Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

LiDAR-Based Place Recognition For Autonomous Driving: A Survey

LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition, exploring existing challenges, and describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition and for researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table

Low-Rank Modular Reinforcement Learning via Muscle Synergy

Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. Previous work on modular RL has proven its ability to control morphologically different agents with a shared actuator policy. However, with the increase in the Degree of Freedom (DoF) of robots, training a morphology-generalizable modular controller becomes exponentially difficult. Motivated by the way the human central nervous system controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control. Actuators are grouped into synergies by an unsupervised learning method, and a synergy action is learned to control multiple actuators in synchrony. In this way, we achieve a low-rank control at the synergy level. We extensively evaluate our method on a variety of robot morphologies, and the results show its superior efficiency and generalizability, especially on robots with a large DoF like Humanoids++ and UNIMALs.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Elements of paradoxes in supply chain management literature: A systematic literature review

This study reports the results of a systematic literature review investigating paradoxes in supply chain management. This issue is important because supply chain practitioners frequently face paradoxes in industry with little direction provided in supply chain literature. Investigating the years 1997 through 2019, we identified 64 articles as the basis of our research containing a total of 68 unique paradoxes. In identifying the paradox elements (PEs), we adopted paradox theory (PT) as the base theoretical approach, which was utilized in only 7 of the articles. We employed contingency theory, institutional complexity theory, and complexity theory to support our findings. For each paradox, we also extracted and summarized managerial insights for practitioners. This study addresses the emergent needs of investigating paradoxes in the supply chain management domain to extend the use of PT and complementary theories that can aid practitioners in how to efficiently manage the paradoxes they encounter in industry

Research on the performance and application of low traffic hardened lime-fly ash pavement materials in rural areas

By means of curing agent hardening technology applied for lime-fly ash road surface, the performance of unconfined compressive strength, water stability and freezing stability of hardened fly ash mixture has been tested, and the feasibility of the technology has been verified. At the same time, combined with the indoor test results, the test road has been paved which achieved good results, and the construction technology is simple, economical and reasonable, so it has a good value of popularization and application

Is the US 3PL industry overcoming paradoxes amid the pandemic?

Purpose: Third-party logistics (3PL) companies have experienced an explosion of volume during coronavirus disease 2019 (COVID-19). Special tiers have been introduced to provide differentiated levels of service to the customers. However, such changes in an organization reveal and intensify tensions known as paradoxes. The purpose of this research is to identify what paradoxes emerged or have become more salient specifically due to COVID-19 in 3PLs\u27 ground operations and how they are dealt with by ground operation managers. Design/methodology/approach: This is a qualitative study conducted in two phases. Phase one utilizes a questionnaire approach to identify the paradoxes within the 3PLs operating in the USA. Phase two, conducted six months after phase one, follows an in-depth one-on-one interview approach. NVivo 12 is employed to analyze the interview data. Findings: The results show that new paradoxes did in fact emerge due to the COVID-19 and are mostly related to the performing paradox category. Findings from in-depth interviews show that the 3PL managers focus on keeping safety as priority to manage COVID-19 related paradoxes, along with modifying operational plans, improving communication, investing in training, optimizing hub network, introducing modified/new methods and adapting modified human resource policies. Originality/value: This paper is among the first known to identify paradoxes within the 3PL operations during the COVID-19 and provides insights into how these paradoxes are dealt with at mid-management level. Findings of this study provide foundations for the development of a theoretical framework on handling paradoxes within 3PLs

Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements

Utilizing the nanosecond pulse technique to improve antigen intracellular delivery and presentation to treat tongue squamous cell carcinoma

Tongue squamous cell carcinoma is the most common squamous cell carcinoma of the head and neck. Immunotherapy has great potential in the treatment of tongue squamous cell carcinoma because of its unique advantages. However, the efficacy of immunotherapy is limited by the efficiency of antigen phagocytosis by immune cells. We extracted dendritic cells (DCs) from human peripheral blood. Utilizing a nanosecond pulsed electric field (nsPEF), we deliver the tumour lysate protein into DCs and then incubate the DCs with PBMCs to obtain specific T cells to kill tumour cells. The biosafety of nsPEF was evaluated by the ANNEXIN V-FITC/PI kit. The efficacy of lysate protein delivery was evaluated by flow cytometry. The antitumour efficacy was tested by CCK-8 assay. The nsPEF of the appropriate field strength can significantly improve the phagocytic ability of DCs to tumour lysing proteins and have good biosafety. The tumour cell killing rate of the nsPEF group was higher than the other group (p< 0.05). Utilizing nsPEF to improve the phagocytic and presenting ability of DCs could greatly activate the adaptive immune cells to enhance the immunotherapeutic effect on tongue squamous cell carcinoma