The Protection, Designation and Management of Cultural Routes: A Case Study of the Tea & Horse Road in China

Cultural routes are a relatively new, and much discussed concept in heritage designation and management. The extent to which this concept provides an effective theoretical framework for management of diverse sites, monuments, and landscapes, encompassing multiple stakeholders and values, is under debate. The research explores the so-called Tea & Horse Road (THR), which stretched from southwestern China to the South Asian subcontinent. It is an intriguing example of a historic network of interactions, combining multidimensional issues of protection, designation, and management, within a challenging contemporary social and political context. Using literature reviews, case studies, semi-structured interviews, and field investigations, the thesis focuses on the THR within Yunnan Province in China. The selected case study was divided into three categories: productive regions, transfer regions and consuming regions, in order to both articulate the assorted THR heritage, and to explore relevant crucial issues: the nature of the physical remains; their integrity and authenticity; the potential and impacts of tourism; local, regional and state-based values; and the prospective management, protection and designation of these areas. The research concludes that introducing the concept of cultural routes enables these multifaceted sites and landscapes to be integrated within a wider systematic framework, which offers possible approaches to top-down preservation and management of the THR. However, the research also reveals the tensions between cultural route and cultural landscape approaches, with the latter far easier to implement at a local/regional level. More broadly, it also raises questions about the implementation of cultural routes as a nomination strategy when dealing with diverse heritage resources, landscapes and communities

A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions

High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via sub-networks and then recover high-resolution representations. This will lose spatial information, and errors introduced by the decoder will be more serious when multiple types of objects are considered or objects are far away from the camera. To address these issues, we revisit the design paradigm of CNN for robotic perception tasks. We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks. In particular, guidelines of neural network design are provided for robotic perception tasks, e.g., high-resolution representation and lightweight design, which respond to the challenges in different manipulation scenarios. We then develop a novel grasping visual architecture referred to as HRG-Net, a parallel-branch structure that always maintains a high-resolution representation and repeatedly exchanges information across resolutions. Extensive experiments validate that these two designs can effectively enhance the accuracy of visual-based grasping and accelerate network training. We show a series of comparative experiments in real physical environments at Youtube: https://youtu.be/Jhlsp-xzHFY

Lightweight Neural Path Planning

Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical challenge, we develop a lightweight neural path planning architecture with a dual input network and a hybrid sampler for resource-constrained robotic systems. Our architecture is designed with efficient task feature extraction and fusion modules to translate the given planning instance into a guidance map. The hybrid sampler is then applied to restrict the planning within the prospective regions indicated by the guide map. To enable the network training, we further construct a publicly available dataset with various successful planning instances. Numerical simulations and physical experiments demonstrate that, compared with baseline approaches, our approach has nearly an order of magnitude fewer model size and five times lower computational while achieving promising performance. Besides, our approach can also accelerate the planning convergence process with fewer planning iterations compared to sample-based methods.Comment: 8 page

TODE-Trans: Transparent Object Depth Estimation with Transformer

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction and reflection of light. In this work, we present a transformer-based transparent object depth estimation approach from a single RGB-D input. We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas. In addition, to better enhance the fine-grained features, a feature fusion module (FFM) is designed to assist coherent prediction. Our empirical evidence demonstrates that our model delivers significant improvements in recent popular datasets, e.g., 25% gain on RMSE and 21% gain on REL compared to previous state-of-the-art convolutional-based counterparts in ClearGrasp dataset. Extensive results show that our transformer-based model enables better aggregation of the object's RGB and inaccurate depth information to obtain a better depth representation. Our code and the pre-trained model will be available at https://github.com/yuchendoudou/TODE.Comment: Submitted to ICRA202

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually focus on reducing the number of trainable parameters in the network. While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation. Notably, neural networks are usually trained using stochastic gradient descent. We argue that in stochastic optimization, models can handle noisy gradients as long as the gradient estimator is unbiased with reasonable variance. Following this motivation, we propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance, which only requires storing the sub-sampled activations for calculating the gradient. Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones. By replacing the linear operation with our approximated one in transformers, we can achieve up to 2.7$\times$ peak memory reduction with almost no accuracy drop and enables up to $6.4\times$ larger batch size. Under the same hardware, WTA-CRS enables better down-streaming task performance by applying larger models and/or faster training speed with larger batch sizes

3D-Printed Artificial Microfish

Hydrogel microfish featuring biomimetic structures, locomotive capabilities, and functionalized nanoparticles are engineered using a rapid 3D printing platform: microscale continuous ­optical printing (μCOP). The 3D-printed ­microfish exhibit chemically powered and magnetically guided propulsion, as well as highly efficient detoxification capabilities that highlight the technical versatility of this platform for engineering advanced functional microswimmers for diverse biomedical applications

A new approach for obtaining rapid uniformity in rice (Oryza sativa L.) via a 3x x 2x cross

