arXiv:2607.17725v1 Announce Type: new
Abstract: The intricate motion arising from fluid--boundary interactions is visually compelling, yet notoriously difficult and computationally expensive to simulate in the presence of complex boundaries. Accurately resolving boundary geometry requires body-fitted grids constructed via cut-cell methods, which often leads to poorly conditioned linear systems in the pressure projection stage and, consequently, prohibitive computational cost. We present FastVEM, an efficient boundary-conforming fluid simulation framework that enables high-fidelity flow--boundary interaction at substantially reduced cost. Computational efficiency is achieved through a coordinated, top-down design spanning numerical discretization, grid construction, and linear solvers. FastVEM adopts a Virtual Element Method (VEM) discretization to robustly enforce incompressibility and boundary conditions on irregular body-fitted grids, and employs a VEM polynomial-space Particle-in-Cell scheme for advection. Complementing this discretization, a convexity-preserving cut-cell strategy is introduced to construct simulation-friendly body-fitted grids. To accelerate pressure projection, we develop a Galerkin geometric multigrid solver featuring a diffusion-free prolongation operator that prevents coarse-level matrix densification, along with a nested, boundary-aware grid hierarchy that ensures well-posed placement of coarse-level degrees of freedom. Compared to prior cut-cell--based fluid simulators, FastVEM speeds up the computationally dominant pressure projection stage by up to 100x, while robustly handling even more challenging boundary geometries.
Science Journals
arXiv:2607.18128v1 Announce Type: cross
Abstract: Entropy regularization smooths equilibrium policies in time-inconsistent stochastic control. At low temperature, the same Gibbs response can strongly amplify errors in learned rewards and dynamics. We show that the derivative of an exploratory equilibrium is governed by a backward Volterra-parabolic resolvent. Along an aligned positive mode, a lower bound has the same exponential order. A block decomposition identifies the source of the amplification: causal paths contribute powers of 1/tau, whereas a positive feedback cycle can produce exponential growth.
At fixed temperature, a local equilibrium branch is twice differentiable with respect to finite-dimensional model parameters, which yields a function-valued delta method. A bounded uniformly elliptic diffusion realizes this path-cycle distinction in every finite dimension. Closing one positive cycle changes the root-n linear-response boundary from a power law to order 1/log n; along the cyclic Perron mode, right-endpoint discretization is relatively consistent exactly when N tau^2 -> infinity. An affine model also gives an exact nonlinear transition at the Lambert-W temperature beta T / W(beta T sqrt(n)). Numerical calculations illustrate these rates.
arXiv:2607.18196v1 Announce Type: cross
Abstract: Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Although SQD is considered robust to noisy samples and imperfect classical inputs, its robustness across practical deployment choices has not been systematically analyzed. As a result, shot budgets, qubit layouts, noise mitigation strategies, and the coupled-cluster singles and doubles (CCSD) amplitudes that initialize the ansatz are often chosen without clear empirical guidance. We analyze SQD robustness on IBM Heron hardware across these dimensions. Structured CCSD-amplitude perturbations, including complete zeroing, produce only modest energy shifts from the clean baseline. Differences across layouts and noise-mitigation settings are large in the first recovery iteration but narrow within a few iterations. Accuracy saturates at moderate shot budgets, while very large budgets slightly worsen recovered energies, likely because working-set selection limits the value of additional samples. These results identify where SQD provides genuine deployment robustness and where its limits remain.
arXiv:2607.17260v1 Announce Type: cross
Abstract: We consider the uniform exponential stability analysis of infinite-dimensional impulsive systems defined on a Banach or Hilbert space, whose flow is governed by a fixed $C_0$-semigroup generator and whose jumps occur at a prescribed time sequence. While the flow and jump maps are themselves time-invariant, the time-triggered impulses render the propagator a genuinely time-varying evolution family, which is the source of the analysis difficulty addressed here. We combine ideas from hybrid systems theory and infinite-dimensional systems to produce operator-based stability conditions, which can be analytically or numerically checked via convex programming. Necessary and sufficient conditions for the uniform exponential stability of impulsive systems on Banach spaces are obtained in the context of a fixed impulse-times sequence but also of arbitrary, constant, minimum, and range dwell-times using both non-coercive and coercive Lyapunov functionals. Some of those results are then adapted to systems on a Hilbert space and quadratic Lyapunov functionals. As an application, linear switched systems are shown to be an exact special case: reformulated as impulsive systems with unit-norm selector jumps, they inherit non-coercive and clock-dependent dwell-time stability conditions on both Banach and Hilbert spaces. Theoretical and numerical examples are given for illustration, notably on the sampled-data control of time-delay systems.
