Bird's Eye View Research Articles

ST‐SIGMA: Spatio‐temporal semantics and interaction graph aggregation for multi‐agent perception and trajectory forecasting

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving (AD) system. However, most proposed methods aim at addressing one of the two challenges mentioned above with a single model. To tackle this dilemma, this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting (ST-SIGMA), an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework. ST-SIGMA adopts a trident encoder–decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view (BEV) maps simultaneously. Specifically, an iterative aggregation network is first employed as the scene semantic encoder (SSE) to learn diverse scene information. To preserve dynamic interactions of traffic agents, ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder. Meanwhile, a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed. Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-the-art (SOTA) methods in terms of scene perception and trajectory forecasting, respectively. Therefore, the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in real-world AD scenarios.

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges.

In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.

A bird eye view on cancer comorbidities in rheumatoid arthritis patients: an underestimated incidences and possible preventive treatments

article: A bird eye view on cancer comorbidities in rheumatoid arthritis patients: an underestimated incidences and possible preventive treatments - Minerva Biotechnology and Biomolecular Research 2022 December;34(4):145-52 - Minerva Medica - Journals

Quality of COVID-19 research: An overview of large-scale assessments and a case study on excess mortality estimates

Abstract COVID-19 is an ongoing public health emergency, which affected individual and population health, whereas it disrupted social and economic activities for more than 2 years. High-quality research evidence, which is published without delays in the peer-review and publication pipeline, is the most powerful tool for evidence-based decision-making by physicians, public health and health policy specialists, and politicians. However, from the early stages of the pandemic till now, there is a debate on the quality of COVID-19 research, whereas it has been observed that the decrease in the time from submission to publication was accompanied by a decrease in research quality. This presentation discusses the findings and main conclusions from two research projects. The first part focuses on a systematic review of published large-scale qualitative evaluations of COVID-19 research. By taking advantage of these large-scale assessments, we will provide a bird's eye view on the quality of COVID-19 research done so far. The second part focuses on a case study describing the methodological quality of studies calculating excess mortality estimates during COVID-19 pandemic. Excess mortality estimates depend on important choices about the pre-pandemic reference period, the pandemic period of interest, and the modelling of the comparison between these two periods. Such an assessment has not been done yet although there is an abundance of published approaches to estimate excess mortality during the COVID-19 pandemic. Overall, these two projects highlight the importance of meta-research in the time of pandemic by scrutinizing and assessing scientific evidence while deriving recommendations for improvement of future research. This presentation concludes with a summary of the implications of low-quality COVID-19 research in decision-making and some general considerations to improve research quality and integrity in the case of future pandemics.

A Critical Review On Alambusha ( Biophytum Sensitivum Linn)

Alambusha (Biophytum sensitivum) belonging to the family oxalidaceae, commonly known as Mukkutti, is mostly found in marshy regions of south India, abundantly seen during rainy season. It is known as the “Small Tree Plant” with sensitive leaflets and bright yellow circular small flowers resembles like a tree in its texture and looks. The phytochemistry of B.sensitivum showed a wide range of chemical compounds, such as, two biflavones, three flavonoids and two acids. The plant is described in Ayurvedic classics, by Nighantu Acharyas, as Krimighna herb. The plant, is traditionally believed to exhibit antioxidants, anti-inflammatory and anti-tumor activity, radioprotection, immune-modulation, antibacterial, wound-healing, hypoglycemic actions. B.sensitivum has been used as traditional medicine for several purposes such as expectorant, stimulant, and tonic. It is recommended to be used in the treatment of stomach ache, asthma, treating insomnia, convulsions, cramps, chest complaints, inflammations, wound infections, tumors, and remedying chronic skin diseases. In the present review, all the available sources regarding the plant B. sensitivum was highlighted in a bird eye view

Untying a Gordian knot: Exploring the nomological network of resilience.

This study investigates the relations of resilience with coping, personality traits, emotional intelligence, sense of coherence and maladaptive personality traits. The study employs network analysis techniques to the study of resilience, showcasing how these methods can estimate a model that is simple to interpret while still retaining the most important relations and that can even suggest the direction of causality despite using a cross-sectional design (N=305). The results highlight several important variables that should be considered for fostering resilience, foremost among them the use of positive reappraisal coping, sense of coherence, and the social management aspect of emotional intelligence. The results successfully replicated known associations between resilience and other psychological constructs (emotional intelligence, personality, sense of coherence, coping) and shed light on relations between resilience and maladaptive personality traits. Network analysis considered all these constructs together, so as to take into account the complex pattern of relations between them and offer a bird's eye view of the whole network of associations centred on resilience. The resulting model is parsimonious and easy to interpret while still striving to preserve the complexity of the variables' interrelations.

