Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To overcome these challenges, we present VeritasFi, an innovative hybrid RAG framework that incorporates a multi-modal preprocessing pipeline alongside a cutting-edge two-stage training strategy for its re-ranking component. VeritasFi enhances financial QA through three key innovations: (1) A multi-modal preprocessing pipeline that seamlessly transforms heterogeneous data into a coherent, machine-readable format. (2) A tripartite hybrid retrieval engine that operates in parallel, combining deep multi-path retrieval over a semantically indexed document corpus, real-time data acquisition through tool utilization, and an expert-curated memory bank for high-frequency questions, ensuring comprehensive scope, accuracy, and efficiency. (3) A two-stage training strategy for the document re-ranker, which initially constructs a general, domain-specific model using anonymized data, followed by rapid fine-tuning on company-specific data for targeted applications. By integrating our proposed designs, VeritasFi presents a novel framework that greatly enhances the adaptability and robustness of financial RAG systems, providing a scalable solution for both general-domain and company-specific QA tasks. Code accompanying this work is available at https://github.com/simplew4y/VeritasFi.git.

Leveraging large language models in real-world settings often entails a need to utilize domain-specific data and tools in order to follow the complex regulations that need to be followed for acceptable use. Within financial sectors, modern enterprises increasingly rely on Retrieval-Augmented Generation (RAG) systems to address complex information retrieval in financial document workflows. However, existing solutions struggle to account for the inherent heterogeneity of data (e.g., text, tables, diagrams) and evolving complexity in financial filings, leading to compromised accuracy in critical information extraction. We propose the FinSage framework as a solution, utilizing a multi-aspect RAG framework tailored for data retrieval and summarization in multi-modal financial documents. øurmodel introduces three innovative components: (1) a multi-modal pre-processing pipeline that unifies diverse data formats and generates chunk-level metadata summaries, (2) a multi-path sparse-dense retrieval system augmented with query expansion (HyDE) and metadata-aware semantic search, and (3) a domain-specialized re-ranking module fine-tuned via Direct Preference Optimization to prioritize ground-truth-related content. Extensive experiments demonstrate that FinSage achieves an impressive recall of 92.51% on 75 expert-curated questions derived from surpasses the best baseline method on the FinanceBench question answering datasets by 24.06% in accuracy. Moreover, FinSage has been successfully deployed as financial question-answering system in online meetings, where it has already served more than 1,200 people. The implementation is publicly available at https://github.com/simplew4y/finsage.

Cell rotation plays a crucial role in micromanipulation. Among manual cell rotation techniques, single-micropipette cell rotation is widely adopted due to its high efficiency and flexibility. However, there is currently no method capable of achieving automated single-micropipette cell rotation. In this study, we developed the first three-dimensional (3D) simulation system for single-micropipette cell rotation. Based on this simulation system, we successfully achieved single-micropipette cell rotation imitation learning (IL) for the first time. Specifically, we first analyze the forces acting on cells in the fluid, establishing a dynamic model that describes the cell's behavior in response to the flow velocity at the holding micropipette's orifice, the relative position of the micropipette, and time. We then developed the cell rotation simulation environment by discretizing the model and designing the simulation's cell and holding micropipette models based on real-world conditions. Finally, we designed a network architecture for IL using this model, achieving single-micropipette cell rotation in simulation. The results demonstrate that the simulation system exhibits a relative error range of 5.34% to 12.21% compared to real-world experiments, indicating a high degree of accuracy. Additionally, the single-micropipette cell rotation task achieved a success rate of 69% with an average completion time of 17.13 seconds, closely matching the expert data's average time of 17.69 seconds, confirming the feasibility of the simulation system.

Wavefront sensor-less adaptive optics (WFS-less AO) systems have garnered considerable interest in recent years due to their compact architecture and extensive applicability. However, most algorithms developed for adaptive optics systems predominately rely on conventional control methodologies, which often take an unexpectedly prolonged period to converge and severely hinder the feasibility of practical applications, especially in scenarios where rapid environmental fluctuations necessitate real-time control capabilities. Therefore, we propose an efficient closed-loop wavefront reconstruction method based on a novel forward prediction paradigm. The method leverages information from the current control objective, the wavefront phase, and the previous control signals (typically the voltages applied to deformable mirrors) to predict wavefront distortions and implement anticipatory control actions accordingly. Experimental results demonstrate that, compared to most conventional techniques, our method can reduce the post-control RMS wavefront aberration by nearly one-third while exhibiting robustness to different types of turbulence and showing promising potential for complex multi-layer turbulence scenarios.

We present a highly efficient method for interactive volumetric meshless shape deformation. Our method operates within a low dimensional sub-space of shape-aware C ∞ harmonic maps, and is the first method that is guaranteed to produce a smooth locally injective deformation in 3D. Unlike mesh-based methods in which local injectivity is enforced on tetrahedral elements, our method enforces injectivity on a sparse set of domain samples. The main difficulty is then to certify the map as locally injective throughout the entire domain. This is done by utilizing the Lipschitz continuity property of the harmonic basis functions. We show a surprising relation between the Lipschitz constant of the smallest singular value of the map Jacobian and the norm of the Hessian. We further carefully derive a Lipschitz constant for the Hessian, and develop a sufficient condition for the injectivity certification. This is done by utilizing the special structure of the harmonic basis functions combined with a novel regularization term that pushes the Lipschitz constants further down. As a result, the injectivity analysis can be performed on a relatively sparse set of samples. Combined with a parallel GPU-based implementation, our method can produce superior deformations with unique quality guarantees at real-time rates which were possible only in 2D so far.