even when the document length exceeds the model's maximum input size. The exclusion of the entire document from the summarization model reduces the time and computational resources required for training, making the framework suitable for contexts with constrained local computational resources.

The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have focused on optimizing model inference but have overlooked a critical question: is it necessary to perform user model inference for every ad request in large-scale social networks? To address this question and these challenges, we first analyze user access patterns at Meta and find that most user model inferences occur within a short timeframe. T his observation reveals a triangular relationship among model complexity, embedding freshness, and service SLAs. Building on this insight, we designed, implemented, and evaluated ERCache, an efficient and robust caching framework for large-scale user representations in ads recommendation systems on social networks. ERCache categorizes cache into direct and failover types and applies customized settings and eviction policies for each model, effectively balancing model complexity, embedding freshness, and service SLAs, even considering the staleness introduced by caching. ERCache has been deployed at Meta for over six months, supporting more than 30 ranking models while efficiently conserving computational resources and complying with service SLA requirements.

Video Large Language Models (Video LLMs) have achieved impressive performance on video-and-language tasks, such as video question answering. However, most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding. To address this gap, we propose a Time Gating Video LLM (TG-Vid) designed to enhance temporal modeling through a novel Time Gating module (TG). The TG module employs a time gating mechanism on its sub-modules, comprising gating spatial attention, gating temporal attention, and gating MLP. This architecture enables our model to achieve a robust understanding of temporal information within videos. Extensive evaluation of temporal-sensitive video benchmarks (i.e., MVBench, TempCompass, and NExT-QA) demonstrates that our TG-Vid model significantly outperforms the existing Video LLMs. Further, comprehensive ablation studies validate that the performance gains are attributed to the designs of our TG module. Our code is available at https://github.com/LaVi-Lab/TG-Vid., Comment: EMNLP 2024 Findings (Short)

Two dimensional (2D) crystals have attracted strong interest as a new class of proton conducting materials that can block atoms, molecules and ions while allowing proton transport through the atomically thin basal planes. Although 2D materials exhibit this perfect selectivity, the reported proton conductivities have been relatively low. Here we show that vacancy-rich titania monolayers are highly permeable to protons while remaining impermeable to helium with proton conductivity exceeding 100 S cm-2 at 200 C and surpassing targets set by industry roadmaps. The fast and selective proton transport is attributed to an extremely high density of titanium-atom vacancies (one per square nm), which effectively turns titania monolayers into angstrom-scale sieves. Our findings highlight the potential of 2D oxides as membrane materials for hydrogen-based technologies.

We characterize the compactness of embedding derivatives from Hardy space $H^p$ into Lebesgue space $L^q(\mu)$. We also completely characterize the boundedness and compactness of derivative area operators from $H^p$ into $L^q(\mathbb{S}_n)$, $0

Watermarking of large language models (LLMs) generation embeds an imperceptible statistical pattern within texts, making it algorithmically detectable. Watermarking is a promising method for addressing potential harm and biases from LLMs, as it enables traceability, accountability, and detection of manipulated content, helping to mitigate unintended consequences. However, for open-source models, watermarking faces two major challenges: (i) incompatibility with fine-tuned models, and (ii) vulnerability to fine-tuning attacks. In this work, we propose WAPITI, a new method that transfers watermarking from base models to fine-tuned models through parameter integration. To the best of our knowledge, we propose the first watermark for fine-tuned open-source LLMs that preserves their fine-tuned capabilities. Furthermore, our approach offers an effective defense against fine-tuning attacks. We test our method on various model architectures and watermarking strategies. Results demonstrate that our method can successfully inject watermarks and is highly compatible with fine-tuned models. Additionally, we offer an in-depth analysis of how parameter editing influences the watermark strength and overall capabilities of the resulting models.