Perspective Transformer and MobileNets-Based 3D Lane Detection from Single 2D Image

Three-dimensional (3D) lane detection is widely used in image understanding, image analysis, 3D scene reconstruction, and autonomous driving. Recently, various methods for 3D lane detection from single two-dimensional (2D) images have been proposed to address inaccurate lane layouts in scenarios (e.g., uphill, downhill, and bumps). Many previous studies struggled in solving complex cases involving realistic datasets. In addition, these methods have low accuracy and high computational resource requirements. To solve these problems, we put forward a high-quality method to predict 3D lanes from a single 2D image captured by conventional cameras, which is also cost effective. The proposed method comprises the following three stages. First, a MobileNet model that requires low computational resources was employed to generate multiscale front-view features from a single RGB image. Then, a perspective transformer calculated bird’s eye view (BEV) features from the front-view features. Finally, two convolutional neural networks were used for predicting the 2D and 3D coordinates and respective lane types. The results of the high-reliability experiments verified that our method achieves fast convergence and provides high-quality 3D lanes from single 2D images. Moreover, the proposed method requires no exceptional computational resources, thereby reducing its implementation costs.

Comparison of hand hygiene compliance among healthcare workers in Intensive care units and wards of COVID-19: A large scale multicentric study in India

BackgroundHand hygiene is a significant component involved in preventing transmission of health care associated infections including COVID-19. Compliance to hand hygiene among the health care workers (HCWs) requires evaluation and timely feedback. “You can't improve what you can't measure” is a famous saying and this multicentric study was designed to measure hand hygiene compliance and have birds eye view on hand hygiene compliance in COVID Intensive care units (ICUs) and wards across India. MethodsA prospective multicentric observational study was conducted for a period of 6 months in 92 health care facility across India which included varied type of public and private hospitals. Hand hygiene audit was conducted in COVID ICU and COVID non-ICU wards in all these facilities by their HCWs using the IBHAR mobile application based on WHO's hand hygiene audit tool. Hand hygiene total adherence rate (HHTAR) and hand hygiene complete adherence rate (HHCAR) were analyzed and compared between 2 locations. Adherence rates were analyzed based on the zones, institute type, profession and for each WHO moments. ResultsA total of 1,61,056 hand hygiene opportunities were documented and adherence rates were recorded higher in COVID wards (HHTAR-61.4%; HHCAR-28.8%) than COVID ICUs (HHTAR-57.8%; HHCAR-25.6%). Overall, the adherence rates were observed higher in COVID wards (HHTAR- 68.1%; HHCAR-38.3%) of private hospitals, COVID wards of the west zone (HHTAR- 70.2%; HHCAR-36.8%), cleaning staffs of the COVID ward scores better compliance than all other professions in COVID ICUs and COVID wards. HHTAR was found to be the higher in moment 3 (After body fluid exposure-76.3%) followed by moment 4 (after touching patient-73.7%) done in COVID wards compared to moments done in ICUs. ConclusionsThis study highlights the practice of hand hygiene in COVID care locations across India. Effective strategies need to be implemented in COVID ICUs across the facilities to improve the compliance.

Treating a genre as a database: a digital research methodology for studying Chinese local gazetteers

As digital humanities research relies on the digitization of sources, many of its applications are based on access to data on a huge scale that makes quantitative analyses and distant reading (or a birds-eye view) possible. Based on this assumption, we show how the genre of Chinese local gazetteers, with its volume, consistent structures, and broad geographic and temporal range, provides an ideal case to benefit from the digital approach. This paper introduces the Local Gazetteers Research Tools (LoGaRT), a suite of research tools designed for studying Chinese local gazetteers based on the philosophy that any comprehensive genre, such as Chinese local gazetteers, when accompanied with tools that aim to bring a collective lens to the genre, can greatly enrich the ways that scholars approach it and can transform the genre into a research infrastructure that enables new types of research. We report on how LoGaRT opens up new perspectives for researching Chinese history by showing case studies and the scholarly breakthroughs made by our research group. With this paper we hope to provide one possible answer to the question of how digital methods can contribute to humanities research. Even though LoGaRT is developed for a specific Chinese genre, we argue that the proposed research methodology and the corresponding user workflow and tools developed in our software can be applied to other genres or collections of sources when certain criteria are met.

Technology fitness landscape for design innovation: a deep neural embedding approach based on patent data

ABSTRACT Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's-eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.

Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking

3D single object tracking is a key task in 3D computer vision. However, the sparsity of point clouds makes it difficult to compute the similarity and locate the object, posing big challenges to the 3D tracker. Previous works tried to solve the problem and improved the tracking performance in some common scenarios, but they usually failed in some extreme sparse scenarios, such as for tracking objects at long distances or partially occluded. To address the above problems, in this letter, we propose a sparse-to-dense and transformer-based framework for 3D single object tracking. First, we transform the 3D sparse points into 3D pillars and then compress them into 2D bird's eye view (BEV) features to have a dense representation. Then, we propose an attention-based encoder to achieve global similarity computation between template and search branches, which could alleviate the influence of sparsity. Meanwhile, the encoder applies the attention on multi-scale features to compensate for the lack of information caused by the sparsity of point cloud and the single scale of features. Finally, we use set-prediction to track the object through a two-stage decoder which also utilizes attention. Extensive experiments show that our method achieves very promising results on the KITTI and NuScenes datasets.

PORTVM TRAIANI: AN INTERDISCIPLINARY APPROACH TO THE IMAGERY OF TRAJAN'S HARBOUR AT PORTUS

This is a collaborative in-depth study of Trajan's sestertii with a bird's-eye view of the harbour at Portus (RIC 631–2), struck in AD 112–14. It is based on a new numismatic analysis of the coin type, featuring a corpus of 46 specimens and a critical study of their reverse dies, as well as on recent archaeological research at the port that allows for a better understanding of the harbour buildings and their chronology. The article provides the first detailed comparison of the structures shown on the nine reverse dies with those on the ground. The new interdisciplinary assessment of the evidence leads to a new hypothesis regarding the occasion of the coin issue: it probably commemorated the inauguration of the harbour's distinctive hexagonal basin, through which the new port facility became operational.

Sweden, Inc.: Temporal Sovereignty of the Realm and People from the Middle Ages to Modernity

Sweden, Inc.: Temporal Sovereignty of the Realm and People from the Middle Ages to Modernity

S2G2: Semi-Supervised Semantic Bird-Eye-View Grid-Map Generation Using a Monocular Camera for Autonomous Driving

Semantic bird-eye-view (BEV) grid map is a straightforward data representation for semantic environment perception. It can be conveniently integrated with downstream tasks, such as motion planning, trajectory prediction, etc. Most existing methods of semantic BEV grid-map generation adopt supervised learning, which requires extensive hand-labeled ground truth to achieve acceptable results. However, there exist limited datasets with hand-labeled ground truth for semantic BEV grid map generation, which hinders the research progress in this field. Moreover, manually labeling images is tedious and labor-intensive, and it is difficult to manually produce a semantic BEV map given a front-view image. To provide a solution to this problem, we propose a novel semi-supervised network to generate semantic BEV grid maps. Our network is end-to-end, which takes as input an image from a vehicle-mounted front-view monocular camera, and directly outputs the semantic BEV grid map. We evaluate our network on a public dataset. The experimental results demonstrate the superiority of our network over the state-of-the-arts.

Robust Multiobject Tracking Using Mmwave Radar-Camera Sensor Fusion

With the recent hike in the autonomous and automotive industries, sensor-fusion-based perception has garnered significant attention for multiobject classification and tracking applications. Furthering our previous work on sensor-fusion-based multiobject classification, this letter presents a robust tracking framework using a high-level monocular-camera and millimeter wave radar sensor-fusion. The proposed method aims to improve the localization accuracy by leveraging the radar's depth and the camera's cross-range resolutions using decision-level sensor fusion and make the system robust by continuously tracking objects despite single sensor failures using a tri-Kalman filter setup. The camera's intrinsic calibration parameters and the height of the sensor placement are used to estimate a birds-eye view of the scene, which in turn aids in estimating 2-D position of the targets from the camera. The radar and camera measurements in a given frame is associated using the Hungarian algorithm. Finally, a tri-Kalman filter-based framework is used as the tracking approach. The proposed approach offers promising MOTA and MOTP metrics including significantly low missed detection rates that could aid large-scale and small-scale autonomous or robotics applications with safe perception.

Revisiting Social Stigma in Non-suicidal Self-injury: A Narrative Review.