arXiv:2607.18209v1 Announce Type: cross
Abstract: This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary across environments, whereas the latent structure is decomposed into invariant factors with shared loadings and heterogeneous factors with environment-specific loadings. Such a model is motivated by transfer learning and latent factor regression, where one seeks stable low-dimensional representations for both interpretation and robust out-of-sample prediction of the response $Y$. Leveraging the invariance principle, we show that the invariant and heterogeneous factors are disentangled under a minimal structural condition. Based on this, we propose ATLAS, an Auxiliary-label and invariance-guided Transfer via Latent Alignment across heterogeneous environmentS. ATLAS is a unified procedure that leverages the invariance principle to separate aligned invariant and unaligned heterogeneous factors, and further exploits supervision from auxiliary labels to extract prediction-invariant and transferable factors from those unaligned heterogeneous factors. ATLAS yields near-oracle performance for downstream latent factor regression, enables transferable prediction in new environments through the full latent signal when auxiliary labels are available, and reduces to robust invariant-factor-only prediction otherwise. We establish sharp non-asymptotic error bounds for recovering invariant and heterogeneous factors, identifying all the response-invariant factors, and estimating the invariant signal in $Y$.
arXiv:1905.12913v2 Announce Type: replace
Abstract: This paper investigates the problem of utilizing network topology and partial timestamps to detect the information source in a network. The problem incurs prohibitive cost under canonical maximum likelihood estimation (MLE) of the source due to the exponential number of possible infection paths. Our main idea of source detection, however, is to approximate the MLE by an alternative infection path based estimator, the essence of which is to identify the most likely infection path that is consistent with observed timestamps. The source node associated with that infection path is viewed as the estimated source $\hat{v}$. We first study the case of tree topology, where by transforming the infection path based estimator into a linear integer programming, we find a reduced search region that remarkably improves the time efficiency. Within this reduced search region, the estimator $\hat{v}$ is provably always on a path which we term as \emph{candidate path}. This notion enables us to analyze the distribution of $d(v^{\ast},\hat{v})$, the error distance between $\hat{v}$ and the true source $v^{\ast}$, on arbitrary tree, which allows us to obtain for the first time, in the literature provable performance guarantee of the estimator under limited timestamps. Specifically, on the infinite $g$-regular tree with uniform sampled timestamps, we get a refined performance guarantee in the sense of a constant bounded $d(v^{\ast},\hat{v})$. By virtue of time labeled BFS tree, the estimator still performs fairly well when extended to more general graphs. Experiments on both synthetic and real datasets further demonstrate the superior performance of our proposed algorithms.
arXiv:2607.17421v1 Announce Type: cross
Abstract: The strong chromatic index $\chi'_s(G)$ is the smallest number of colours needed to colour the edges of a graph $G$ so that any two edges at distance at most $2$ receive different colours. Using the \emph{local flag algebra} framework introduced in a companion paper, we prove $\chi'_s(G) \leq 1.73\,\Delta(G)^2$ for every graph $G$ of maximum degree $\Delta(G)$, $\chi'_s(G) \leq 1.6255\,\Delta(G)^2$ for every bipartite $G$, and $\chi'_s(G) \leq 1.6633\,\Delta_A(G)\,\Delta_B(G)$ for every bipartite $G$ of side maximum degrees $\Delta_A(G), \Delta_B(G)$ with rational $\Delta_B(G)/\Delta_A(G) \in (0, 1]$, provided $\Delta(G)$, $\Delta_A(G)$, $\Delta_B(G)$ are sufficiently large. These three bounds make progress towards three established conjectures: those of Erd\H{o}s-Ne\v{s}et\v{r}il (1985) for general graphs, Faudree-Gy\'arf\'as-Schelp-Tuza (1989) for bipartite graphs, and Brualdi-Quinn Massey (1993) in the asymmetric bipartite setting.