Fine-tuning language models (LMs) with the Adam optimizer often demands excessive memory, limiting accessibility. The "in-place" version of Stochastic Gradient Descent (IP-SGD) and Memory-Efficient Zeroth-order Optimizer (MeZO) have been proposed to address this. However, IP-SGD still requires substantial memory, and MeZO suffers from slow convergence and degraded final performance due to its zeroth-order nature. This paper introduces Addax, a novel method that improves both memory efficiency and performance of IP-SGD by integrating it with MeZO. Specifically, Addax computes zeroth- or first-order gradients of data points in the minibatch based on their memory consumption, combining these gradient estimates to update directions. By computing zeroth-order gradients for data points that require more memory and first-order gradients for others, Addax overcomes the slow convergence of MeZO and the excessive memory requirement of IP-SGD. Additionally, the zeroth-order gradient acts as a regularizer for the first-order gradient, further enhancing the model's final performance. Theoretically, we establish the convergence of Addax under mild assumptions, demonstrating faster convergence and less restrictive hyper-parameter choices than MeZO. Our experiments with diverse LMs and tasks show that Addax consistently outperforms MeZO regarding accuracy and convergence speed while having a comparable memory footprint. When fine-tuning OPT-13B with one A100 GPU, on average, Addax outperforms MeZO in accuracy/F1 score by 14% and runs 15x faster while using memory similar to MeZO. In our experiments on the larger OPT-30B model, on average, Addax outperforms MeZO in terms of accuracy/F1 score by >16 and runs 30x faster on a single H100 GPU. Moreover, Addax surpasses the performance of standard fine-tuning approaches, such as IP-SGD and Adam, in most tasks with significantly less memory requirement.

This study focuses on a critical aspect of bridge engineering -- the evaluation of cable forces, paying particular attention to the cables that are internally constrained by elastic supports. Detecting these cable forces is important for the safety and stability of bridges. The practical problem introduces a novel mathematical challenge: how to effectively address string vibration equations with one or multiple internal elastic supports,~which remains a theoretical issue not fully solved in engineering. To tackle this, it is necessary to firstly establish an appropriate mathematical model and accurately define initial-boundary value problems. We then formulate the well-posedness of the solution using both classical and weak solution approaches, supplementing the existing numerical results available in engineering. Meanwhile, we attempt to use PINNs (Physics-Informed Neural Networks) instead of traditional FEM (Finite Element Method) in engineering. Consequently, in contrast to the classical solution method, we demonstrate that for a string with finite elastic supports, the weak solution method not only improves mathematical modeling efficiency but also simplifies the process of explaining the well-posedness of the solution.

Federated graph learning is an emerging field with significant practical challenges. While many algorithms have been proposed to enhance model accuracy, their system performance, crucial for real-world deployment, is often overlooked. To address this gap, we present FedGraph, a research library designed for practical distributed deployment and benchmarking in federated graph learning. FedGraph supports a range of state-of-the-art methods and includes profiling tools for system performance evaluation, focusing on communication and computation costs during training. FedGraph can then facilitate the development of practical applications and guide the design of future algorithms., Comment: https://github.com/FedGraph/fedgraph

Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (LLMs) with human preferences. Direct Preference Optimization (DPO), one of the most popular approaches, formulates RLHF as a policy optimization problem without explicitly estimating the reward function. It overcomes the stability and efficiency issues of two-step approaches, which typically involve first estimating the reward function and then optimizing the policy via proximal policy optimization (PPO). Since RLHF is essentially an optimization problem, and it is well-known that momentum techniques can accelerate optimization both theoretically and empirically, a natural question arises: Can RLHF be accelerated by momentum? This paper answers this question in the affirmative. In detail, we first show that the iterative preference optimization method can be viewed as a proximal point method. Based on this observation, we propose a general Accelerated Preference Optimization (APO) framework, which unifies many existing preference optimization algorithms and employs Nesterov's momentum technique to speed up the alignment of LLMs. Theoretically, we demonstrate that APO can achieve a faster convergence rate than the standard iterative preference optimization methods, including DPO and Self-Play Preference Optimization (SPPO). Empirically, we show the superiority of APO over DPO, iterative DPO, and other strong baselines for RLHF on the AlpacaEval 2.0 benchmark., Comment: 44 pages, 10 tables

