DPSA: Dense pixelwise spatial attention network for hatching egg fertility detection

© 2020 SPIE and IS & T. Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our previous work has mainly investigated three factors of networks to push performance: depth, width, and cardinality. However, an important problem that feeble embryos with weak blood vessels interfering with the classification of resilient fertile ones remains. Inspired by fine-grained classification, we introduce the attention mechanism into our model by proposing a dense pixelwise spatial attention module combined with the existing channel attention through depthwise separable convolutions to further enhance the network class-discriminative ability. In our fused attention module, depthwise convolutions are used for channel-specific features learning, and dilated convolutions with different sampling rates are adopted to capture spatial multiscale context and preserve rich detail, which can maintain high resolution and increase receptive fields simultaneously. The attention mask with strong semantic information generated by aggregating outputs of the spatial pyramid dilated convolution is broadcasted to low-level features via elementwise multiplications, serving as a feature selector to emphasize informative features and suppress less useful ones. A series of experiments conducted on our hatching egg dataset show that our attention network achieves a lower misjudgment rate on weak embryos and a more stable accuracy, which is up to 98.3% and 99.1% on 5-day and 9-day old eggs, respectively

Biomechanical evaluation of three surgical scenarios of posterior lumbar interbody fusion by finite element analysis

BACKGROUND: For the treatment of low back pain, the following three scenarios of posterior lumbar interbody fusion (PLIF) were usually used, i.e., PLIF procedure with autogenous iliac bone (PAIB model), PLIF with cages made of PEEK (PCP model) or titanium (Ti) (PCT model) materiel. But the benefits or adverse effects among the three surgical scenarios were still not fully understood. METHOD: Finite element analysis (FEA), as an efficient tool for the analysis of lumbar diseases, was used to establish a three-dimensional nonlinear L1-S1 FE model (intact model) with the ligaments of solid elements. Then it was modified to simulate the three scenarios of PLIF. 10 Nm moments with 400 N preload were applied to the upper L1 vertebral body under the loading conditions of extension, flexion, lateral bending and torsion, respectively. RESULTS: Different mechanical parameters were calculated to evaluate the differences among the three surgical models. The lowest stresses on the bone grafts and the greatest stresses on endplate were found in the PCT model. The PCP model obtained considerable stresses on the bone grafts and less stresses on ligaments. But the changes of stresses on the adjacent discs and endplate were minimal in the PAIB model. CONCLUSIONS: The PCT model was inferior to the other two models. Both the PCP and PAIB models had their own relative merits. The findings provide theoretical basis for the choice of a suitable surgical scenario for different patients

Real-time Fatigue Driving Recognition System Based on Deep Learning and Embedded Platform

The frequent occurrence of automobile traffic accidents seriously threatens the safety of human life and property. Therefore, fatigue driving detection has important social value and research significance. In consideration of the market demand of intelligent assistant driving system, we design a real-time driver fatigue detection system based on deep learning and ARM platform, which uses Samsung 6818A53 series ARM as the driver fatigue real-time detection platform. In order to reduce the interference caused by the change of light and the occlusion of sunglasses in the actual driving environment, the driver's face image is captured by USB infrared camera. Firstly, face detection and alignment are carried out by multi-task cascaded convolutional neural network; Then the eye region is obtained according to geometric relationship between the feature points; Moreover, the driver's eye state is identified by Convolutional Neural Network (CNN); Finally, fatigue judgment is made based on PERCLOS criterion. The system has been tested in the experimental simulation environment and the actual driving environment. The experimental results show that detection speed of the system can reach more than 20 frames per second, which meets the requirement of real-time detection

Special Libraries, August 1933

Volume 24, Issue 7https://scholarworks.sjsu.edu/sla_sl_1933/1006/thumbnail.jp

PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter

The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The generator formulates the answer based on the documents retrieved by the retriever. Incorporating Large Language Models (LLMs) as generators is beneficial due to their advanced QA capabilities, but they are typically too large to be fine-tuned with budget constraints while some of them are only accessible via APIs. To tackle this issue and further improve ReQA performance, we propose a trainable Pluggable Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box. Positioned between the retriever and generator in a Pluggable manner, PRCA refines the retrieved information by operating in a token-autoregressive strategy via maximizing rewards of the reinforcement learning phase. Our experiments validate PRCA's effectiveness in enhancing ReQA performance on three datasets by up to 20% improvement to fit black-box LLMs into existing frameworks, demonstrating its considerable potential in the LLMs era.Comment: Accepted by the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. (EMNLP2023

Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning

Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. However, their performance can be hampered by inherent biases. Instead of traditionally sought methods that aim to minimize or correct these biases, this study introduces a novel methodology named ``bias-kNN''. This approach capitalizes on the biased outputs, harnessing them as primary features for kNN and supplementing with gold labels. Our comprehensive evaluations, spanning diverse domain text classification datasets and different GPT-2 model sizes, indicate the adaptability and efficacy of the ``bias-kNN'' method. Remarkably, this approach not only outperforms conventional in-context learning in few-shot scenarios but also demonstrates robustness across a spectrum of samples, templates and verbalizers. This study, therefore, presents a unique perspective on harnessing biases, transforming them into assets for enhanced model performance.Comment: Accepted by the 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024

Knockdown of Linc00515 Inhibits Multiple Myeloma Autophagy and Chemoresistance by Upregulating miR-140-5p and Downregulating ATG14

Background/Aims: The purpose of our experiments was to investigate the targeting relationship of linc00515, miR-140-5p and ATG14 and to explore the roles of linc00515, miR-140-5p and ATG14 in autophagy and chemoresistance of melphalan-resistant multiple myeloma cells. Methods: Plasmids that could interfere with the expression of linc00515 and ATG14 were loaded into myeloma cells, which were cultured with melphalan. MTT assay and flow cytometry analysis were utilized to investigate the effect of linc00515, miR-140-5p and ATG14 on the resistance of myeloma cells. QRT-PCR was used to determine the levels of mRNAs. Western blot was utilized to explore the level of ATG14 and autophagy-related proteins. Dual luciferase assay was utilized to explore the targeting relationship between linc00515, miR-140-5p and ATG14. GFP LC3 fluorescence assay was conducted to study the autophagy of cells. Results: The expression of linc00515 and ATG14 were significantly higher in melphalan-resistant myeloma cells. Knockdown of linc00515 and ATG14 led to decreased autophagy and chemoresistance of melphalan-resistant myeloma cells. The forced expression of miR-140-5p suppressed autophagy and chemoresistance of melphalan-resistant myeloma cells. Conclusion: Linc00515 enhanced autophagy and chemoresistance of melphalan-resistant myeloma by directly inhibiting miR-140-5p, which elevated ATG14 level

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its autonomous generation prowess. Through the adept application of IFD, cherry samples are pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on renowned datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of conventional data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the optimization of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/MingLiiii/Cherry_LL