Additionally, for the random bipartite graph $G \sim G(n_A, n_B, p)$ at constant $p \in (0,1)$ and bounded aspect ratio $\max(n_A, n_B) = O(\min(n_A, n_B))$, we prove the Brualdi-Quinn Massey bound $\chi'_s(G) \leq \Delta_A(G)\,\Delta_B(G)$ asymptotically almost surely.
arXiv:2607.17484v1 Announce Type: cross
Abstract: Solar wind alpha particles exhibit preferential heating and acceleration relative to protons; however, their behavior in the vicinity of turbulent coherent structures remains less understood. We report the first evidence of localized alpha particle and proton heating within coherent structures identified using the Partial Variance of Increments (PVI) method, based on Parker Solar Probe (PSP) observations. Our results show that high-PVI events are associated with significant, species-dependent temperature enhancements: protons undergo a relative larger temperature increase than alpha particles. This preferential proton heating produces a localized decrease in the alpha-to-proton temperature ratio, indicating that the plasma is driven toward thermal equilibration between species. The heating is also anisotropic, being dominated by enhancements in the perpendicular temperature. These temperature-signatures coincide with a pronounced reduction in the normalized alpha-proton differential flow speed and a localized minimum in the Coulomb collision age, suggesting that the relaxation is affected primarily by collisionless kinetic effects. These findings provide new insight into the intermittent energy conversion and ion thermodynamics in the solar wind.
arXiv:1911.11966v2 Announce Type: replace
Abstract: With the advent of a new round of the Industrial Revolution, the Industrial Internet will carry the convergence of heterogeneous network and the dynamic reconfiguration of industrial equipment. In order to further provide higher performance of network capabilities, the Industrial Internet has experienced unprecedented growth while facing enormous challenges from the actual needs of industrial networks. The typical scenarios in industrial applications, combined with the technical advantages of mobile edge computing, are described in view of the low latency, high bandwidth and high reliability demanded by the Industrial Internet in the new era. The key technologies of mobile edge computing for the Industrial Internet have been outlined in this treatise, whose feasibility and importance are demonstrated by typical industrial applications that have been deployed. As combined with the development trend of the Industrial Internet, this paper summarizes the existing work and discusses the future research direction of key technologies of mobile edge computing for the Industrial Internet.
arXiv:2103.06579v2 Announce Type: replace
Abstract: Aiming at the local overload of multi-controller deployment in software-defined networks, a load balancing mechanism of SDN controller based on reinforcement learning is designed. The initial paired migrate-out domain and migrate-in domain are obtained by calculating the load ratio deviation between the controllers, a preliminary migration triplet, contains migration domain mentioned above and a group of switches which are subordinated to the migrate-out domain, makes the migration efficiency reach the local optimum. Under the constraint of the best efficiency of migration in the whole and without migration conflict, selecting multiple sets of triples based on reinforcement learning, as the final migration of this round to attain the global optimal controller load balancing with minimum cost. The experimental results illustrate that the mechanism can make full use of the controllers' resources, quickly balance the load between controllers, reduce unnecessary migration overhead and get a faster response rate of the packet-in request.
arXiv:2607.17067v1 Announce Type: new
Abstract: Generative AI (GenAI) is reshaping software engineering, raising concerns about how the development pathway through which juniors become seniors is being eroded. While macro statistics show a decline in junior hiring and controlled studies demonstrate the effects of AI on individual task performance, the mechanisms through which GenAI reshapes early-career development in real organizational and educational contexts have not been thoroughly examined. Through 14 semi-structured interviews with juniors at the threshold of entering software engineering and senior software engineers in South Korea, analyzed using Reflexive Thematic Analysis, we reveal a foundational pattern of Absorption -- GenAI redirects entry-level work into senior-AI workflows -- and three consequences: (1) juniors losing the productive struggle through which expertise once developed; (2) the structural reproduction of this loss through collective normalization of GenAI use in university classrooms; and (3) the perceptual asymmetry between seniors and juniors that prevents either side from correcting these dynamics on their own. By extending learning theory and situated cognition to organizational and institutional scales, we argue that GenAI appears to be absorbing not just specific categories of tasks but also parts of the pathway through which the next generation of seniors is formed. Preserving this pathway will require deliberate institutional design across classrooms, workplaces, and the evaluation criteria for juniors.
