DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data

Graduate Admissions Recruitment Project

In this project, several comparison schools were interviewed and disclosed to have used search lists to find candidates. The organizations that have valuable search lists which may be of good use for the School of Professional Studies include Educational Testing Service (ETS) and the Graduate Management Admission Council (GMAC). By choosing criteria such as demographics, location, academic performance, educational history provided by search lists, we believe there are many quality candidates for SPS programs. However, as we further investigated the functionality and cost-efficiency or return of investment of GRE search list, we spotted many uncertainties and few solid and successful cases in our comparisons. Instead of focusing on one solution, our project expanded the research to social media scraping, EAB consulting, Alumni database, which all contain possibilities and valuable information for student recruitment in SPS

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202

Transcriptional control of Flt3 ligand targeted by fluorouracil-induced Egr-1 promoter in hematopoietic damage

<p>Abstract</p> <p>Background</p> <p>Ionizing radiation (IR) activate the early growth response-1 (Egr-1) promoter by production of radical oxygen intermediates (ROIs). Egr-EF, an expression vector pCIneo containing Egr-1 promoter cloned upstream of the cDNA for Flt3 ligand, was used to treat hematopoietic damage. 5-fluorouracil, a commonly used chemotherapeutic agent, cause tumor cell death by producing DNA damage and generating ROIs. We therefore hypothesized that clinically employed chemotherapeutic agents that increase ROIs could also be employed to activate Egr-EF in a chemoinducible gene therapy strategy. The goal of this study was to explore the effect of Flt3 Ligand gene transcription regulated by fluorouracil-induced Egr-1 promoter on hematopoietic recovery.</p> <p>Methods</p> <p>Human Flt3 Ligand (FL) cDNA and enhanced green fluorescent protein (EGFP) cDNA were linked together with IRES and inserted into the expression vector pCI-neo under control of the Egr-1 promoter (Egr-EF). The vector was transfected into the HFCL human bone marrow stromal cell line, and these cells were exposed to 5-FU, a chemotherapeutic drug. Expression of FL by HFCL/EF cells after 5-FU treatment was determined with ELISA, western blot and RT-PCR assays. In addition, the effect of FL from HFCL/EF cell culture supernatants on growth of CD34⁺cells from cord blood was also studied. HFCL/EF cells were injected into CB-17 combined immunodeficient (SCID) mice with B16 melanoma. 5-FU was given three days after injection of the HFCL/EF cells. In the recipient mice, white blood cell levels in peripheral blood and expression of EGFP and FL in human stromal cells were measured. Tumor volumes in tumor-bearing mice were also measured.</p> <p>Results</p> <p>5-FU treatment increased EGFP levels and secreted FL levels in HFCL/EF cells. Supernatants from HFCL/EF cell cultures treated with 5-FU increased CD34⁺cell growth significantly. HFCL/EF exhibited an increase in the number of white blood cells after chemotherapy.</p> <p>Conclusion</p> <p>The data presented here support the use of transcriptional control mediated by chemoinducible gene therapy to reduce hematopoietic injury associated with 5-FU.</p

Effects of x-ray irradiation on polycrystalline silicon, thin-film transistors

The effects of x-ray irradiation on the transfer and noise characteristics of excimer-laser-annealed polycrystalline silicon (poly-Si) thin-film transistors (TFTs) have been examined at dose levels up to 1000 Gy1000Gy. Parameters including mobility, threshold voltage, subthreshold swing, and leakage current, as well as flicker and thermal noise coefficients, were determined as a function of dose. In addition, the physical mechanisms of the observed changes in these parameters are analyzed in terms of radiation-generated charge in the gate oxide, at the Si–SiO2Si–SiO2 interface, and at the grain boundaries. The results of the studies indicate that poly-Si TFTs exhibit sufficient radiation tolerance for the use in active-matrix flat-panel imagers for most medical x-ray applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87455/2/064501_1.pd

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately, especially for large datasets, and thus the datasets inevitably contain noisy labels. This label-noise issue has been studied extensively for classification, while still remaining under-explored for edge detection. To address the label-noise issue for edge detection, this paper proposes to learn Pixel-level NoiseTransitions to model the label-corruption process. To achieve it, we develop a novel Pixel-wise Shift Learning (PSL) module to estimate the transition from clean to noisy labels as a displacement field. Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels. In addition, a local edge density regularization term is devised to exploit local structure information for better transition learning. This term encourages learning large shifts for the edges with complex local structures. Experiments on SBD and Cityscapes demonstrate the effectiveness of our method in relieving the impact of label noise. Codes are available at https://github.com/DREAMXFAR/PNT-Edge.Comment: Accepted by ACM-MM 202

Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.Comment: 9 pages, 5 figues. Accepted by IJCAI 202

Parameter Efficient Multi-task Model Fusion with Partial Linearization

Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven to be powerful and scalable to incorporate fine-tuned weights from different tasks into a multi-task model. However, efficiently fine-tuning large pre-trained models on multiple downstream tasks remains challenging, leading to inefficient multi-task model fusion. In this work, we propose a novel method to improve multi-task fusion for parameter-efficient fine-tuning techniques like LoRA fine-tuning. Specifically, our approach partially linearizes only the adapter modules and applies task arithmetic over the linearized adapters. This allows us to leverage the the advantages of model fusion over linearized fine-tuning, while still performing fine-tuning and inference efficiently. We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone. Experimental results demonstrate the capabilities of our proposed partial linearization technique to effectively construct unified multi-task models via the fusion of fine-tuned task vectors. We evaluate performance over an increasing number of tasks and find that our approach outperforms standard parameter-efficient fine-tuning techniques. The results highlight the benefits of partial linearization for scalable and efficient multi-task model fusion