Ego-perspective enhanced fitness training experience of AR Try to Move game

AR, a recent emerging technology, has been widely used in entertainment to provide users with immersive, interactive, and, sometimes, engaging experiences. The process of rehabilitation treatment and motor training process is often boring, and it is well known that users' exercise efficiency is often not as efficient as in a rehabilitation institution. Thus far, there is no effective upper limb sports rehabilitation training game based on the ego-perspective. Hence, with the objective of enhancing the enjoyment experience in rehabilitation and more effective remote rehabilitation training, this work aims to provide an AR Try to Move game and a convolutional neural network (CNN) for identifying and classifying user gestures from a self-collected AR multiple interactive gestures dataset. Utilizing an AR game scoring system, users are incentivized to enhance their upper limb muscle system through remote training with greater effectiveness and convenience.Comment: 6 pages, 2 figures, 2 tables, 2023 International Conference on Machine Learning and Automation (CONF-MLA 2023

Synthesis of well-defined catechol polymers for surface functionalization of magnetic nanoparticles

In order to obtain dual-modal fluorescent magnetic nanoparticles, well-defined fluorescent functional polymers with terminal catechol groups were synthesized by single electron transfer living radical polymerization (SET-LRP) under aqueous conditions for “grafting to” modification of iron oxide nanoparticles. Acrylamide, N-isopropylacrylamide, poly(ethylene glycol) methyl ether acrylate, 2-hydroxyethyl acrylate, glycomonomer and rhodamine B piperazine acrylamide were homo-polymerized or block-copolymerized directly from an unprotected dopamine-functionalized initiator in an ice-water bath. The Cu-LRP tolerated the presence of catechol groups leading to polymers with narrow molecular weight distributions (Mw/Mn < 1.2) and high or full conversion obtained in a few minutes. Subsequent immobilization of dopamine-terminal copolymers on an iron oxide surface were successful as demonstrated by Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), transition electron microscopy (TEM) and thermogravimetric analysis (TGA), generating stable polymer-coated fluorescent magnetic nanoparticles. The nanoparticles coated with hydrophilic polymers showed no significant cytotoxicity when compared with unmodified particles and the cellular-uptake of fluorescent nanoparticles by A549 cells was very efficient, which also indicated the potential application of these advanced nano materials for bio-imaging

Scheduling and control of systems subject to random faults

LAUREA MAGISTRALEIn questa tesi presentiamo l’applicazione di alcuni recenti risultati teorici sui sistemi lineari a commutazione Markoviana (MJLS) al controllo di sistemi soggetti a guasti. Precisamente, consideriamo un sistema di controllo in retroazione in cui il segnale di attuazione è intermittente a causa della presenza di guasti. Il modello dei guasti è descritto da una catena di Markov a tempo discreto, mentre le dinamiche dell’impianto e del controllore sono lineari. Come ingresso di controllo aggiuntivo si considera un segnale deterministico di schedulazione capace di commutare all’interno di un insieme di possibili controllori. In tal caso il modello diventa un sistema lineare a duplice commutazione (Dual Switching Linear System). Il principale problema trattato in questo lavoro è il progetto di opportune strategie di commutazione in retroazione capaci di assicurare la stabilità in media quadratica e di ottenere un livello garantito di prestazioni in termini di una funzione di costo quadratica. Il progetto è realizzato mediante il Matlab LMI-Toolbox formulando diverse ipotesi sullo schema di controllo (impianto singolo/doppio, controllore singolo/doppio) e sui parametri della sottostante catena di Markov. Sono state condotte varie simulazioni allo scopo di convalidare i risultati teorici, valutare il grado di conservativismo dei risultati di prestazione e confrontare diverse strategie per calcolare l’ingresso all’impianto quando l’attuatore è in condizioni di guasto.In this thesis we present an application of recent theoretical results regarding Markov Jump Linear Systems to the control of systems affected by random faults. More precisely, we consider a feedback control system where the actuation signal is intermittent, due to the occurrence of faults. The model of faults is described by a discrete-time Markov chain, while the dynamics of the plant and the controller is linear. As an additional control input, a deterministic scheduling signal is considered that can switch among a set of possible controllers. In that case, the model becomes a Dual Switching Linear System. The main problem addressed in this work is the design of suitable switching feedback strategies able to ensure mean-square stability and the attainment of some guaranteed level of performance in terms of a quadratic cost function. The design is carried out by using the Matlab LMI-Toolbox under different assumptions on the control scheme (single/multi plant, single/multi controller) and the parameters of the underlying Markov chain. Several simulations are carried out in order to validate the theoretical results, to assess the degree of conservatism of the results on the performance, and to compare different strategies for computing the input applied to the plant when the actuator is faulty (zero-input vs. input-hold)

