Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Craters and nanostructures on BaF2 sample induced by a focused 46.9nm laser

We successfully damaged BaF2 samples by a 46.9nm capillary discharge laser of 100μJ focused by a toroidal mirror at a grazing incidence. Ablation craters with clear boundaries were detected by optical microscope and atomic force microscope (AFM). Laser-induced nanostructures with a period of ∼1μm were observed in the ablation area under single pulse irradiation and multiple pulses irradiation. The surface behavior was compared and analyzed with that induced by the laser of 50μJ. The nanostructures were supposed to be attributed to the thermoelastic effect and the period of the structures was effected by the energy of the laser

Focusing and Wavefront Splitting of an Extreme Ultraviolet Laser with a Tubular Optical Element

A capillary discharge extreme ultraviolet laser is focused and wavefront split at 46.9 nm by a tubular optical element. The reflectivity at 46.9 nm is both simulated and measured to be higher than 90% with a slight optical aberration. The operating principle of the tubular element for focusing and wavefront splitting is discussed. Dense and intense grating-like fringes with a period of ~150 nm are achieved. The method used in this work allows nano-scale processing with extreme ultraviolet laser at single-shot exposure mode

Bridge Crack Detection Based on SSENets

Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance

Characterizing the Grating-like Nanostructures Formed on BaF₂ Surfaces Exposed to Extreme Ultraviolet Laser Radiation

Monocrystalline barium fluoride (BaF2) slab targets were irradiated by focused 46.9-nm laser radiation at various fluence levels above the ablation threshold. Well-developed ablation patterns with sharp edges were studied by AFM (atomic force microscopy). Their inner surfaces were uniformly covered by periodic structures. The spatial period of the ripples depends on the laser fluence. When the sample is rotated by 45°, the orientation of the grating-like structure changes accordingly. Thus, the grating vector of the periodic structure seems to be coupled to the crystallographic planes of the single crystal. This means that the XUV-laser induced ripples reported here differ from LIPSS (laser-induced periodic surface structures) associated with interference phenomena occurring on illuminated surfaces. Therefore, other mechanisms are discussed to explain the formation of the periodic nanostructures reported in this article

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study

Abstract Background This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. Methods A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. Results A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. Conclusions This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs

The Alteration of Brain Interstitial Fluid Drainage with Myelination Development.

The integrity of myelination is crucial for maintaining brain interstitial fluid (ISF) drainage in adults; however, the mechanism of ISF drainage with immature myelin in the developing brain remains unknown. In the present study, the ISF drainage from the caudate nucleus (Cn) to the ipsilateral cortex was studied at different developmental stages of the rat brain (P 10, 20, 30, 40, 60, 80, 10-80). The results show that the traced ISF drained to the cortex from Cn and to the thalamus in an opposite direction before P30. From P40, we found impeded drainage to the thalamus due to myelin maturation. This altered drainage was accompanied by enhanced cognitive and social functions, which were consistent with those in the adult rats. A significant difference in diffusion parameters was also demonstrated between the extracellular space (ECS) before and after P30. The present study revealed the alteration of ISF drainage regulated by myelin at different stages during development, indicating that a regional ISF homeostasis may be essential for mature psychological and cognitive functions

The effect of image resolution on convolutional neural networks in breast ultrasound

Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. Materials and methods: During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. Results: The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. Conclusion: Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower