Virtual Reality Simulator for Training in Myringotomy with Tube Placement

Myringotomy refers to a surgical incision in the eardrum, and it is often followed by ventilation tube placement to treat middle-ear infections. The procedure is difficult to learn; hence, the objectives of this work were to develop a virtual-reality training simulator, assess its face and content validity, and implement quantitative performance metrics and assess construct validity. A commercial digital gaming engine (Unity3D) was used to implement the simulator with support for 3D visualization of digital ear models and support for major surgical tasks. A haptic arm co-located with the stereo scene was used to manipulate virtual surgical tools and to provide force feedback. A questionnaire was developed with 14 face validity questions focusing on realism and 6 content validity questions focusing on training potential. Twelve participants from the Department of Otolaryngology were recruited for the study. Responses to 12 of the 14 face validity questions were positive. One concern was with contact modeling related to tube insertion into the eardrum, and the second was with movement of the blade and forceps. The former could be resolved by using a higher resolution digital model for the eardrum to improve contact localization. The latter could be resolved by using a higher fidelity haptic device. With regard to content validity, 64% of the responses were positive, 21% were neutral, and 15% were negative. In the final phase of this work, automated performance metrics were programmed and a construct validity study was conducted with 11 participants: 4 senior Otolaryngology consultants and 7 junior Otolaryngology residents. Each participant performed 10 procedures on the simulator and metrics were automatically collected. Senior Otolaryngologists took significantly less time to completion compared to junior residents. Junior residents had 2.8 times more errors as compared to experienced surgeons. The senior surgeons also had significantly longer incision lengths, more accurate incision angles, and lower magnification keeping both the umbo and annulus in view. All metrics were able to discriminate senior Otolaryngologists from junior residents with a significance of p \u3c 0.002. The simulator has sufficient realism, training potential and performance discrimination ability to warrant a more resource intensive skills transference study

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

Traffic state prediction in a transportation network is paramount for effective traffic operations and management, as well as informed user and system-level decision-making. However, long-term traffic prediction (beyond 30 minutes into the future) remains challenging in current research. In this work, we integrate the spatio-temporal dependencies in the transportation network from network modeling, together with the graph convolutional network (GCN) and graph attention network (GAT). To further tackle the dramatic computation and memory cost caused by the giant model size (i.e., number of weights) caused by multiple cascaded layers, we propose sparse training to mitigate the training cost, while preserving the prediction accuracy. It is a process of training using a fixed number of nonzero weights in each layer in each iteration. We consider the problem of long-term traffic speed forecasting for a real large-scale transportation network data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). Experimental results show that the proposed GCN-STGT and GAT-STGT models achieve low prediction errors on short-, mid- and long-term prediction horizons, of 15, 30 and 45 minutes in duration, respectively. Using our sparse training, we could train from scratch with high sparsity (e.g., up to 90%), equivalent to 10 times floating point operations per second (FLOPs) reduction on computational cost using the same epochs as dense training, and arrive at a model with very small accuracy loss compared with the original dense trainin

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, we consider the dynamic sparse training as a sparse connectivity search problem and design an exploitation and exploration acquisition function to escape from local optima and saddle points. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98\% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracy improvement compared to SOTA sparse training methods

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89\% of the LTH training cost on ResNet-19 and 31.35\% of the LTH training cost on VGG-16 on CIFAR-10

PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems. Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration. However, they either achieve a low reduction ratio and suffer from high latency due to limited computation and communication saving, or are power-hungry as existing works mainly focus on general computing platforms such as CPUs and GPUs. In this work, as the first attempt, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration, by integrating hardware latency of the cryptographic building block into the DNN loss function to achieve high energy efficiency, accuracy, and security guarantee. Instead of heuristically checking the model sensitivity after a DNN is well-trained (through deleting or dropping some non-polynomial operators), our key design principle is to em enforce exactly what is assumed in the DNN design -- training a DNN that is both hardware efficient and secure, while escaping the local minima and saddle points and maintaining high accuracy. More specifically, we propose a straight through polynomial activation initialization method for cryptographic hardware friendly trainable polynomial activation function to replace the expensive 2P-ReLU operator. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) platform

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 times ReLU budget reduction.Comment: ICCV 2023 accepeted publicatio

Genes of ACYL CARRIER PROTEIN Family Show Different Expression Profiles and Overexpression of ACYL CARRIER PROTEIN 5 Modulates Fatty Acid Composition and Enhances Salt Stress Tolerance in Arabidopsis

Acyl carrier proteins (ACPs) are a group of small acidic proteins functioning as important cofactors in the de novo synthesis of fatty acids. In Arabidopsis, ACPs are encoded by a small gene family comprising five plastid members, AtACP1 to AtACP5, and three mitochondrial members. The biological functions and the transcriptional responses to abiotic stresses of most AtACPs have yet to be elucidated. The present study extends previous findings and provides new knowledge on the function of ACPs by examining the responses of AtACP-encoding genes to several abiotic stresses and, in particular, the role of AtACP5 in the adaptation to salt stress. Phylogenetic analysis showed that AtACP1, AtACP2, AtACP3, and AtACP5 can be classified into one group and separated from a group comprising AtACP4 and ACP homologs from related species. Quantitative RT-PCR analysis revealed that the expression of AtACP1, AtACP2, and AtACP3 was induced by drought. Both iron deficiency and nitrogen starvation resulted in down-regulation of AtACP4. The most pronounced response was observed for AtACP5, the expression of which was dramatically decreased by salt stress. Knock-out of AtACP5 showed increased sensitivity to NaCl stress, whereas transgenic lines overexpressing AtACP5 displayed increased salt tolerance relative to the wild-type. Overexpression of AtACP5 further led to an altered composition of fatty acids, mainly a decrease of oleic acid (C18:1) and an increase of palmitic acid (C16:0), and to a lower Na+/K+ ratio when compared to the salt stressed wild-type. The comprehensive transcriptional information on the small plastid AtACP gene family in response to various abiotic stresses and the further investigation of the AtACP5 indicate that AtACP5 might be critical for salt tolerance through alterations of the composition of fatty acids and, subsequently, the Na+/K+ ratio

Ethnobotanical study on ritual plants used by Hani people in Yunnan, China

Abstract Background The Hani people, who reside in Yuanyang County, Honghe Hani and Yi Autonomous Prefecture, Yunnan Province, rely on rice terrace farming as their primary livelihood. They utilize plants in various traditional ritual practices. The Hani people have categorized the value of plants based on their natural attributes and have refined the ways of using different plants in specific rituals through practical observations and experiences derived from their agricultural culture. Although the plants used in these rituals hold significant cultural value, they have yet to be studied from the perspective of ethnobotany. This study aims to approach the ritual plants using ethnobotanical methods. Methods Ethnobotanical fieldwork was conducted in 10 villages in Yuanyang County between 2021 and 2023. Data were collected from the local Hani people through semi-structured interviews and participatory observations and 41 informants were interviewed during the field investigations. The frequency of citation (FC) and relative frequency of citation (RFC) were utilized to evaluate the relative importance of ritual plants among the local communities. Results A total of 36 plant species, belonging to 18 families and 34 genera, were recorded as being used in 11 ritual practices by the Hani people. Rosaceae, Poaceae, and Fabaceae were found to have the highest number of species. Most of the ritual plants used by the Hani people were collected from the wild. FC and RFC analysis showed that the preferred plants for Hani rituals were Rhus chinensis Mill, Oryza sativa L., Phyllostachys sulphurea (Carr.) A. et C. Riv. and Musa basjoo Siebold & Zucc. ex Iinuma. The 11 rituals are all centered around the performance of people, crops and livestock. The Hani people use plants in different rituals mainly based on their biological attributes. Conclusions Many rituals of the Hani people are closely related to their production and livelihood, and the plants used in these rituals are deeply rooted in Hani’s traditional ecological knowledge and beliefs. The Hani people’s reverence for nature, respect for life, gratitude towards ancestors, and seeking blessings and disaster prevention for their families, crops, and livestock are all reflected in these rituals and their utilization of ritual plants. The Hani people showcase their agricultural culture in the Honghe Hani Rice Terraces through plant-based ritual performances. Studying ritual plants in the core area of the Hani Rice Terraces is of great significance for protecting the Hani Terrace farming culture. In the future, it is essential to pay more attention to the role of traditional knowledge in biodiversity conservation

