Il “corridoio etnico”: Vicissitudini di una nozione centrale negli studi di antropologia linguistica della Cina

The paper is the transcript of a conversation between the two authors on the rise and development of scientific debate on the “Ethnic Corridor” within the Chinese anthropological tradition. It is supplemented by several references to the personal and academic life of the famed ethnolinguist Hongkai Sun, professor at the Institute of Ethnology and Anthropology of the Chinese Academy of Social Sciences (CASS) and initiator of the studies on the “Qiangic” minority languages of the Sino-Tibetan borderlands. Recorded on 17h February 2015 at the professor’s apartment in Beijing, the interview was carried out entirely in Chinese. The text has been transcribed, translated and edited by Tommaso Previato based on the original statements. It illustrates the feasibility and difficulties pertaining to the state-led program of ethnic categorization during the decades that followed the foundation of the People’s Republic of China (PRC), as well as the cultural pecularities of western China’s border societies. Particular attention is paid to the geographical distribution of the ethnic groups of the Tibetan-Burmese language family and their interactional patterns in various historical periods. The most recent development and practical applications of this emerging branch of studies are also briefly summarized before the closing statements.L’articolo si presenta in forma di una conversazione tra i due autori sulla nascita e lo sviluppo del dibattito scientifico sul “corridoio etnico” in seno alla tradizione antropologica cinese. È integrato da diversi riferimenti alla vita personale e accademica del noto etnolinguista Hongkai Sun, professore emerito dell’Istituto di Etnologia e Antropologia presso l’Accademia Cinese di Scienze Sociali (CASS) nonché massimo esponente nel campo delle lingue minoritarie Qiangic, parlate nelle regioni di frontiera sino-tibetane. Registrata il 17 febbraio 2015 a Pechino nell’appartamento del prof. Sun, l’intervista si è svolta interamente in cinese. Il testo è stato trascritto, tradotto, riadattato e curato da Tommaso Previato sulla base delle dichiarazioni originali. Illustra le difficoltà insite nel programma statale di categorizzazione etnica lanciato nei primi decenni che seguirono la fondazione della Repubblica Popolare Cinese (RPC), così come le peculiarità culturali delle società di confine nella Cina occidentale. Particolare attenzione è rivolta alla distribuzione geografica dei gruppi etnici della famiglia tibeto-birmana e alle dinamiche di interazione nelle varie epoche storiche. Le applicazioni più recenti di questo emergente ramo di studi vengono brevemente riassunte nella sezione conclusiva

On Language Endangerment and the Safeguarding of Intangible Cultural Heritage in China and the World

After summarizing the need to safeguard endangered languages in the global context and exploring the correlations between language diversity and cultural diversity, this paper examines related urgent issues in China and suggests that they are closely related to the lack of an explicit mentioning of language as a form of the most important form of cultural heritage in UNESCO’s Declaration Universal Declaration on Cultural Diversity or The Convention for the Safeguarding of Intangible Cultural Heritage. It also offers a list of proposals that will help preserve endangered languages and cultures, and improve the language situation as a whole, with passing a UNESCO convention for safeguarding endangered languages at the very top

Detecting phone-related pedestrian distracted behaviours via a two-branch convolutional neural network

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phonerelated distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phonerelated pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods

Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications

It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space where the regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition. In order to identify all sizes of objects precisely, we combine the features of the last three layers of the CNN to extract multi-scale features of the ROIs. The evaluation results on the KITTI dataset demonstrate that : (1) Unlike sliding windows that produce thousands of candidate object-region proposals, 3D LIDAR provides an average of 86 real candidates per frame and the minimal recall rate is higher than 95%, which greatly lowers the proposals extraction time; (2) The average processing time for each frame of the proposed method is only 66.79ms, which meets the real-time demand of autonomous vehicles; (3) The average identification accuracies of our method for car and pedestrian on the moderate level are 89.04% and 78.18% respectively, which outperform most previous methods

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.Comment: 6 pages, 4 figures, 2023ICM

Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original remote sensing image, could result in a collapse for the well-trained deep learning based SOD model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing SOD dataset (EORSSD) show the promising defense against adversarial cloud attacks

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHPComment: accepted by AAAI202

An overview of Old Tibetan synchronic phonology

Despite the importance of Old Tibetan in the Tibeto-Burman language family, little research has treated Old Tibetan synchronic phonology. This article gives a complete overview of the Old Tibetan phonemic system by associating sound values with the letters of the Tibetan alphabet and exploring the distribution of these sounds in syllable structure