A triploid (2n = 3x = 36) rice plant was obtained by screening a twin seedling population in which each seed germinated to two or three sprouts that were then crossed with diploid plants. One diploid plant was chosen among the various F1 progenies and developed into an F 2 population via self-pollination. Compared with the control variety Shanyou 63, this F 2 population had a stable agronomical performance in field trials, as confirmed by the F-test. The stability of the F 2 population was further substantiated by molecular analysis with simple sequence repeat markers. Specifically, of 160 markers assayed, 37 (covering all 12 chromosomes) were polymorphic between the parental lines. Testing the F 1 hybrid individually with these markers showed that each PCR product had only a single band instead of two bands from each parent. The bands were identical to either maternal (23 markers) or paternal (eight markers) bands or distinct from both parents (six markers). The amplified bands of all 60 randomly selected F 2 plants were uniform and identical to those of the F 1 hybrid. These results suggest that the F 1 plant is a non-segregating hybrid and that a stable F 2 population was obtained. This novel system provides an efficient means for shortening the cycle of hybrid rice seed production

Direct Bypass Surgery Vs. Combined Bypass Surgery for Hemorrhagic Moyamoya Disease: A Comparison of Angiographic Outcomes

Objective: Extracranial-intracranial bypass is currently recognized as the optimal treatment for hemorrhagic-type moyamoya disease (MMD) which reduces incidence of rebleeding. Recent studies have reported the advantage of combined bypass over direct bypass for the general MMD patients. However, the effect of direct bypass and combined bypass surgery specifically for hemorrhagic-type MMD had not been investigated yet.Methods: Hemorrhagic-type MMD patients who underwent direct and combined bypass surgery with complete clinical and radiological documentation from a multicenter cohort between 2009 and 2017 were retrospectively included. Surgical methods included superficial temporal artery-middle cerebral artery (STA-MCA) anastomosis (direct bypass), combined STA-MCA bypass with encephalodurosynangiosis (EDS), and combined STA-MCA bypass with encephaloduroarteriosynangiosis (EDAS). Matsushima standard on follow-up catheter angiography was used to assess surgical outcome. Modified Rankin Scale, incidence of rebleeding and ischemia during follow-up were recorded. Rebleeding-free survival rates between direct and combined bypass were compared by Kaplan-Meier analysis.Results: Sixty eight hemorrhagic-onset MMD patients were included in this study, among which 71 hemispheres were treated with surgery (direct bypass: 17; bypass+EDS: 24; bypass+EDAS: 30). Forty six (64.8%) hemispheres had satisfactory revascularization (Matsushima level 2–3) and 26 (36.6%) had poor neoangiogenesis. Matsushima level was not significantly different between surgical groups (P = 0.258). Good neoangiogenesis from dural grafts was achieved in 26 (36.6%) hemispheres, and good neoangiogenesis from STA grafts was only seen in 4 (out of 30, 12.5%) hemispheres. Multivariate analysis showed bypass patency [P < 0.001, OR (95%CI): 13.41 (3.28–54.80)] and dural neoangiogenesis [P < 0.001, OR (95%CI): 13.18 (3.26–53.36)] both independently contributed to good angiographic outcome. During follow-up, incidences of rebleeding or ischemic events, and re-bleeding free survival rate were not significantly different between surgical groups (P = 0.433, P = 0.559, and P = 0.997). However, patients who underwent combined bypass surgery had significantly lower mRS at follow-up comparing to patients who underwent direct bypass (P = 0.006).Conclusion: Combined bypass surgery and direct bypass surgery offered similar revascularization for hemorrhagic MMD. Bypass patency and dural angiogenesis both contributed to revascularization independently. The potential of indirect bypass to grow new vessels in hemorrhagic-MMD patients was generally limited, but dural leaflets offered better neoangiogenesis than STA grafts and was therefore recommended for surgical revascularization of hemorrhagic MMD

The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria

Objective: Obesity-related disease risks may vary depending on whether the subject has metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO). At least 5 definitions/criteria of obesity and metabolic disorders have been documented in the literature, yielding uncertainties in a reliable international comparison of obesity phenotype prevalence. This report aims to compare differences in MHO and MUO prevalence according to the 5 most frequently used definitions. Methods: A random sample of 4,757 adults aged 35 years and older (male 51.1%) was enrolled. Obesity was defined either according to body mass index or waist circumference, and the definitions of metabolic abnormalities were derived from 5 different criteria. Results: In MHO, the highest prevalence was obtained when using the homeostasis model assessment (HOMA) criteria (13.6%), followed by the Chinese Diabetes Society (11.4%), Adult Treatment Panel III (10.3%), Wildman (5.2%), and Karelis (4.2%) criteria; however, the MUO prevalence had an opposite trend to MHO prevalence. The magnitude of differences in the age-specific prevalence of MHO and MUO varied greatly and ranked in different orders. The proportion of insulin resistance for MHO and MUO individuals differed significantly regardless of which metabolic criterion was used. Conclusion: The prevalence of MHO and MUO in the Chinese population varies according to different definitions of obesity and metabolic disorders