arXiv:2103.06611v2 Announce Type: replace
Abstract: With the mass deployment of computing-intensive applications and delay-sensitive applications on end devices, only adequate computing resources can meet differentiated services' delay requirements. By offloading tasks to cloud servers or edge servers, computation offloading can alleviate computing and storage limitations and reduce delay and energy consumption. However, few of the existing offloading schemes take into consideration the cloud-edge collaboration and the constraint of energy consumption and task dependency. This paper builds a collaborative computation offloading model in cloud and edge computing and formulates a multi-objective optimization problem. Constructed by fusing optimal transport and Policy-Based RL, we propose an Optimal-Transport-Based RL approach to resolve the offloading problem and make the optimal offloading decision for minimizing the overall cost of delay and energy consumption. Simulation results show that the proposed approach can effectively reduce the cost and significantly outperforms existing optimization solutions.
arXiv:2607.17906v1 Announce Type: new
Abstract: Four-dimensional MRI (4D MRI) characterizes respiratory organ motion, yet existing reconstruction pipelines are tightly coupled to specific acquisition platforms (e.g., non-Cartesian trajectories with self-gating, vendor-specific navigators, or external respiratory hardware), limiting broad adoption across diverse clinical and research settings, including low-field, open-bore, and non-supine imaging. We present SIMPLE-4D (Surrogate-free, IMplicit, PortabLE 4D MRI), a software-first portable workflow that operates entirely on reconstructed slices from standard fast multi-slice 2D MRI and requires no pulse-sequence modification, no non-Cartesian trajectory, no navigator, and no external hardware. SIMPLE-4D combines an acquisition-agnostic front end consuming standard 2D protocols, a surrogate-free variational motion encoder that extracts a compact motion code directly from each 2D slice, and a physics-aware continuous spatio-temporal reconstruction based on a hash-encoded implicit neural representation (INR) with a SIREN deformation network producing bidirectional cycle-consistent DVFs and motion-dependent Gauss-Legendre thick-slice quadrature. Bidirectionality yields a complete inter-frame motion model by composition, supporting downstream tasks such as dose accumulation without retraining. We validate the identical pipeline on two contrasting datasets: a 1.5 T clinical bSSFP dataset (5 volunteers, 3 sessions each) and a 0.5 T open-bore HASTE dataset (5 volunteers, supine and upright). To our knowledge, this is the first per-frame 4D volumetric respiratory MRI reconstruction on a weight-bearing upright open-bore low-field scanner from reconstructed 2D Cartesian slices alone. On low-field data the INR template additionally acts as an implicit denoiser, yielding +132% SNR. Systematic ablations isolate each component's contribution.
arXiv:2607.17678v1 Announce Type: cross
Abstract: Monte-Carlo trajectory (quantum-jump) methods are the practical route to simulating noisy quantum circuits once the exact density-matrix method is precluded by its $4^n$ memory cost. Their bottleneck is estimator variance: resolving one expectation value can demand thousands of trajectories. Recent tensor-network work shows that \emph{variance-reduced unravelings} -- projector and analog sampling -- sharply cut this variance, but only on CPU matrix-product-state backends, with no path into production tooling. We implement both unravelings on a \emph{GPU dense-statevector} trajectory engine and validate them against the exact density matrix (ideal-circuit fidelity $1-2.2\times10^{-16}$; $1/\sqrt{N}$ convergence; all unravelings unbiased to trace distance $<0.01$). On a single consumer GPU, projector unraveling reaches a target standard error with $20.8\times$ fewer trajectories than Qiskit-Aer's \texttt{batched\_shots\_gpu} at $n=10$, a factor that holds at $19$--$26\times$ across $n=8$--$20$. A regime map places analog sampling optimal at weak noise and projector at strong noise, crossing near $\gamma t\approx0.35$. We further report a systems finding: Qiskit-Aer applies noise at the \emph{channel} level and reconstructs a canonical Kraus decomposition at apply time, discarding any user-supplied unraveling, so variance-reduced unravelings cannot be delivered through its public API. Because Aer's Born-rule collapse machinery already exists, we specify a minimal change that would unlock the technique in production.
