CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari

Case Report: Primary hepatic neuroendocrine tumor: two cases report with literature review

Background & AimsPrimary hepatic neuroendocrine tumors (PHNETs) are rare malignant liver tumors that present diagnostic challenges owing to their rarity and absence of specific clinical features. This study aimed to investigate the characteristics of this rare liver tumor to enhance our understanding of the disease, improve diagnostic accuracy, and explore standardized diagnostic and treatment approaches.Case descriptionDuring physical examination, two elderly women, aged 64 and 74 years, were found to have liver masses. 18F-FDG Positron Emission Tomography-Computed Tomography (18F-FDG PET-CT) and Ga68-DOTATATE PET-CT scans of both individuals revealed multiple liver masses that were initially suspected to be hepatic neuroendocrine tumors. Subsequent puncture pathology confirmed the diagnosis of neuroendocrine tumors. Furthermore, in Case 1, the tumor was also detected by 18F-FDG PET-CT in the lung, suggesting a metastatic tumor, in conjunction with liver immunohistochemistry and imaging findings. Laboratory tests revealed no significant abnormalities in liver function or autoimmune liver disease indicators, and there was no evidence of viral hepatitis infection. However, partial hepatectomy was not indicated for cases with distant metastasis or multiple space-occupying lesions. Individualized treatment approaches have been developed for such situations. A large portion of the tumor underwent Transarterial Embolization (TAE), and targeted combination chemotherapy or endocrine therapy was administered based on the pathological results. During regular follow-ups a 13 and 12 months, the tumor remained stable. The patients’ quality of life was good, and their psychological well-being was healthy. They led active lifestyles, demonstrated a thorough understanding of their disease and its progression, and actively cooperated during the follow-up process.ConclusionOur findings suggest that a combination of serological, radiological, and immunohistochemical examinations can aid in the diagnosis of PHNET. In addition, we determined that TAE combined with drug therapy could be an effective method for controlling PHNET progression. Regular postoperative follow-ups are important for monitoring the prognosis and tumor progression status of patients with PHNET

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

A Non-Destructive Measurement of Trunk Moisture Content in Living Trees Based on Multi-Sensory Data Fusion

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions

Research on coal-rock recognition based on sound signal analysis

The recognition of the cutting state of shearer is the key technology to realize variable speed cutting and mining automation. It is of great significance for improving shearer reliability, ensuring personal safety and improving coal quality. This paper proposed a coal-rock recognition method based on sound signal analysis. The original sound signal produced during the cutting process of shearer is decomposed by variational mode decomposition (VMD), and the obtained IMFs can construct a signal matrix. The signal matrix is processed by singular value decomposition (SVD), and a series of singular values can be obtained and defined as the signal features. Finally, the coal-rock recognition is realized by extreme learning machine (ELM) based on the extracted signal features. The experiment results show that the overall recognition accuracy is 91.7% under the actual cutting condition, which verifies the effectiveness of the proposed method in coal-rock recognition, and lays a theoretical foundation for the automation and intellectualization of shearer mining

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis

Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions

The Identity Development of Onscreen Legendary Heroes: A Visual Affect Analysis of Mulan in Three Movie Adaptions

This study examines the visual emotive meanings involved in three films, that is, Mulan (1998), Mulan (2009), and Mulan (2020), which are adapted from The Ballad of Mulan . By adopting the theoretical framework of visual affect, this research conducts quantitative and qualitative analyses on the emotional episodes of Mulan in the three films. The study reveals that these films construct Mulan as a personality rich in emotions. However, there are distinctively different distributions of visual affect. In Mulan (1998), Mulan is built as a tomboy disrupting the masculine tradition, while in Mulan (2009) and Mulan (2020), Mulan is constructed as a filial and dutiful woman and an inherent legendary heroine respectively. The identity development revealed in this paper could be seen as a crucial way to transforming traditional figures in adapted films, contributing to the field of adaptation study from a semiotic perspective