Transverse momentum spectra of f0(980)f_0(980) from coalescence model

We use a coalescence model to generate $f_{0}$(980) particles for four configurations: ${s\bar{s}}$ meson, ${u\bar{u}s\bar{s}}$ tetraquark, ${K^{+}K^{-}}$ molecule and $u\bar{u}$ p-wave state. The phase-space information of the coalescing constituents is taken from a multi-phase transport (AMPT) simulation of proton-proton and proton-lead collisions at the LHC. It is shown that the transverse momentum spectra and production yields of $f_0(980)$ differ significantly among the configurations. It is suggested that the $p_T$ spectra of the $f_0(980)$ compared to those of other hadrons (such as pion) and the ratio of the $f_0(980)$ $p_T$ spectra in pPb over pp can be exploited to tell the configuration of the $f_0(980)$

A Novel Wide-Area Multiobject Detection System with High-Probability Region Searching

In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficient object searching, and accurate localization. To address these challenges, this paper presents a hybrid system that incorporates a wide-angle camera, a high-speed search camera, and a galvano-mirror. In this system, the wide-angle camera offers panoramic images as prior information, which helps the search camera capture detailed images of the targeted objects. This integrated approach enhances the overall efficiency and effectiveness of wide-area visual detection systems. Specifically, in this study, we introduce a wide-angle camera-based method to generate a panoramic probability map (PPM) for estimating high-probability regions of target object presence. Then, we propose a probability searching module that uses the PPM-generated prior information to dynamically adjust the sampling range and refine target coordinates based on uncertainty variance computed by the object detector. Finally, the integration of PPM and the probability searching module yields an efficient hybrid vision system capable of achieving 120 fps multi-object search and detection. Extensive experiments are conducted to verify the system's effectiveness and robustness.Comment: Accepted by ICRA 202

EventDrop: data augmentation for event-based learning

The advantages of event-sensing over conventional sensors (e.g., higher dynamic range, lower time latency, and lower power consumption) have spurred research into machine learning for event data. Unsurprisingly, deep learning has emerged as a competitive methodology for learning with event sensors; in typical setups, discrete and asynchronous events are first converted into frame-like tensors on which standard deep networks can be applied. However, over-fitting remains a challenge, particularly since event datasets remain small relative to conventional datasets (e.g., ImageNet). In this paper, we introduce EventDrop, a new method for augmenting asynchronous event data to improve the generalization of deep models. By dropping events selected with various strategies, we are able to increase the diversity of training data (e.g., to simulate various levels of occlusion). From a practical perspective, EventDrop is simple to implement and computationally low-cost. Experiments on two event datasets (N-Caltech101 and N-Cars) demonstrate that EventDrop can significantly improve the generalization performance across a variety of deep networks.Comment: IJCAI 202

MoMa-Pos: Where Should Mobile Manipulators Stand in Cluttered Environment Before Task Execution?

Mobile manipulators always need to determine feasible base positions prior to carrying out navigation-manipulation tasks. Real-world environments are often cluttered with various furniture, obstacles, and dozens of other objects. Efficiently computing base positions poses a challenge. In this work, we introduce a framework named MoMa-Pos to address this issue. MoMa-Pos first learns to predict a small set of objects that, taken together, would be sufficient for finding base positions using a graph embedding architecture. MoMa-Pos then calculates standing positions by considering furniture structures, robot models, and obstacles comprehensively. We have extensively evaluated the proposed MoMa-Pos across different settings (e.g., environment and algorithm parameters) and with various mobile manipulators. Our empirical results show that MoMa-Pos demonstrates remarkable effectiveness and efficiency in its performance, surpassing the methods in the literature. %, but also is adaptable to cluttered environments and different robot models. Supplementary material can be found at \url{https://yding25.com/MoMa-Pos}.Comment: Submitted to IROS 202

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Optimization of preparation techniques for high-temperature resistant waterborne phenolic-epoxy resin emulsion under low carbon background

In light of escalating global climate change concerns and the pressing need to address industries with high carbon emissions and pollution, enhancing the preparation of phenol-formaldehyde epoxy resins has emerged as a critical research focus. This study seeks to fabricate waterborne phenol-formaldehyde epoxy resins with superior performance by investigating pivotal factors influencing their properties and refining preparation methods. Utilizing tetrabutylammonium bromide as a phase transfer catalyst, the phenol-formaldehyde epoxy resins are synthesized via a two-step alkalization process. Subsequent etherification reactions involve modifying the phenol-formaldehyde epoxy resins using cationic modifier diethanolamine (DEA) and anionic modifier sodium p-amino benzenesulfonate, resulting in waterborne phenol-formaldehyde epoxy resins. Subsequently, in situ synthesis is employed to produce nanoscale silica (SiO2) modified waterborne phenol-formaldehyde epoxy resins. The findings reveal that when the ratio of n1 to n2 falls within the range of 1/3.25 to 1/3, the emulsion displays a moderate particle size and maintains stable storage. Furthermore, an increase in DEA dosage leads to a particle size of less than 324 nm when the ratio of n1 to n2 exceeds 1/3, indicating stability. Moreover, optimal stability and prolonged storage lifespan are achieved when the nano SiO2 content is approximately 1.5%. This study contributes by synthesizing high-quality waterborne phenol-formaldehyde epoxy resin emulsions through optimized methods. The research findings offer a theoretical foundation for this domain and support the practical application of low-carbon and environmentally friendly concepts in the coatings industry