Cavitation is a highly turbulent, multi-phase flow phenomenon that manifests in the form of vapor cavities as a result of a sudden drop in the liquid pressure. The phenomenon has been observed as widely detrimental in hydraulic and marine applications like propellers and pumps, while also accelerating the sub-processes involved for bio-diesel production. Modelling cavitating flows has been extremely challenging owing to the resulting flow unsteadiness, phase transition and the cavitation-turbulence interaction. Standard methods to model turbulence like Reynolds-Averaged Navier-Stokes equations (RANS) and hybrid RANS-Large Eddy Simulations (LES) models are unable to reproduce the local turbulence dynamics observed in cavitating flow experiments. In an effort to facilitate the development of accurate RANS modelling procedures for cavitating flows, a data-driven approach devised by integrating Gene-Expression Programming (GEP) into a traditional RANS approach, without continuous RANS model feedback is proposed. The GEP algorithm is employed to obtain an additional corrective term, composed of flow variables derived directly from a baseline URANS cavitating flow calculation to be fit directly in the Boussinesq approximation. In addition, an optimizer based on the BFGS algorithm is employed on top of the GEP algorithm to ensure the solution is independent of the uncertainties associated with GEP. To evaluate the complexity of the model, we compare the approach with a more elementary linear regression technique. The GEP-CFD method is then used to train two expressions, for the Reynolds shear stress and the Turbulent Kinetic Energy simultaneously. The GEP-CFD approach is able to produce the flow features not captured by baseline RANS simulations in several areas, outperforming the RANS simulations and thus projects as a promising approach to augment numerical simulations for cavitating flows.

The Win Ratio has gained significant traction in cardiovascular trials as a novel method for analyzing composite endpoints (Pocock and others, 2012). Compared with conventional approaches based on time to the first event, the Win Ratio accommodates the varying priorities and types of outcomes among components, potentially offering greater statistical power by fully utilizing the information contained within each outcome. However, studies using Win Ratio have largely been confined to fixed design, limiting flexibility for early decisions, such as stopping for futility or efficacy. Our study proposes a sequential design framework incorporating multiple interim analyses based on Win Ratio or Net Benefit statistics. Moreover, we provide rigorous proof of the canonical joint distribution for sequential Win Ratio and Net Benefit statistics, and an algorithm for sample size determination is developed. We also provide results from a finite sample simulation study, which show that our proposed method controls Type I error maintains power level, and has a smaller average sample size than the fixed design. A real study of cardiovascular study is applied to illustrate the proposed method., Comment: 36 pages, 1 figure, 5 tables

Polaronic effects involving stabilization of localized charge character by structural deformations and polarizations have attracted considerable investigations in soft lattice lead halide perovskites. However, the concept of polaron assisted nonlinear photonics remains largely unexplored, which has a wide range of applications from optoelectronics to telecommunications and quantum technologies. Here, we report the first observation of the polaronic state assisted saturable absorption through subbandgap excitation with a redshift exceeding 60 meV. By combining photoluminescence, transient absorption measurements and density functional theory calculations, we explicate that the anomalous nonlinear saturable absorption is caused by the transient picosecond timescale polaronic state formed by strong carrier exciton phonon coupling effect. The bandgap fluctuation can be further tuned through exciton phonon coupling of perovskites with different Young's modulus. This suggests that we can design targeted soft lattice lead halide perovskite with a specific structure to effectively manipulate exciton phonon coupling and exciton polaron formation. These findings profoundly expand our understanding of exciton polaronic nonlinear optics physics and provide an ideal platform for developing actively tunable nonlinear photonics applications.

High-speed ultra-broadband detectors play a crucial role in aerospace technology, and national security etc. The interfacial work function internal photoemission (IWIP) detector employs multiple absorption mechanism comprehensively across different wavelength band to achieve complete photon type detection, which makes it possible to realize high-speed and ultra-broadband simultaneously. We propose a ratchet heterojunction IWIP (HEIWIP) detector, which shows 3-165THz ultra-broadband coverage. The high-speed response is investigated in detail by both microwave rectification technology and high-speed modulated terahertz light. Up to 5.1GHz 3dB bandwidth is acquired in terms of microwave rectification measurement. And 4.255GHz inter-mode optical beat note signal was successfully detected.