arXiv:2106.04770v2 Announce Type: replace
Abstract: We study parameter nonuniqueness in continuous-width depth-two fully connected neural networks. Our main contribution is a direct method for solving the neural-network equation $S[\gamma]=f$. Starting from the Fourier expression of the synthesis operator, separation of variables produces a ridgelet particular solution and identifies every homogeneous direction. To isolate the argument, we first prove an abstract reconstruction formula for unitary factorizations, yielding the adjoint, normalized right inverse, and orthogonal solution geometry. We then specialize this formula to neural-network synthesis: for tempered-distribution activations such as ReLU, we equip the activation class $A_{s,t}$ with a Hilbert structure, construct compatible coefficient and parameter Hilbert spaces $H_{s,t}$ and $G_{s,t}$, and prove that $S:G_{s,t}\to L^2(\mathbb R^m)$ is bounded. The resulting ridgelet expansion exhausts the null space and the complete solution set and identifies the unique minimum-norm parameter distribution. Concrete examples give adjoint ridgelet functions for standard activations. Further developments show that finite-measure null elements admit normalized width-$N$ discretizations with $O(N^{-1/2})$ output error and characterize how additive parameter perturbations can reveal information encoded in the null space. A Lean 4 blueprint for the main results is available at https://shosonoda.github.io/lean-ridgelet/ .
arXiv:2310.16152v5 Announce Type: replace
Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis. These applications are trained on datasets from many FL participants that often include privacy-sensitive data, such as healthcare records, phone/credit card numbers, login credentials, etc. Although FL enables computation without necessitating clients to share their raw data, existing works show that privacy leakage is still probable in federated language models. In this paper, we present two novel findings on the leakage of privacy-sensitive user data from federated large language models without requiring access to gradients. Firstly, we make a key observation that model snapshots from the intermediate rounds in FL can cause greater privacy leakage than the final trained model. Secondly, we identify that a malicious FL participant can aggravate the leakage by tampering with the model's selective weights that are responsible for memorizing the sensitive training data of some other clients, even without any cooperation from the server. Our best-performing method increases the membership inference recall by 29% and achieves up to 71% private data reconstruction, evidently outperforming existing attacks that consider much stronger adversary capabilities. Lastly, we recommend a balanced suite of techniques for an FL client to defend against such privacy risk.
arXiv:2607.17733v1 Announce Type: new
Abstract: 4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-precision integer quantization, but often relies on software-managed scaling and frequent dequantization, incurring substantial overhead. Microscaling formats, such as MXINT, eliminate these inefficiencies by encoding scales in hardware, yet remain incompatible with rotation-based methods. Our analysis reveals that outliers vary in severity, from rare extremes to frequent mild deviations, and that quantization sensitivity is unevenly distributed across layers and columns. These insights motivate a fine-grained, sensitivity-guided approach. We introduce MXSens, a training-free method that assigns mixed mantissa bitwidths (4/6/8) based on column- and layer-wise sensitivity, naturally leveraging the block-wise structure of MXINT. MXSens outperforms state-of-the-art quantization methods across a range of models and tasks. Under the W4A4KV4 setting, MXSens achieves perplexities of 3.77 and 7.63 on LLaMA-2-70B and LLaMA-3-8B, respectively, substantially improving over existing baselines on WikiText-2. Our work establishes a new balance between accuracy and resource efficiency for LLM quantization.