An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction

DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

Document image restoration is a crucial aspect of Document AI systems, as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently, leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge, we propose DocRes, a generalist model that unifies five document image restoration tasks including dewarping, deshadowing, appearance enhancement, deblurring, and binarization. To instruct DocRes to perform various restoration tasks, we propose a novel visual prompt approach called Dynamic Task-Specific Prompt (DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features, which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution, DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover, DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://github.com/ZZZHANG-jx/DocResComment: Accepted by CVPR 202

Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202

UPOCR: Towards Unified Pixel-Level OCR Interface

In recent years, the optical character recognition (OCR) field has been proliferating with plentiful cutting-edge approaches for a wide spectrum of tasks. However, these approaches are task-specifically designed with divergent paradigms, architectures, and training strategies, which significantly increases the complexity of research and maintenance and hinders the fast deployment in applications. To this end, we propose UPOCR, a simple-yet-effective generalist model for Unified Pixel-level OCR interface. Specifically, the UPOCR unifies the paradigm of diverse OCR tasks as image-to-image transformation and the architecture as a vision Transformer (ViT)-based encoder-decoder. Learnable task prompts are introduced to push the general feature representations extracted by the encoder toward task-specific spaces, endowing the decoder with task awareness. Moreover, the model training is uniformly aimed at minimizing the discrepancy between the generated and ground-truth images regardless of the inhomogeneity among tasks. Experiments are conducted on three pixel-level OCR tasks including text removal, text segmentation, and tampered text detection. Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models. Code will be publicly available

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table structure recognition, and information extraction from visually-rich document. The evaluation reveals that GPT-4V performs well in recognizing and understanding Latin contents, but struggles with multilingual scenarios and complex tasks. Specifically, it showed limitations when dealing with non-Latin languages and complex tasks such as handwriting mathematical expression recognition, table structure recognition, and end-to-end semantic entity recognition and pair extraction from document image. Based on these observations, we affirm the necessity and continued research value of specialized OCR models. In general, despite its versatility in handling diverse OCR tasks, GPT-4V does not outperform existing state-of-the-art OCR models. How to fully utilize pre-trained general-purpose LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study offers a critical reference for future research in OCR with LMMs. Evaluation pipeline and results are available at https://github.com/SCUT-DLVCLab/GPT-4V_OCR

Biomechanical Study of 3 Osteoconductive Materials Applied in Pedicle Augmentation and Revision for Osteoporotic Vertebrae: Allograft Bone Particles, Calcium Phosphate Cement, Demineralized Bone Matrix

Objective This study assessed biomechanical properties of pedicle screws enhanced or revised with 3 materials. We aimed to compare the efficacy of these materials in pedicle augmentation and revision. Methods One hundred twenty human cadaveric vertebrae were utilized for in vitro testing. Vertebrae bone density was evaluated. Allograft bone particles (ABP), calcium phosphate cement (CPC), and demineralized bone matrix (DBM) were used to augment or revise pedicle screw. Post the implantation of pedicle screws, parameters such as insertional torque, pullout strength, cycles to failure and failure load were measured using specialized instruments. Results ABP, CPC, and DBM significantly enhanced biomechanical properties of the screws. CPC augmentation showed superior properties compared to ABP or DBM. ABP-augmented screws had higher cycles to failure and failure loads than DBM-augmented screws, with no difference in pullout strength. CPC-revised screws exhibited similar strength to the original screws, while ABP-revised screws showed comparable cycles to failure and failure loads but lower pullout strength. DBM-revised screws did not match the original screws’ strength. Conclusion ABP, CPC, and DBM effectively improve pedicle screw stability for pedicle augmentation. CPC demonstrated the highest efficacy, followed by ABP, while DBM was less effective. For pedicle revision, CPC is recommended as the primary choice, with ABP as an alternative. However, using DBM for pedicle revision is not recommended