The light curve of the TeV emission in GRB 221009A displays a smooth transition from an initial rapid rise to a slower rise and eventually a decay phase. The smooth temporal profile of the TeV emission suggests that it mainly results from an external shock. The temporal overlap between the prompt KeV-MeV emission and the early TeV afterglow indicates that external inverse Compton scattering (EIC) between the prompt KeV-MeV photons and the afterglow electrons is inevitable. Since the energy density of the prompt emission is much higher than that of the afterglow during the early phase, the EIC process dominates the cooling of afterglow electrons. The EIC scattering rate is influenced by the anisotropy of the seed photon field, which depends on the radii of the internal dissipation ($R_{\rm dis}$), where the prompt emission is produced, and that of the external shock ($R_{\rm ext}$), where the afterglow emission is produced. We investigate the EIC process for different values of $R_{\rm dis}/R_{\rm ext}$. We find that, for varying \( R_{\rm dis}/R_{\rm ext} \), the EIC scattering rate can differ by a factor of $\sim 2$. For GRB 221009A, the EIC emission is dominated during the early rising phase of the TeV afterglow. It then transitions to a phase dominated by the synchrotron self-Compton (SSC) emission as the intensity of the prompt emission decreases. Additionally, we investigate the effect of $\gamma \gamma$ absorption in the TeV afterglow caused by prompt MeV photons and find that it is insufficient to explain the early rapid rise in the TeV afterglow, even in the case of $R_{\rm dis}/R_{\rm ext} \sim 1$., Comment: 25 pages, 11 figures, 2 tables, comments are welcome

Since the development of photography art, many so-called "templates" have been formed, namely visual styles summarized from a series of themed and stylized photography works. In this paper, we propose to analysize and and summarize these 'templates' in photography by learning composite templates of photography images. We present a framework for learning a hierarchical reconfigurable image template from photography images to learn and characterize the "templates" used in these photography images. Using this method, we measured the artistic quality of photography on the photos and conducted photography guidance. In addition, we also utilized the "templates" for guidance in several image generation tasks. Experimental results show that the learned templates can well describe the photography techniques and styles, whereas the proposed approach can assess the quality of photography images as human being does.

Higgs and Goldstone modes manifest as fluctuations in the order parameter of system, offering insights into its phase transitions and symmetry properties. Exploring the dynamics of these collective excitations in a Rydberg atoms system advances various branches of condensed matter, particle physics, and cosmology. Here, we report an experimental signature of Higgs and Goldstone modes in a U(1) symmetry-broken Rydberg atomic gases. By constructing two probe fields to excite atoms, we observe the distinct phase and amplitude fluctuations of Rydberg atoms collective excitations under the particle-hole symmetry. Due to the van der Waals interactions between the Rydberg atoms, we detect a symmetric variance spectrum divided by the divergent regime and phase boundary, capturing the full dynamics of the additional Higgs and Goldstone modes. Studying the Higgs and Goldstone modes in Rydberg atoms allows us to explore fundamental aspects of quantum phase transitions and symmetry breaking phenomena, while leveraging the unique properties of these highly interacting systems to uncover new physics and potential applications in quantum simulation.

Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variable interaction. To alleviate this problem, we propose a new sparse additive model, named generalized sparse additive model with unknown link function (GSAMUL), in which the component functions are estimated by B-spline basis and the unknown link function is estimated by a multi-layer perceptron (MLP) network. Furthermore, $\ell_{2,1}$-norm regularizer is used for variable selection. The proposed GSAMUL can realize both variable selection and hidden interaction. We integrate this estimation into a bilevel optimization problem, where the data is split into training set and validation set. In theory, we provide the guarantees about the convergence of the approximate procedure. In applications, experimental evaluations on both synthetic and real world data sets consistently validate the effectiveness of the proposed approach.