arXiv:2607.16549v1 Announce Type: new
Abstract: Language models (LMs) are trained to excel at predicting the next word in the sequence given prior context, and humans also share this predictability in reading comprehension. Neuroscience research reveals that next-word predictability influences brain response, as recorded at millisecond resolution using electroencephalography (EEG). While our evidence indicates that advanced LMs achieve accuracies closely aligned with human performance at the next-word prediction task, this raises the question: Does higher prediction accuracy necessarily mean that these models adequately capture the cognitive signals associated with human reading comprehension? Here, we generate regressors for both humans and LMs based on two information measures, including top-1 prediction and surprisal, to predict event-related potential (ERP) elicited from EEG recordings which reflect different stages of cognitive processing during reading. We argue that modelling ERP patterns offers fine-grained analysis of the cognitive plausibility of various LMs during reading. Our results indicate that only surprisal potentially correlates with language-processing ERPs, especially for open-class words with high semantic content. Moreover, our findings challenge the assumption that scaling LMs with increased parameters and computational budgets will consistently lead to improved convergence with human-like linguistic processing.
arXiv:2607.16822v1 Announce Type: new
Abstract: This work proposes a compact and hardware-efficient frequency- and radiation-pattern-reconfigurable antenna (FPRA) for passive multi-target direction-of-arrival (DOA) estimation using a single receive RF chain. The antenna consists of a sectorized circular patch loaded with 16 PIN diodes. By switching the diode states, the current-concentration boundary on the patch is shifted, enabling reconfiguration of both the operating frequency and radiation pattern. With a single receive RF chain, the proposed antenna achieves beam scanning from -40 degrees to 40 degrees and provides multiple operating frequencies across the S- and C-bands. Based on these reconfigurable observation states, radiation-pattern switching is used to emulate the spatial sampling of a conventional antenna array, while multi-frequency observations introduce phase diversity to reduce the correlation among echoes from multiple passive targets illuminated by the same transmitter. Experimental results demonstrate that the combined virtual spatial sampling and frequency diversity enable passive multi-target DOA estimation without a conventional antenna array or multiple receive RF chains. The proposed FPRA offers a compact and hardware-efficient sensing solution for future integrated sensing and communication (ISAC) systems.
arXiv:2403.06025v4 Announce Type: replace
Abstract: We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS). CCS has been proved to be a key component for a carbon neutral society. However, scientists see there are challenges along the way including the high computational cost due to the large model scale and limitations to generalize a pre-trained model with complex physics. We tackle those challenges by training models directly from the subsurface geometry images. The goal is to understand the respons of land surface displacement due to carbon injection and utilize our trained models to inform decision making in CCS projects.
We implement multiple models (CNN, ResNet, and ResNetUNet) for static mechanics problem, which is a image prediction problem. Next, we use the LSTM and transformer for transient mechanics scenario, which is a video prediction problem. It shows ResNetUNet outperforms the others thanks to its architecture in static mechanics problem, and LSTM shows comparable performance to transformer in transient problem. This report proceeds by outlining our dataset in detail followed by model descriptions in method section. Result and discussion state the key learning, observations, and conclusion with future work rounds out the paper.
arXiv:2607.17101v1 Announce Type: cross
Abstract: This study proposes a novel scheme for distributing GHZ-equivalent states across repeater-based quantum networks, with particular focus on the analysis and mitigation of decoherence effects during transmission. The proposed scheme enables remote users to share graph states, which can be leveraged to implement various quantum communication protocols, such as quantum key distribution and quantum secret sharing. Compared with existing approaches, the proposed distributed scheme requires only O(N) qubits without introducing redundant entanglement structures. Together with the linear-scaling merging procedure in both controlled gate count and qubit usage, the proposed framework supports more efficient large-scale graph state distribution. To evaluate its feasibility and correctness, this study utilizes the quantum network simulation tool, NetSquid, to implement the proposed scheme. Simulation results demonstrate that the proposed approach is both effective and practical for executing quantum communication protocols within quantum networks.