In Vitro Corrosion and Cytocompatibility of ZK60 Magnesium Alloy Coated with Hydroxyapatite by a Simple Chemical Conversion Process for Orthopedic Applications

Magnesium and its alloys—a new class of degradable metallic biomaterials—are being increasingly investigated as a promising alternative for medical implant and device applications due to their advantageous mechanical and biological properties. However, the high corrosion rate in physiological environments prevents the clinical application of Mg-based materials. Therefore, the objective of this study was to develop a hydroxyapatite (HA) coating on ZK60 magnesium alloy substrates to mediate the rapid degradation of Mg while improving its cytocompatibility for orthopedic applications. A simple chemical conversion process was applied to prepare HA coating on ZK60 magnesium alloy. Surface morphology, elemental compositions, and crystal structures were characterized using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction, respectively. The corrosion properties of samples were investigated by immersion test and electrochemical test. Murine fibroblast L-929 cells were harvested and cultured with coated and non-coated ZK60 samples to determine cytocompatibility. The degradation results suggested that the HA coatings decreased the degradation of ZK60 alloy. No significant deterioration in compression strength was observed for all the uncoated and coated samples after 2 and 4 weeks’ immersion in simulated body fluid (SBF). Cytotoxicity test indicated that the coatings, especially HA coating, improved cytocompatibility of ZK60 alloy for L929 cells

Ethnobotanical study of medicinal plants used by the Yi people in Mile, Yunnan, China

Abstract Background The Yi people are a sociolinguistic group living in Mile City, which is their typical settlement in southeastern Yunnan, China. Over the long history of using medicinal plants, the Yi people have accumulated and developed a wealth of traditional medicinal knowledge, which has played a vital role in their health care. However, only a few studies have been performed to systematically document the medicinal plants commonly used by the Yi people. This study provides fundamental data for the development and application of ethnomedicine as well as supports the conservation of the traditional medical knowledge of the Yi people. Methods This study was conducted from May 2020 to August 2022 and involved five townships in Mile. Information regarding medicinal plants was obtained through semistructured interviews, key informant interviews, and participatory observation. The collected voucher specimens were identified using the botanical taxonomy method and deposited in the herbarium. Ethnobotanical data were analyzed using informant consensus factor, relative frequency of citation, and fidelity level. Results In total, 114 informants distributed in five townships of Mile were interviewed. The Yi people used 267 medicinal plant species belonging to 232 genera and 104 families to treat various diseases. Asteraceae, Lamiaceae, and Fabaceae were the most commonly used plant families by the Yi people. In addition, herbs were most commonly used by the Yi people. Whole plants and roots were the preferred medicinal parts. Decoctions were the most common method of herbal medicine preparation. There are 49 different recorded diseases treated by Yi medicinal plants, and among them, respiratory diseases, rheumatism, traumatic injury, fractures, and digestive system diseases have the largest number of species used. A quantitative analysis demonstrated that plants such as Zingiber officinale, Lycopodium japonicum, Aconitum carmichaelii, Panax notoginseng, Cyathula officinalis, and Leonurus japonicus played crucial roles in disease prevention and treatment. Conclusion Traditional knowledge of medicinal plants is closely associated with the social culture of the local Yi people. The medicinal plants used for health care in the study area were diverse. Local healers were skilled at using medicinal plants to treat various diseases. Their treatment methods were convenient and unique, exhibiting distinctive regional characteristics. However, the inheritance of their traditional medicinal knowledge and protection of wild medicinal plant resources are facing serious challenges, including the decreasing number of local healers, aging of healers, lack of successors, and excessive harvesting of medicinal plant resources. This ethnobotanical survey provides a useful reference for the sustainable utilization and protection of medicinal plant resources in Mile and the inheritance of traditional medicinal knowledge of the Yi people