Existing diffusion-based methods for inverse problems sample from the posterior using score functions and accept the generated random samples as solutions. In applications that posterior mean is preferred, we have to generate multiple samples from the posterior which is time-consuming. In this work, by analyzing the probability density evolution of the conditional reverse diffusion process, we prove that the posterior mean can be achieved by tracking the mean of each reverse diffusion step. Based on that, we establish a framework termed reverse mean propagation (RMP) that targets the posterior mean directly. We show that RMP can be implemented by solving a variational inference problem, which can be further decomposed as minimizing a reverse KL divergence at each reverse step. We further develop an algorithm that optimizes the reverse KL divergence with natural gradient descent using score functions and propagates the mean at each reverse step. Experiments demonstrate the validity of the theory of our framework and show that our algorithm outperforms state-of-the-art algorithms on reconstruction performance with lower computational complexity in various inverse problems., Comment: 10 pages, 7 figures, conference

Microperforated panels (MPPs) display excellent capacity in noise control applications owing to their high strength, simple design, and efficacy in low-frequency sound absorption. Traditionally, the development of MPPs has relied on a trial-and-error design approach. Although simple optimization-based methods have recently begun to be employed, these designs often overlook practical considerations, such as the increased costs associated with adding more MPP layers, which presents a gap to achieve the practical feasibility of MPP deployment. To address this, the study aims to develop an inverse multi-objective optimization-empowered framework for MPP design to enhance low-frequency noise mitigation while minimizing fabrication costs. Specifically, a finite element (FE) model is established to conduct the acoustic analysis of MPPs, followed by thorough experimental validation. A novel multi-objective particle swarm optimization algorithm (MOPSO) is then developed to cope with mixed-type design variables with interrelations inherent to the MPP architecture. Using the high-fidelity FE model as a cornerstone, the MOPSO guides the inverse optimization analysis to yield multiple non-dominant solutions. These solutions not only avoid the trap of local optima, but also allow for continuous screening to ensure the engineering viability based on empirical judgment. The results clearly demonstrate the effectiveness of the proposed methodology. The MPPs designed in this study show great potential for mitigating indoor noise in buildings, addressing noise issues arising from rapid urbanization and transportation development. Furthermore, the novel optimization strategy proposed in this study holds wide applicability for other sound absorption materials., Comment: 32 pages, 11 figures

The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs. To address this issue, we introduce R-Bench, a benchmark focused on the **Real-world Robustness of LMMs**. Specifically, we: (a) model the complete link from user capture to LMMs reception, comprising 33 corruption dimensions, including 7 steps according to the corruption sequence, and 7 groups based on low-level attributes; (b) collect reference/distorted image dataset before/after corruption, including 2,970 question-answer pairs with human labeling; (c) propose comprehensive evaluation for absolute/relative robustness and benchmark 20 mainstream LMMs. Results show that while LMMs can correctly handle the original reference images, their performance is not stable when faced with distorted images, and there is a significant gap in robustness compared to the human visual system. We hope that R-Bench will inspire improving the robustness of LMMs, **extending them from experimental simulations to the real-world application**. Check https://q-future.github.io/R-Bench for details.

Recent works suggest that, in multiplanetary systems, a close-in exoplanet can sometimes avoid becoming tidally locked to its host star if it is captured into a secular spin-orbit resonance with a companion planet. In such a resonance, the planet remains at a sub-synchronous spin rate and an appreciable obliquity (the planet's spin-orbit misalignment angle). However, many of these works have only considered planets with fluid-like rheologies. Recent observations suggest that planets up to a few Earth masses may be rocky and thus may have an appreciable rigidity. In this work, we study the spin-orbit dynamics of such rigid planets using a linear dissipative tidal model and not enforcing principal axis rotation about the body's shortest principal axis. We identify a new class of spin-orbit resonances when the planet spins at twice its orbital frequency. These resonances exist at nonzero obliquity and spontaneously excite non-principal-axis rotation upon resonance capture. While these resonances eventually disappear as tidal dissipation damps the obliquity to zero (and the body returns to principal-axis rotation), they still modify the spin evolutionary history of the planet. Such resonances may enhance the prevalence of secular spin-orbit resonances in exoplanetary systems., Comment: 20 pages, 4 figures, submitted to ApJ. Comments welcome!