arXiv:2403.12537v2 Announce Type: replace
Abstract: Foundation models have become pivotal in advancing computational pathology, particularly for whole slide image (WSI) classification. However, prevailing methodologies often rely on frozen, pre-trained models for feature extraction, overlooking the pronounced domain shift and task discrepancy between the pre-training and downstream tasks. To address this challenge, we propose PAMT, a novel Prompt-guided Adaptive Model Transformation framework that enables precise adaptation of general foundation models to the distinct domain of histopathology. To encapsulate the intricate distributions characteristic of histopathological data, we introduce Representative Patch Sampling (RPS) and Prototypical Visual Prompt (PVP), which reconstruct the input into compact yet highly informative representations. Further, to effectively bridge the domain gap, we incorporate Adaptive Model Transformation (AMT) via adapter modules within the feature extraction pipeline, facilitating the acquisition of domain-specific features by the foundation model. We conduct rigorous evaluation across 14 publicly available datasets and demonstrate consistent, substantial improvements in classification accuracy. These results establish PAMT as a compelling new benchmark for pathology image classification and underscore the critical value of targeted model adaptation within computational pathology.
arXiv:2607.17717v1 Announce Type: cross
Abstract: Active chiral fluids can support a nondissipative transport coefficient known as odd (or Hall) viscosity. Hydrodynamic descriptions of such fluids typically introduce odd viscosity phenomenologically. How such a response emerges from specific microscopic interactions remains incompletely understood. Here, building on classical shear rheology, we microscopically derive an odd rheological response in active chiral films: thin layers of torque-exerting, elongated particles anchored to a no-slip surface. A canonical realization of such a film is the bacterial carpet, in which flagellated bacteria are tethered head-down to a solid surface while their flagella remain free to spin and inject angular momentum into the surrounding fluid. Using a kinetic theory for the orientational dynamics of these anchored particles, we derive their stress response to an imposed shear flow. We reveal that shear-induced reorientation leads to a flow-aligned polarization and a transverse surface traction from which the odd-viscosity tensor follows in closed form. Numerical solutions of the nonlinear kinetic theory further highlight saturation of the transverse traction at strong shear, driven by shear-induced orientation dynamics -- signaling departure from linear response. Our results demonstrate how odd viscosity can emerge self-consistently as a coarse-grained rheological signature of active fluid-structure interaction and establish active chiral films as a new controllable setting for odd hydrodynamics.
arXiv:2404.01549v2 Announce Type: replace
Abstract: In the rapidly evolving domain of artificial intelligence, Large Language Models (LLMs) play a crucial role due to their advanced text processing and generation abilities. This study introduces a new strategy aimed at harnessing on-device LLMs in invoking software APIs. We meticulously compile a dataset derived from software API documentation and apply fine-tuning to LLMs with capacities of 2B, 3B and 7B parameters, specifically to enhance their proficiency in software API interactions. Our approach concentrates on refining the models' grasp of API structures and syntax, significantly enhancing the accuracy of API function calls. Additionally, we propose \textit{conditional masking} techniques to ensure outputs in the desired formats and reduce error rates while maintaining inference speeds. We also propose a novel benchmark designed to evaluate the effectiveness of LLMs in API interactions, establishing a foundation for subsequent research. Octopus, the fine-tuned model, is proved to have better performance than GPT-4 for the software APIs calling. This research aims to advance automated software development and API integration, representing substantial progress in aligning LLM capabilities with the demands of practical software engineering applications.
arXiv:2404.19296v2 Announce Type: replace
Abstract: Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by OpenAI and various models by Anthropic are expensive and consume substantial energy. In contrast, the open-source community has produced competitive models, like Llama3. Furthermore, niche-specific smaller language models, such as those tailored for legal, medical or financial tasks, have outperformed their proprietary counterparts. This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages functional tokens to intelligently direct user queries to the most appropriate vertical model and reformat the query to achieve the best performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models, excels in selection and parameter understanding and reformatting. Additionally, we explore the use of graph as a versatile data structure that effectively coordinates multiple open-source models by harnessing the capabilities of the Octopus model and functional tokens. Use our open-sourced GitHub (https://www.nexa4ai.com/) to try Octopus v4 models (https://huggingface.co/NexaAIDev/Octopus-v4), and contrite to a larger graph of language models. By activating models less than 10B parameters, we achieved SOTA MMLU score of 74.8 among the same level models.