IN-SITU MEASUREMENT OF EPITHELIAL TISSUE OPTICAL PROPERTIES: DEVELOPMENT AND IMPLEMENTATION OF DIFFUSE REFLECTANCE SPECTROSCOPY TECHNIQUES

Cancer is a severe threat to human health. Early detection is considered the best way to increase the chance for survival. While the traditional cancer detection method, biopsy, is invasive, noninvasive optical diagnostic techniques are revolutionizing the way that cancer is diagnosed. Reflectance spectroscopy is one of these optical spectroscopy techniques showing promise as a diagnostic tool for pre-cancer detection. When a neoplasia occurs in tissue, morphologic and biochemical changes happen in the tissue, which in turn results in the change of optical properties and reflectance spectroscopy. Therefore, a pre-cancer can be detected by extracting optical properties from reflectance spectroscopy. This dissertation described the construction of a fiberoptic based reflectance system and the development of a series of modeling studies. This research is aimed at establishing an improved understanding of the optical properties of mucosal tissues by analyzing reflectance signals at different wavelengths. The ultimate goal is to reveal the potential of reflectance-based optical diagnosis of pre-cancer. The research is detailed in Chapter 3 through Chapter 5. Although related with each other, each chapter was designed to become a journal paper ultimately. In Chapter 3, a multi-wavelength, fiberoptic system was constructed, evaluated and implemented to determine internal tissue optical properties at ultraviolet A and visible wavelengths. A condensed Monte Carlo model was deployed to simulate light-tissue interaction and generate spatially distributed reflectance data. These data were used to train an inverse neural network model to extract tissue optical properties from reflectance. Optical properties of porcine mucosal and liver tissues were finally measured. In Chapter 4, the condensed Monte Carlo method was extended so that it can rapidly simulate reflectance from a single illumination-detection fiber thus enabling the calculation of large data sets. The model was implemented to study spectral reflectance changes due to breast cancer. The effect of adding an illumination-detection fiber to a linear array fiber for optical property determination was also evaluated. In Chapter 5, an investigation of extracting the optical properties from two-layer tissues was performed. The relationship between spatially-resolved reflectance distributions and optical properties in two-layer tissue was investigated. Based on all the aforementioned studies, spatially resolved reflectance system coupled with condensed Monte Carlo and neural network models was found to be objective and appear to be sensitive and accurate in quantitatively assessing optical property change of mucosal tissues

Biomethanol Conversion from Sugar Beet Pulp with Pectin Methyl Esterase

Conversion of renewable biomethanol was studied from sugar beet pulp with pectin methyl esterases (PMEs). An analytical method for methanol and PME activity was developed based on potassium permanganate assay. Results showed that this method was sensitive, accurate and stable. Two PMEs, natural and mutated, were then produced and characterized. The kinetics parameters of Michaelis-Menten model and thermal deactivation model were determined. Based on the characterization results of PMEs, the methanol conversion from beet pulp was further studied and the effects of organisms, pH values, pulp size, and temperature were evaluated. After this, methods for separation and concentration of methanol from water, including striping, distillation and pervaporation, were studied and a model for the recovery of methanol from beet pulp after reaction was developed. Finally, the whole methanol conversion process was designed. The economic cost based on this design was estimated

Respiratory Rate Estimation from Face Videos

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework

COCO is "ALL'' You Need for Visual Instruction Fine-tuning

Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. Visual instruction fine-tuning (IFT) is a vital process for aligning MLLMs' output with user's intentions. High-quality and diversified instruction following data is the key to this fine-tuning process. Recent studies propose to construct visual IFT datasets through a multifaceted approach: transforming existing datasets with rule-based templates, employing GPT-4 for rewriting annotations, and utilizing GPT-4V for visual dataset pseudo-labeling. LLaVA-1.5 adopted similar approach and construct LLaVA-mix-665k, which is one of the simplest, most widely used, yet most effective IFT datasets today. Notably, when properly fine-tuned with this dataset, MLLMs can achieve state-of-the-art performance on several benchmarks. However, we noticed that models trained with this dataset often struggle to follow user instructions properly in multi-round dialog. In addition, tradition caption and VQA evaluation benchmarks, with their closed-form evaluation structure, are not fully equipped to assess the capabilities of modern open-ended generative MLLMs. This problem is not unique to the LLaVA-mix-665k dataset, but may be a potential issue in all IFT datasets constructed from image captioning or VQA sources, though the extent of this issue may vary. We argue that datasets with diverse and high-quality detailed instruction following annotations are essential and adequate for MLLMs IFT. In this work, we establish a new IFT dataset, with images sourced from the COCO dataset along with more diverse instructions. Our experiments show that when fine-tuned with out proposed dataset, MLLMs achieve better performance on open-ended evaluation benchmarks in both single-round and multi-round dialog setting

Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling

Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these computational challenges, a less-explored issue is designing an efficient and adaptable network backbone for iterative refinement. Current options like U-Net and Vision Transformer often rely on resource-intensive deep networks and lack the flexibility needed for generating images at variable resolutions or with a smaller network than used in training. This study introduces LEGO bricks, which seamlessly integrate Local-feature Enrichment and Global-content Orchestration. These bricks can be stacked to create a test-time reconfigurable diffusion backbone, allowing selective skipping of bricks to reduce sampling costs and generate higher-resolution images than the training data. LEGO bricks enrich local regions with an MLP and transform them using a Transformer block while maintaining a consistent full-resolution image across all bricks. Experimental results demonstrate that LEGO bricks enhance training efficiency, expedite convergence, and facilitate variable-resolution image generation while maintaining strong generative performance. Moreover, LEGO significantly reduces sampling time compared to other methods, establishing it as a valuable enhancement for diffusion models

A Benchmark Dataset for Understandable Medical Language Translation

In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions. The dataset contains 12,801 training samples, 1,015 validation samples, and 1,016 testing samples. We then evaluate one naive and six deep learning-based approaches on the MedLane dataset, including directly copying, a statistical machine translation approach Moses, four neural machine translation approaches (i.e., the proposed PMBERT-MT model, Seq2Seq and its two variants), and a modified text summarization model PointerNet. To compare the results, we utilize eleven metrics, including three new measures specifically designed for this task. Finally, we discuss the limitations of MedLane and baselines, and point out possible research directions for this task

Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large Language Models (LLMs), along with the emerging field of Multimodal Large Language Models (MLLMs), have demonstrated impressive capabilities across a wide range of multimodal tasks and applications. Particularly, various MLLMs, each with distinct model architectures, training data, and training stages, have been evaluated across a broad range of MLLM benchmarks. These studies have, to varying degrees, revealed different aspects of the current capabilities of MLLMs. However, the reasoning abilities of MLLMs have not been systematically investigated. In this survey, we comprehensively review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of MLLMs, introduce recent trends in applications of MLLMs on reasoning-intensive tasks, and finally discuss current practices and future directions. We believe our survey establishes a solid base and sheds light on this important topic, multimodal reasoning

Evaluation of acetic acid treatment of fresh–cut water chestnuts using gray–correlation analysis based on the variation–coefficient weight

IntroductionThe demand for fresh–cut water chestnuts, a convenient and nutritive vegetable, is increasing in market. However, the slicing of water chestnuts can cause mechanical damage to tissue, which results in quality deterioration. We aimed to select the optimal treatment through a comprehensive comparison of the preservation effect of acetic acid, which could prolong the shelf life of fresh–cut water chestnuts and improve their storage quality.MethodsA comprehensive evaluation was conducted using the gray–correlation method based on the variation–coefficient weight to observe the treatment of 0, 2 and 5% acetic acid. Their effects on color, weight loss rate, and the content of ascorbic acid, total sugar, reducing sugar, soluble protein, and free amino acid were determined.ResultsThe color, weight loss rate, and nutritional content of fresh–cut chestnuts varied under different processing and storage times. When stored for more than 4 days, the b* value, and the content of total sugar and soluble protein in CK were higher than those in 2% or 5% acetic acid, but the weight loss rate, and the content of ascorbic acid and free amino acid in CK were less than those in acetic acid treatments. Considering various indicators, it was difficult to determine which treatment to choose for fresh–cut water chestnut preservation. The gray–correlation analysis results indicated that when stored for 8, 12, or 16 days, the gray–correlation degree of 5% acetic acid was the highest, while that of the control was the lowest. It could be directly concluded by the gray–correlation degree that when the storage time exceeded 4 days, acetic acid could be used to improve storage quality, and 5% acetic acid had a better preservation effect than 2%. Fresh–cut water chestnuts can be stored for 4 days without the need for acetic acid treatment.ConclusionThese findings could provide information and comprehensive evaluation methods for the preservation of fresh–cut fruits and vegetables. The next step is to evaluate the preservation effect of acetic acid by measuring its effects on other indicators of fresh–cut water chestnuts (e.g., flavonoids, and microorganisms), providing ideas for the research of preservatives