As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of decreasing performance when combining disparate models. Various techniques have been proposed for the aggregation of pre-trained LLMs, including model merging, Mixture-of-Experts, and stacking. Despite their merits, a comprehensive comparison and synergistic application of them to a diverse model zoo is yet to be adequately addressed. In light of this research gap, this paper introduces Model-GLUE, a holistic LLM scaling guideline. First, our work starts with a benchmarking of existing LLM scaling techniques, especially selective merging, and variants of mixture. Utilizing the insights from the benchmark results, we formulate an strategy for the selection and aggregation of a heterogeneous model zoo characterizing different architectures and initialization. Our methodology involves the clustering of mergeable models and optimal merging strategy selection, and the integration of clusters through a model mixture. Finally, evidenced by our experiments on a diverse Llama-2-based model zoo, Model-GLUE shows an average performance enhancement of 5.61%, achieved without additional training. Codes are available at: https://github.com/Model-GLUE/Model-GLUE., Comment: 24 pages, 4 figures, accepted to NeurIPS 2024 Datasets and Benchmarks Track

Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for long-form and variable-length music generation and editing controlled by text and melody prompts. For more precise and fine-grained melody control, we introduce a novel top-$k$ constant-Q Transform representation as the melody prompt, reducing ambiguity compared to previous representations (e.g., chroma), particularly for music with multiple tracks or a wide range of pitch values. To effectively balance the control signals from text and melody prompts, we adopt a curriculum learning strategy that progressively masks the melody prompt, resulting in a more stable training process. Experiments have been performed on text-to-music generation and music-style transfer tasks using open-source instrumental recording data. The results demonstrate that by extending StableAudio, a pre-trained text-controlled DiT model, our approach enables superior melody-controlled editing while retaining good text-to-music generation performance. These results outperform a strong MusicGen baseline in terms of both text-based generation and melody preservation for editing. Audio examples can be found at https://stable-audio-control.github.io/web/., Comment: 5 pages, 1 figure

Laminar separation bubbles around airfoils lead to the growth of instability waves, which enhances to acoustic scattering at the trailing-edge, forming a feedback loop that produces to tonal noise. To reduce the trailing-edge tonal noise, an array of roughness elements was used over a NACA0012 airfoil, at low angles of attack and moderate Reynolds number. Aeroacoustics and flow visualization experiments were performed for four configurations: a baseline smooth airfoil, two configurations with roughness elements on either airfoil surface (pressure side or suction side), and a fourth configuration with roughness elements at both airfoil surfaces. The roughness elements are made up of a row of spanwise periodically spaced cylinders, which were placed close to the mid-chord position. It is expected the proposed array of roughness elements stabilizes the Kelvin-Helmholtz instability mechanism that is present on the separated shear layer through the induction of streak structures. The separation bubble is assessed using oil flow visualizations, and the cases with roughness elements show the generation of streaks downstream of the array cylinders, disrupting the separation bubble. Acoustic measurements results show a decrease, and in some cases the total suppression, of the tonal noise at Reynolds numbers ranging from 0.6$\times$10$^5$ to 2.5$\times$10$^5$, and angles of attack ranging from 0 to 4 degrees., Comment: 20 pages, 17 figures

In a combined experimental and numerical effort we investigate airfoil tonal noise generation and reduction. The means of noise control are streak generators in form of cylindrical roughness elements. These elements are placed periodically along the span of airfoil at the mid chord streamwise position. Experiments are performed for a wide range of Reynolds number and angle of attack in a companion work (Alva et al. 2024). In the present work we concentrate on numerical investigations for a further investigation of selected cases. We have performed wall-resolved large-eddy simulations for a NACA 0012 airfoil at zero angle of attack and Mach 0.3. Two Reynolds numbers (80,000 and 100,000) have been investigated, showing acoustic results consistent with experiments at the same Reynolds but lower Mach numbers. Roughness elements attenuate tones in the acoustic field and, for the higher Reynolds number, suppress them. Through Fourier decomposition and spectral POD analysis of streamwise velocity data, dominating structures have been identified. Further, the coupling between structures generated by surface roughness and instability modes (Kelvin-Helmholtz) of shear layer has been identified through stability analysis, suggesting stabilisation mechanisms by which the sound generation by the airfoil is reduced by the roughness elements., Comment: 24 pages, 15 figures

Motivated by a rheumatoid arthritis clinical trial, we propose a new Bayesian method called SPx, standing for synthetic prior with covariates, to borrow information from historical trials to reduce the control group size in a new trial. The method involves a novel use of Bayesian model averaging to balance between multiple possible relationships between the historical and new trial data, allowing the historical data to be dynamically trusted or discounted as appropriate. We require only trial-level summary statistics, which are available more often than patient-level data. Through simulations and an application to the rheumatoid arthritis trial we show that SPx can substantially reduce the control group size while maintaining Frequentist properties.