Non-suicidal self-injury (NSSI) is highly prevalent in our community. Yet, there is a significant discrepancy between the number of individuals engaging in NSSI and those who seek treatment for NSSI. This discrepancy can be due to the high social stigma associated with the behavior. The impact of NSSI stigma is significant and can impair the quality of life in the individuals engaging in NSSI, delay help-seeking, reduce access to mental health care and further fuel misinformation. Even though the symptomatology, risks, and demographics of NSSI have received attention in terms of research, there is limited literature on NSSI stigma and its consequences. With that background set, this review provides a bird's-eye view of the different levels of stigma in NSSI (public, self, and health care), associated discrimination, the various aspects of such stigmatization (NSSI-related language, physical scarring, misinformation, the media), and, finally, the collaborative clinical-outreach interventions for mitigating NSSI-associated social stigma. If NSSI is indeed recognized as a clinical disorder, future research would need to focus on these constructs of stigma and treat them with the same importance as the one given to clinical studies of intervention and symptomatology in NSSI.

대학 신입생의 자기주도 활동 프로젝트와 기획서 쓰기의 효과 연구

This study attempted to analyze the process of preparing for the future with more creative and problem-solving ability with writing proposals through university freshman’s self-directed activity and writing activity. Through a new class called Dongduk Arete, students go through the process of self-exploration and develop creative ideas in order to lead them to develop themselves more. Each student tries developing a self-directed program based on ideas that fit their circumstances and tastes and writing them as a proposal. As it has been said that a proposal is 'building', not 'writing', proposal writing is an activity that goes beyond simple writing. As writing a proposal is like building up bricks and writing them together one by one, it is a document for realizing an idea, so it is called a 'blueprint' that is made before construction. Planning is a complex blueprint for putting ideas into practice, a bird's eye view, and a plan for carrying out work. The process of university freshmen leading their life through the process of self-exploration and planning it as a document is a work that demonstrates important values for self-growth in future.

Status of Malaria in the W.H.O. Western Pacific Region

Objective: The objective of this paper is to present a birds-eye view of the Malaria situation in the W.H.O. Western Pacific Region. Methods: The methods included online searches related to Malaria for each country such as from W.H.O., World Bank and journal articles as well as national program documents.Findings: Twenty-eight countries, territories, and areas (American Samoa, Australia, Brunei Darussalam, China, Cook Islands, Fiji, French Polynesia, Guam, Hong Kong SAR, Japan, Kiribati, Macao SAR, Marshall Islands, Federated States of Micronesia, Mongolia, Nauru, New Caledonia, New Zealand, Niue, Northern Mariana Islands, Palau, Pitcairn Islands, Samoa, Singapore, Tokelau, Tonga, Tuvalu, and Wallis and Futuna) are free of indigenous Malaria while the remaining nine (Cambodia, Lao People’s Democratic Republic, Malaysia, Papua New Guinea, Philippines, Republic of Korea, Solomon Islands, Vanuatu and Viet Nam) are progressing towards that target. Conclusion: Cambodia and Malaysia are most likely to receive a certification for Malaria elimination in the immediate future.

Revisiting Praise as Feedback in L2 Writing: Where are we Going?

While a plethora of studies have been conducted on corrective feedback in L2 writing, praise as feedback has received less attention in L2 writing despite its well-acknowledged motivating functions in education and psychology (For economy of expression, the word “praise” will be used consistently throughout this paper to denote “praise as feedback”). To fill this gap, this review article aims to provide a bird's eye view of praise in L2 writing. This paper reports praise givers’ perceptions and practices, praise receivers’ perceptions and the effect of praise from reviewed studies. Specifically, this paper highlights the issue of ill-defined praise in L2 writing, the mismatch between teachers’ positive perceptions and their practices, inconsistent findings regarding the effectiveness of praise as well as motivating functions of praise. Based on these findings, this article argues that more attention should be given to unleashing the potential of praise through teachers’ giving balanced and quality praise and more investigations of praise with longitudinal research design and multiple data sources so as to advance our understandings of feedback in student L2 writing development.

Strategy as Practice Research: Reflections on its Rationale, Approach, and Contributions

AbstractStrategy as practice (SAP) research has developed into an established area of study, and the Journal of Management Studies (JMS) has played an important role in this as a hospitable outlet, including via a foundational special issue in 2003. This article offers a bird's eye view of the domain and a ready source of relevant literature to both seasoned scholars and newcomers to this topic. We make three observations that reflect both the JMS thematic collection and the wider literature on SAP and help to shed light on some of the key tensions facing scholars who seek to develop a distinct body of research. First, in terms of the rationale for SAP research, this body of work has grappled with its framing as building upon, yet distinct from strategy process research, to delve into practices within strategy process. Second, in terms of the approach to research (and community‐building), SAP research has reflected European intellectual roots which has helped to deliver on its broad research purpose but has perhaps also impeded its impact on more mainstream strategy. Third, in terms of the contribution of SAP research, there has been a large volume of high quality work, appearing in well‐regarded journals, generating a considerable diversity of perspectives.