Building accurate representations of the environment is critical for intelligent robots to make decisions during deployment. Advances in photorealistic environment models have enabled robots to develop hyper-realistic reconstructions, which can be used to generate images that are intuitive for human inspection. In particular, the recently introduced \ac{3DGS}, which describes the scene with up to millions of primitive ellipsoids, can be rendered in real time. \ac{3DGS} has rapidly gained prominence. However, a critical unsolved problem persists: how can we fuse multiple \ac{3DGS} into a single coherent model? Solving this problem will enable robot teams to jointly build \ac{3DGS} models of their surroundings. A key insight of this work is to leverage the {duality} between photorealistic reconstructions, which render realistic 2D images from 3D structure, and \emph{3D foundation models}, which predict 3D structure from image pairs. To this end, we develop PhotoReg, a framework to register multiple photorealistic \ac{3DGS} models with 3D foundation models. As \ac{3DGS} models are generally built from monocular camera images, they have \emph{arbitrary scale}. To resolve this, PhotoReg actively enforces scale consistency among the different \ac{3DGS} models by considering depth estimates within these models. Then, the alignment is iteratively refined with fine-grained photometric losses to produce high-quality fused \ac{3DGS} models. We rigorously evaluate PhotoReg on both standard benchmark datasets and our custom-collected datasets, including with two quadruped robots. The code is released at \url{ziweny11.github.io/photoreg}.

In the realm of competitive gaming, 3D first-person shooter (FPS) games have gained immense popularity, prompting the development of game AI systems to enhance gameplay. However, deploying game AI in practical scenarios still poses challenges, particularly in large-scale and complex FPS games. In this paper, we focus on the practical deployment of game AI in the online multiplayer competitive 3D FPS game called Arena Breakout, developed by Tencent Games. We propose a novel gaming AI system named Private Military Company Agent (PMCA), which is interactable within a large game map and engages in combat with players while utilizing tactical advantages provided by the surrounding terrain. To address the challenges of navigation and combat in modern 3D FPS games, we introduce a method that combines navigation mesh (Navmesh) and shooting-rule with deep reinforcement learning (NSRL). The integration of Navmesh enhances the agent's global navigation capabilities while shooting behavior is controlled using rule-based methods to ensure controllability. NSRL employs a DRL model to predict when to enable the navigation mesh, resulting in a diverse range of behaviors for the game AI. Customized rewards for human-like behaviors are also employed to align PMCA's behavior with that of human players.

Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsatisfactory because the models used in FedSeqRec have to be lightweight to accommodate communication bandwidth and clients' on-device computational resource constraints. Recently, large language models (LLMs) have exhibited strong transferable and generalized language understanding abilities and therefore, in the NLP area, many downstream tasks now utilize LLMs as a service to achieve superior performance without constructing complex models. Inspired by this successful practice, we propose a generic FedSeqRec framework, FELLAS, which aims to enhance FedSeqRec by utilizing LLMs as an external service. Specifically, FELLAS employs an LLM server to provide both item-level and sequence-level representation assistance. The item-level representation service is queried by the central server to enrich the original ID-based item embedding with textual information, while the sequence-level representation service is accessed by each client. However, invoking the sequence-level representation service requires clients to send sequences to the external LLM server. To safeguard privacy, we implement dx-privacy satisfied sequence perturbation, which protects clients' sensitive data with guarantees. Additionally, a contrastive learning-based method is designed to transfer knowledge from the noisy sequence representation to clients' sequential recommendation models. Furthermore, to empirically validate the privacy protection capability of FELLAS, we propose two interacted item inference attacks. Extensive experiments conducted on three datasets with two widely used sequential recommendation models demonstrate the effectiveness and privacy-preserving capability of FELLAS.

Electric guitar tone modeling typically focuses on the non-linear transformation from clean to amplifier-rendered audio. Traditional methods rely on one-to-one mappings, incorporating device parameters into neural models to replicate specific amplifiers. However, these methods are limited by the need for specific training data. In this paper, we adapt a model based on the previous work, which leverages a tone embedding encoder and a feature wise linear modulation (FiLM) condition method. In this work, we altered conditioning method using a hypernetwork-based gated convolutional network (GCN) to generate audio that blends clean input with the tone characteristics of reference audio. By extending the training data to cover a wider variety of amplifier tones, our model is able to capture a broader range of tones. Additionally, we developed a real-time plugin to demonstrate the system's practical application, allowing users to experience its performance interactively. Our results indicate that the proposed system achieves superior tone modeling versatility compared to traditional methods., Comment: demo of the ISMIR paper

In this article, motivated by the regularity theory of the solutions of doubly nonlinear parabolic partial differential equations the authors introduce the off-diagonal two-weight version of the parabolic Muckenhoupt class with time lag. Then the authors introduce the uncentered parabolic fractional maximal operator with time lag and characterize its two-weighted boundedness (including the endpoint case) via these weights under an extra mild assumption (which is not necessary for one-weight case). The most novelty of this article exists in that the authors further introduce a new parabolic shaped domain and its corresponding parabolic fractional integral with time lag and, moreover, applying the aforementioned two-weighted boundedness of the uncentered parabolic fractional maximal operator with time lag, the authors characterize the (two-)weighted boundedness (including the endpoint case) of these parabolic fractional integrals in terms of the off-diagonal (two-weight) parabolic Muckenhoupt class with time lag; as applications, the authors further establish a parabolic weighted Sobolev embedding and a priori estimate for the solution of the heat equation. The key tools to achieve these include the parabolic Calder\'on--Zygmund-type decomposition, the chaining argument, and the parabolic Welland inequality which is obtained by making the utmost of the geometrical relation between the parabolic shaped domain and the parabolic rectangle.

We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the locations of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron

In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, most existing methods learn a network to prune redundant visual tokens and require additional training data. Differently, we propose an efficient training-free token optimization mechanism dubbed SparseVLM without extra parameters or fine-tuning costs. Concretely, given that visual tokens complement text tokens in VLMs for linguistic reasoning, we select visual-relevant text tokens to rate the significance of vision tokens within the self-attention matrix extracted from the VLMs. Then we progressively prune irrelevant tokens. To maximize sparsity while retaining essential information, we introduce a rank-based strategy to adaptively determine the sparsification ratio for each layer, alongside a token recycling method that compresses pruned tokens into more compact representations. Experimental results show that our SparseVLM improves the efficiency of various VLMs across a range of image and video understanding tasks. In particular, LLaVA equipped with SparseVLM reduces 61% to 67% FLOPs with a compression ratio of 78% while maintaining 93% of the accuracy. Our code is available at https://github.com/Gumpest/SparseVLMs., Comment: 17 pages

When an electric current runs through a fluid, it generates heat via a process known as ``Ohmic heating'' or ``Joule heating.'' While this phenomenon, and its quantification known as Joule's Law, is the first studied example of heat generation via an electric field, many difficulties still remain in understanding its consequences. In particular, a magnetic fluid naturally generates an electric field via Amp\`ere's law, which heats the fluid via Joule's law. This heat in turn gives rise to convective effects in the fluid, creating complicated dynamical behavior. This has been modeled (in other works) by including an Ohmic heating term in the Magnetohydrodynamic-Boussinessq (MHD-B) equation. However, the structure of this term causes major analytical difficulties, and basic questions of well-posedness remain open problems, even in the two-dimensional case. Moreover, standard approaches to finding a globally well-posed approximate model, such as filtering or adding high-order diffusion, are not enough to handle the Ohmic heating term. In this work, we present a different approach that we call ``calming'', which reduces the effective algebraic degree of the Ohmic heating term in a controlled manner. We show that this new model is globally well-posed, and moreover, its solutions converge to solutions of the MHD-B system with the Ohmic heating term (assuming that solutions to the original equation exist), making it the first globally well-posed approximate model for the MHD-B equation with Ohmic heating.

Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over 4x increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.